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Spice v2.2.1 (Sep 2, 2026)

Β· 15 min read
Ben Chambers
Member of Technical Staff at Spice AI

Spice v2.2.1 is now available! πŸ› οΈ

Spice v2.2.1 is a patch release that improves accelerated data management, query federation, and search. Spice Cayenne now reclaims the storage that upserts and compaction leave behind, and new metrics report what each maintenance pass did. Retention policies apply to more Cayenne datasets. Federated queries push more work down to BigQuery. DuckDB and HTTP datasets keep their memory bounded. Vector search with HuggingFace embedding models returns more relevant results.

What's New in v2.2.1​

Cayenne Reclaims Storage Automatically​

A Spice Cayenne acceleration now reclaims the space that a write or a compaction supersedes. Each maintenance pass removes four kinds of state that earlier releases kept:

  • tombstones for superseded upsert rows
  • snapshot directories that a later commit replaced
  • manifest and catalog rows for snapshots that compaction merged away
  • deletion vectors that a compaction orphaned

Cayenne also releases the metastore pages that a pass frees. On a table with frequent refreshes or CDC upserts, disk and metastore usage now stay bounded. No configuration change is required.

Retention Policies Apply to More Cayenne Datasets​

Cayenne now applies retention_sql on datasets with refresh_mode: full and on CDC-accelerated datasets with refresh_mode: changes. Before this release, only the append refresh path ran the retention pass. A full refresh reloaded every source row, and the rows that a policy had removed came back.

A retention_sql predicate that calls now() also works now. Cayenne prepared the predicate once when the table opened, at a point where DataFusion cannot evaluate now(), so the pass failed and removed nothing. Cayenne now resolves now() at the start of each pass. See Data Refresh for the retention settings.

datasets:
- from: postgres:events
name: events
acceleration:
engine: cayenne
retention_sql: DELETE FROM events WHERE created_at < now() - INTERVAL '7 days'

Cayenne also accepts refresh_append_overlap now. It warns when a dataset sets retention_sql together with mode: memory, or sets indexes, because it cannot apply either setting.

Cayenne Maintenance and Storage Metrics​

Cayenne now reports its maintenance decisions and its storage footprint as metrics on the cayenne meter. The runtime serves them at /metrics when spiced starts with --metrics <addr>. An operator can now tell a pass that found nothing from a pass that declined. The metrics also show where disk and metastore space goes. See Metrics for the metrics endpoint.

  • cayenne_compaction_outcome_total and cayenne_maintenance_outcome_total count each pass by outcome, such as committed, no_op, or declined_<reason>.
  • cayenne_compaction_trigger_total names the threshold that requested a pass.
  • cayenne_maintenance_reclaimed_files_total, cayenne_maintenance_reclaimed_bytes_total, and cayenne_maintenance_reclaimed_rows_total count what each pass reclaimed.
  • cayenne_storage_files, cayenne_storage_bytes, and cayenne_storage_rows report the live footprint by storage tier.
  • cayenne_data_dir_files, cayenne_data_dir_bytes, and cayenne_data_dir_snapshot_dirs report what the data directory holds on disk.
  • cayenne_metastore_db_bytes, cayenne_metastore_wal_bytes, and cayenne_metastore_table_rows report the size of the metastore.

Query Federation and SQL Improvements​

  • ADBC federation: The ADBC Data Connector again keeps Spice-only functions such as json_get_str out of the SQL it sends to a source, and it honours query_federation: disabled. The v2.2.0 move of the connector into its own crate dropped both settings. A query with a Spice-only function against BigQuery then failed with an unknown-function error.
  • JSON functions in BigQuery: BigQuery federation now translates json_get_int and json_get_float into BigQuery SQL. A filter or a join on a JSON value runs in BigQuery instead of in Spice.
  • Negative JSON numbers: json_get_int and json_get_float now read a negative JSON number. Every earlier release returned NULL for one.
  • Joins between described datasets: An INNER or FULL join between two datasets that both set a description now plans. DataFusion carries the description as schema metadata, and the join failed with a schema mismatch error. This release updates DataFusion with the fix.

Cayenne Query and Delete Fixes​

  • Predicate deletes on upsert tables: A DELETE with a non-primary-key predicate on a Cayenne upsert table now removes only the rows that match. The delete matched the predicate against superseded row versions as well as live rows. A key whose old version matched therefore lost its live row too. The delete now applies the same visibility rules as a query.
  • Date filters on Cayenne data files: A pushed-down CAST(date_col AS TIMESTAMP) comparison failed the scan for a Date32 column. For a Date64 column, the same comparison skipped files that held matching rows, so the query returned fewer rows. The scan cast the file's date statistic through the wrong storage type. It now converts the statistic before it prunes on it.
  • Primary keys after an interrupted index rebuild: A rebuild of the primary-key index that failed or was cancelled left the index disabled for the life of the process. A later upsert could then write a second live row for a declared primary key, and every write rebuilt the whole keyset. Cayenne now closes the rebuild window on every exit path.
  • Row counts after a commit: A distributed COUNT(*) that Cayenne answered from its maintained row count could miss the rows of a commit that had just become visible. Cayenne now claims a commit's row-count delta before it publishes the rows.
  • Point lookups on CDC datasets: A primary-key lookup with an ORDER BY on the same key failed to plan on a CDC-accelerated Cayenne dataset after its first transactional commit. An example is WHERE id = 7 ORDER BY id. The runtime rejected the query and returned an error. The scan now forwards its constant columns across the schema cast, and the query plans again.
  • Arrow Map columns in file mode: A Cayenne accelerator with mode: file now stores Arrow Map columns, such as the response_headers column of an HTTP dataset. Every write of such a column failed, and the acceleration stayed empty.

DuckDB and HTTP Datasets Keep Memory Bounded​

DuckDB: The bundled DuckDB moves from v1.5.5 to v1.4.4. On v1.5.5, a DuckDB-accelerated dataset that refreshed through an ON CONFLICT ... DO UPDATE upsert grew its memory by about 6 MiB per refresh. The row count stayed steady while the memory grew, and the write eventually failed with an out-of-memory error. The DuckDB project tracks the regression as duckdb/duckdb#25162, and it is present in every v1.5.x release. v1.4.4 holds a steady footprint. Queries that run during a file-mode DuckDB swap also now recover when the swap invalidates their statements. The configured database path stays attached across the swap.

HTTP: The HTTP Data Connector now bounds its response cache. The cache kept every fetched response for the life of the process. An API-proxy workload with request-shaped keys therefore grew by about 1 MB per distinct request.

The cache now holds at most response_cache_max_size_bytes per dataset, 64 MiB by default, and evicts the oldest entries first. It expires an entry by the origin's max-age and Age headers, and it refuses to store a response that sends no-store or no-cache. The new response_cache_fallback_ttl setting keeps a response from an origin that sends no Cache-Control header at all. Two new metrics, response_cache_size_bytes and response_cache_items_count, report occupancy per dataset.

datasets:
- from: https://api.example.com/v1/items
name: items
params:
response_cache_max_size_bytes: 16777216
response_cache_fallback_ttl: 5m

More Relevant Vector Search with HuggingFace Embeddings​

Vector search with a HuggingFace sentence-transformer embedding model, such as all-MiniLM-L6-v2, now returns relevant results. The runtime kept the fixed padding that some models declare in tokenizer.json, so it padded every input to 128 tokens. The padding then dominated the vector of a short input. The runtime now clears that padding and lets the embedding layer own the attention mask. On the MTEB SciFact benchmark with all-MiniLM-L6-v2, nDCG@10 rises from 0.018 to 0.640 (published: 0.645). Full-text search and model2vec static models were not affected.

A search filter on a timezone-aware timestamp column now matches the exact instant when the runtime pushes it down to a DuckDB-backed search index. The filter compared whole milliseconds, so a filter inside a millisecond returned different rows than requested.

Change Data Capture and Write-Back​

  • PostgreSQL write-back echo suppression: A CDC-accelerated dataset that writes back to its PostgreSQL source now applies each write once. The logical-replication stream returns every write-back transaction as a change. Spice now records the transaction ID of each delivery and drops the matching changes from the stream. Spice drops a replay after a slot reconnect the same way.
  • Write-back for BIGINT primary keys: Durable write-back now delivers for a table whose primary key is a single BIGINT column. Such a table acknowledged writes but never delivered them to the source.
  • MySQL rebuild after a purged binlog: A MySQL acceleration with mysql_replication_invalid_checkpoint_behavior: restart now rebuilds when the source has purged its binlog position, even when mysql_replication_initial_snapshot: disabled is set. It previously resumed from the current head and kept the rows that the source had deleted in the gap.
  • Append refresh on Cayenne: An append refresh on a Cayenne dataset resumes correctly again. The runtime read the append watermark through a timestamp cast that the Cayenne scan could not evaluate. The read failed, and the dataset stopped appending. The runtime now reads the raw column and normalizes the value in memory.

Other Improvements​

  • Startup logs: The log events that the runtime emits while it builds now reach the configured log output. These include the DuckDB memory budget and the query memory-limit defaults. Earlier releases dropped them at every log level.
  • Spice Cloud CLI: The CLI now reconciles its stored credential state and active organization for Spice Cloud after login.
  • Cayenne write concurrency: A Cayenne write that requires a single writer now runs serially even when cayenne_write_concurrency is raised.

Contributors​

Breaking Changes​

No breaking changes.

One behavior change to note before you upgrade:

  • DuckDB moves from v1.5.5 to v1.4.4 to keep the memory of DuckDB-accelerated datasets bounded under upsert refreshes (duckdb/duckdb#25162). SQL syntax and functions that DuckDB added in v1.5 are not available to DuckDB-accelerated datasets.

Cookbook Updates​

No new cookbook recipes.

The Spice Cookbook includes more than 104 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v2.2.1, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:2.2.1 image:

docker pull spiceai/spiceai:2.2.1

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai --version 2.2.1

AWS Marketplace:

Spice is available in the AWS Marketplace.

What's Changed​

Changelog​

  • Make a pinned single partition actually mean one writer by @lukekim in #13325
  • feat(postgres): outstanding write-back xid registry for CDC echo suppression by @Jeadie in #13350
  • fix(llms): clear tokenizer padding/truncation so TEI owns masking by @Jeadie in #13416
  • fix(http): bound the connector's response cache and honour no-store (fixes #13460) by @bjchambers in #13462
  • fix(search): render a DuckDB search filter against the table schema (fixes #13144) by @claudespice in #13489
  • fix(duckdb): bump datafusion-table-providers for the attachment-race fixes by @sgrebnov in #13491
  • fix(cli): reconcile Spice Cloud credential state by @phillipleblanc in #13500
  • fix(mysql): rebuild a purged-position acceleration whatever the snapshot mode says (fixes #13024) by @claudespice in #13501
  • fix(cayenne): decode write-back markers for a single Int64 primary key by @phillipleblanc in #13502
  • fix(deps): bump DataFusion to pick up the join schema-metadata fix by @krinart in #13526
  • fix(cayenne): give each cayenne_catalog test a table root of its own (fixes #13527) by @claudespice in #13535
  • fix(acceleration): read the append watermark without casting the time column by @sgrebnov in #13539
  • test(postgres): cover CDC echo suppression through a real delivery transaction by @phillipleblanc in #13553
  • test(federation): guard the derived-table output names a pin bump landed unguarded (refs #12751) by @claudespice in #13555
  • fix(runtime): carry a scan's constant columns across the schema cast by @phillipleblanc in #13565
  • fix(cayenne): store Arrow Map columns in a file-mode accelerator (fixes #13524) by @bjchambers in #13567
  • fix(cayenne): stop a predicate DELETE destroying live rows on an upsert table by @lukekim in #13574
  • fix(logging): deliver the runtime build's events to the installed subscriber by @sgrebnov in #13587
  • fix(adbc): restore the federation deny-list and query_federation wiring by @peasee in #13590
  • feat(cayenne): observability for maintenance decisions and footprint growth by @bjchambers in #13619
  • test(forks): guard the Spice patches carried on our upstream forks by @bjchambers in #13623
  • test(cayenne): settle the tier before asserting why a subset pass declined by @lukekim in #13632
  • fix(cayenne): close the PK-index checkout window on every exit path (fixes #13267) by @claudespice in #13641
  • fix(cayenne): reclaim inline tombstones, and return the freed metastore pages by @lukekim in #13651
  • fix(cayenne): delete merged-away manifest rows at protected-snapshot compaction by @Jeadie in #13658
  • fix(cayenne): prune old-snapshot catalog rows at subset small-file compaction by @Jeadie in #13659
  • fix(cayenne): delete emptied protected snapshot manifest rows in retention cleanup by @Jeadie in #13660
  • fix(cayenne): schedule the orphaned deletion-vector sweep from every publication that orphans one by @phillipleblanc in #13663
  • fix(cayenne): reclaim superseded snapshot dirs against live state by @peasee in #13670
  • Translate the JSON extraction functions into BigQuery SQL, and read negative JSON numbers by @phillipleblanc in #13672
  • test(forks): guard the four BigQuery dialect unparser fixes the datafusion pin carries by @lukekim in #13691
  • fix(cayenne): claim a commit's live-row delta before it publishes the rows (fixes #13036) by @claudespice in #13720
  • Downgrade DuckDB to v1.4.4 by @sgrebnov in #13742
  • fix(vortex): convert a date statistic into a timestamp before pruning on it (fixes #13624) by @lukekim in #13745
  • fix(cayenne): resolve now() in retention predicates once per pass by @lukekim in #13769

**Full Changelog: https://github.com/spiceai/spiceai/compare/v2.2.0...v2.2.1

Spice v2.1.4 (Aug 5, 2026)

Β· 2 min read
Sergei Grebnov
Member of Technical Staff at Spice AI

Spice v2.1.4 is now available! πŸ› οΈ

Spice v2.1.4 is a patch release that improves DuckDB acceleration and runtime stability: datasets with UTC timestamp columns, such as Iceberg tables, now load successfully when accelerated with DuckDB, retention policies run reliably, and the runtime is more resilient under heavy query load.

What's New in v2.1.4​

DuckDB Acceleration Works with Iceberg Timestamps​

A dataset with a UTC timestamp column β€” for example, any Iceberg timestamptz column β€” could previously fail to load when accelerated with DuckDB, leaving the dataset unhealthy and unqueryable. These datasets now load and become ready normally, with no configuration changes needed.

Reliable Retention Policies on DuckDB​

Retention policies now apply cleanly on DuckDB-accelerated datasets, including policies that combine a time window with an additional condition. Expired rows are evicted on every retention interval, keeping accelerated data fresh and storage bounded.

Improved Runtime Stability​

The runtime is now more robust when queries are cancelled β€” whether by a client disconnecting, a timeout, or a new refresh superseding an in-flight read. A rare crash in this path has been eliminated.

Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

No new cookbook recipes.

The Spice Cookbook includes more than 100 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v2.1.4, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:2.1.4 image:

docker pull spiceai/spiceai:2.1.4

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai --version 2.1.4

AWS Marketplace:

Spice is available in the AWS Marketplace.

What's Changed​

Changelog​

  • chore(deps): bump datafusion and table-providers for the DuckDB timezone and retention fixes (2.1 backport) by @phillipleblanc in #12546
  • fix(deps): bump the Vortex pin to pick up the task-cancellation fix by @phillipleblanc in #12547

*Full Changelog: https://github.com/spiceai/spiceai/compare/v2.1.3...v2.1.4

Spice v2.1.2 (Jul 28, 2026)

Β· 3 min read
Sergei Grebnov
Member of Technical Staff at Spice AI

Spice v2.1.2 is now available! πŸ› οΈ

Spice v2.1.2 is a patch release focused on improving the DuckDB data accelerator. It upgrades the DuckDB engine to v1.5.5 and introduces the on_full_refresh parameter, giving file-mode DuckDB accelerations compact, predictable disk usage across repeated full refreshes.

What's New in v2.1.2​

Bounded DuckDB Acceleration File Growth with on_full_refresh​

File-mode DuckDB accelerations using refresh_mode: full now reclaim disk space on every refresh, keeping the database file compact and disk usage predictable for long-running deployments. Each full refresh bulk-loads a fresh copy of the data, and the new on_full_refresh modes ensure the space held by prior copies is returned rather than accumulating in the file.

The new on_full_refresh acceleration parameter controls how disk space is reclaimed after each full refresh:

acceleration:
engine: duckdb
mode: file
refresh_mode: full
params:
duckdb_file: /data/shared.duckdb
on_full_refresh: replace_file # default: reuse_file
  • reuse_file (default): Existing behavior β€” refresh into the existing database file.
  • replace_file: Each full refresh streams data into a fresh staging database file, carries over every other object sharing the file (other datasets' tables, views, indexes, and Spice metadata), checkpoints it, and atomically replaces the configured file. Queries are never interrupted β€” in-flight queries drain against the old file while new queries see the new file β€” and space is fully reclaimed on every refresh.
  • checkpoint_file: After each refresh, run a CHECKPOINT in place, escalating to FORCE CHECKPOINT when concurrent transactions block the plain attempt (waiting for in-flight transactions; never aborting them).

DuckDB 1.5.5​

The DuckDB engine is upgraded from v1.5.3 to v1.5.5, bringing the latest upstream stability fixes.

Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

No new cookbook recipes.

The Spice Cookbook includes more than 100 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v2.1.2, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:2.1.2 image:

docker pull spiceai/spiceai:2.1.2

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai --version 2.1.2

AWS Marketplace:

Spice is available in the AWS Marketplace.

What's Changed​

Changelog​

  • feat(duckdb): on_full_refresh: replace_file β€” full refresh into a new database file, atomically replaced by @lukekim in #12135
  • feat(duckdb): 'on_full_refresh: checkpoint_file' to bound acceleration file growth by @sgrebnov in #12139

*Full Changelog: https://github.com/spiceai/spiceai/compare/v2.1.1...v2.1.2

Spice v2.1.0 (Jul 9, 2026)

Β· 36 min read
Jack Eadie
Member of Technical Staff at Spice AI

Spice v2.1.0 is now available! πŸŽ‰

Spice v2.1.0 is the next minor release of Spice, headlined by high-throughput Cayenne CDC, scaling and resilience improvements to PostgreSQL logical replication, expanded distributed query with Iceberg catalog scans and broadcast joins, and the upgrade to DataFusion v54 (including v53), Arrow v58.3, and Vortex v0.74. The release also adds experimental adaptive self-tuning for the Cayenne accelerator, distributed GLM inference, and a range of security, search, and connector improvements.

Highlights in v2.1.0 include:

  • High-Throughput Cayenne CDC β€” in-memory CDC tier, dedicated compaction runtime, and write-path optimizations that cut replication lag on high-volume CDC workloads
  • PostgreSQL Replication at Scale β€” multiple changes-mode datasets share a single replication slot, unchanged-TOAST recovery, and resilient reconnects across rolling deploys
  • Distributed Query β€” distributed Ballista scans of Iceberg catalog tables, broadcast joins for small dimension tables, and shared scheduler job state with failover
  • DataFusion v54 β€” upgrade to DataFusion v54 (folding in v53), Arrow v58.3, and Vortex v0.74, bringing faster joins, scans, and planning
  • Adaptive Self-Tuning (Experimental) β€” opt-in closed-loop tuning and maintained aggregates that adapt Cayenne to hardware, schema, and live workload

What's New in v2.1.0​

High-Throughput Cayenne CDC​

A major focus of v2.1 is Spice Cayenne write-path throughput for change-data-capture (HTAP) workloads:

  • In-Memory CDC Tier: A new in-memory CDC tier and follow-ups cut replication lag on hot upsert tables, with bounded mem-tier checkpointing and O(1) per-scan deletion views, plus a two-phase off-fence checkpoint on the ingest path.
  • Dedicated Compaction Runtime: A dedicated compaction runtime with CDC pipelining and protected snapshots isolates compaction from query and ingest paths, with parallelized deletion-vector writes, per-batch directory-barrier coalescing, and size-aware parallel encode for protected-snapshot compaction.
  • Incremental Protected Snapshot Compaction: Incremental compaction of protected snapshots (used in Cayenne's merge-on-read deletion index) reduces disk usage and improves query performance.
  • Smaller WAL & Metadata-Only Publish: cayenne_insert_record table IDs are stored as 16-byte raw-UUID BLOBs, cutting CDC WAL volume ~34%; upsert commits publish metadata-only, dropping per-key insert records; transient staged CDC deltas are light-encoded.
  • Delta-Write Encoding Levels: A new cayenne_delta_encoding setting (default auto) selects delta-write encoding, and cayenne_compression_strategy: zstd is now fully wired.
  • In-Memory CDC Sharding: PK-hash intra-apply sharding parallelizes in-memory CDC apply.
  • Scan Safety Under Write: In-flight scans are ref-counted so snapshot GC can't delete Vortex files mid-read; in-RAM scan parallelism, query admission control, and sound scan output ordering improve read behavior under sustained CDC.

Delta-write encoding effort and Vortex compression are tunable per accelerator. cayenne_delta_encoding: auto (the default) size-gates fresh CDC/append writes β€” small deltas use a light scheme and are re-encoded during compaction β€” or pin an explicit level 0..10 (7 is the full default cascade); cayenne_compression_strategy selects the Vortex compression:

acceleration:
engine: cayenne
refresh_mode: changes
params:
cayenne_delta_encoding: auto # 'auto' (default), or pin a level 0..10 (7 = full cascade)
cayenne_compression_strategy: zstd # 'btrblocks' (default) or 'zstd'

Change Data Capture & HTAP​

PostgreSQL logical replication (CDC, refresh_mode: changes, introduced in v2.0) gets significant scaling and resilience work in v2.1:

  • Shared Replication Slot: Multiple refresh_mode: changes PostgreSQL datasets on the same connection can name the same pg_replication_slot to share a single replication slot, walsender decoder, and publication, with decoded changes multiplexed by (schema, table) to each dataset. This collapses the slot count from one-per-dataset to one β€” staying well under Postgres's default max_replication_slots = 10.
datasets:
- from: postgres:public.orders
name: orders
params:
pg_db: mydb
pg_replication_slot: spice_cdc # shared slot name
acceleration:
refresh_mode: changes
- from: postgres:public.customers
name: customers
params:
pg_db: mydb
pg_replication_slot: spice_cdc # same name -> one slot, walsender & publication
acceleration:
refresh_mode: changes
  • Unchanged-TOAST Recovery: Under REPLICA IDENTITY FULL, when an UPDATE leaves a large TOASTed column unchanged, pgoutput sends an "unchanged" marker; Spice now fills that value from the old tuple β€” its old value is its current value β€” so updates no longer error or drop columns. Without an old tuple, the error persists with a hint to enable REPLICA IDENTITY FULL.
  • Transient Walsender Contention: Slot-contention errors during rolling deploys β€” SQLSTATE 55006 ("replication slot is active for PID") and 53300 ("requested standby connections exceeds max_wal_senders") β€” are now classified as transient and retried with backoff instead of fatally terminating the stream. Replication connections are also released at shutdown start (not process exit), freeing walsender seats for replacement instances.
  • Strict CDC Param Validation: PostgreSQL CDC parameters are strictly validated rather than silently defaulted.
  • Debezium Schema Evolution: Fixes for Debezium schema-evolution support, including tombstone-message handling and sign-extension of minimal-width base64 decimals.

Distributed Query​

Distributed Query gains:

  • Distributed Iceberg Catalog Scans: Ballista distributes scans of Iceberg catalog tables across executors.
  • Broadcast Joins: Small dimension tables are broadcast to executors for distributed joins.
  • Shared Scheduler Job State with Failover: Ballista job state is shared so the scheduler can fail over without losing in-flight work.

Performance & Query Engine​

Apache DataFusion is upgraded to v54, folding in v53, alongside Arrow v58.3 and Vortex v0.74 (with a pin bump adding intra-file decode split and a per-execution kernel cache). Two DataFusion releases land in this upgrade:

  • DataFusion v54 (release notes): adds LATERAL joins, SQL lambda functions (x -> expr with array_transform/array_filter/array_any_match), spilling nested-loop joins, and a faster arrow-avro reader. Performance work includes morsel-driven Parquet scans (up to ~2x faster for skewed scans), 20-50x faster sort-merge semi/anti/mark joins, redundant-sort-key pruning, NDV-based cardinality estimation, and inner_product/cosine_distance functions.
  • DataFusion v53 (release notes): adds LIMIT-aware Parquet row-group pruning, broader filter pushdown through joins and UNION, nested-field pushdown (get_field into the scan), faster query planning (some plans dropping from ~4-5ms to ~100us), and 42 faster built-in functions.

Federation deny-list enforcement and catalog DDL are restored after the DataFusion upgrades, and a cost-based left-deep join reordering rule is added for Cayenne acceleration.

AI & LLM​

  • Native GLM Support with Distributed Inference: Native GLM model support with surfaced reasoning_content, including tensor-parallel GLM inference. Load a GLM model with model_type: glm4 (glm4moe and glm4moelite are also supported):
models:
- name: glm
from: huggingface:huggingface.co/THUDM/glm-4-9b-chat
params:
model_type: glm4

For large models, GLM inference can be distributed across nodes (tensor parallelism) via the mistral.rs pure-TCP ring all-reduce backend β€” no NCCL/system dependency. This is a Spice.ai Enterprise feature requiring the distributed build. Run the same model on each node, changing only node_rank:

models:
- name: glm
from: huggingface:huggingface.co/THUDM/glm-4-9b-chat
params:
model_type: glm4
distributed_backend: ring
nodes: 10.0.4.21,10.0.4.22 # ordered host/IP per rank; the ring backend currently requires exactly 2
node_rank: 0 # rank of THIS node in [0, world_size); rank 0 serves the API. Set node_rank: 1 on 10.0.4.22
  • NSQL Context Endpoint: A new GET /v1/nsql/context endpoint returns the SQL dialect, dataset schemas (with optional sample rows), and registered functions that Spice injects into natural-language-to-SQL (POST /v1/nsql) requests β€” useful for inspecting or caching exactly what the model sees:
# Inspect the context injected into /v1/nsql requests (examples_limit default 3, max 100)
curl "http://localhost:8090/v1/nsql/context?include_examples=true&examples_limit=3"

Returns the dialect, per-dataset schema (keys, indexes, searchable columns), the registered function inventory, and sample rows (abbreviated):

{
"context": "# Spice.ai NSQL Context",
"instructions": [
"Write SQL for the Spice runtime, which uses Apache DataFusion with the SQL parser configured for the PostgreSQL dialect.",
"Use table and column descriptions, primary keys, foreign keys, unique constraints, and indexes when choosing joins and filters."
],
"sql": {
"engine": "Apache DataFusion",
"version": "54.0.0",
"dialect": "PostgreSQL",
"parser": "DataFusion SQL parser configured with PostgreSQL dialect"
},
"datasets": [
{
"name": "sales.orders",
"table": "orders",
"description": "Customer orders",
"columns": [
{ "name": "order_id", "data_type": "Int64", "nullable": false, "primary_key": true, "indexed": true },
{ "name": "customer_id", "data_type": "Utf8", "nullable": false, "vector_search": true, "full_text_search": true }
],
"primary_key": ["order_id"],
"foreign_keys": [
{ "columns": ["customer_id"], "foreign_table": "spice.sales.customers", "foreign_columns": ["id"] }
]
}
],
"functions": {
"summary": "Spice SQL runs on Apache DataFusion ... Run SELECT * FROM list_udfs() to inspect the full registered function inventory",
"search": [
{ "name": "vector_search", "syntax": "vector_search(dataset, 'query text'[, column])" },
{ "name": "text_search", "syntax": "text_search(dataset, 'query text'[, column])" }
]
},
"samples": [
{ "title": "Example rows for `sales.orders`", "content": "| order_id | customer_id |\n| --- | --- |\n| 42 | CUST-1 |" }
]
}

Search & Vectors​

  • S3 Vectors Pagination: QueryVectors paginates for top-K up to 10,000.
  • Elasticsearch kNN Candidate Pool: The default kNN candidate pool is raised from 10 to 1000 for better recall.

SQL & Query Engine​

  • FlightSQL Substrait Plans: CommandStatementSubstraitPlan support.
  • Large Result Streaming: Flight streaming is optimized for large result sets.
  • Write Authorization: The SQL tool allows writes for ReadWrite API keys.
  • Schema Evolution Policies: on_schema_change supports widening-only evolution and a drop_and_recreate policy.

Security & Connectors​

  • Kafka mTLS: Mutual TLS configuration is surfaced in the Kafka data connector.
  • Secret Resolution at Startup: Secret references are checked and reported at startup.
  • DuckDB HNSW: Upgrade to DuckDB v1.5.3 with the statically linked VSS (HNSW) vector extension.

Adaptive Self-Tuning (Experimental)​

The Spice Cayenne accelerator gains experimental opt-in self-tuning. cayenne_tuning: auto derives configuration from the detected hardware and inferred schema, while adaptive additionally runs a per-table closed-feedback controller that adapts flush caps, the in-memory CDC tier, compaction cadence, and write concurrency toward operator SLOs (replication lag, freshness, query latency, queries-per-hour). Cayenne can also maintain aggregates incrementally β€” with predicate-aware delta serving and incremental retraction β€” and fold whole-table SUM/AVG/COUNT/MIN/MAX from statistics. These features are experimental and disabled by default.

datasets:
- from: postgres:public.orders
name: orders
acceleration:
engine: cayenne
refresh_mode: changes
params:
cayenne_tuning: adaptive # 'auto' (static, env- + schema-derived) or 'adaptive' (closed-loop)

Observability​

  • Per-Dataset Query Attribution: The query_executions metric gains a datasets dimension.
  • HTAP Diagnostics: Improved HTAP replication diagnostics on non-convergence.
  • Cayenne Write Observability: Write-phase observability for the in-memory CDC tier.

Notable Bug Fixes​

  • Cayenne Utf8View: The Utf8View read schema avoids a hash-join offset overflow.
  • Cayenne metastore: cayenne_metastore: turso is honored for partitioned tables and the dataset checkpoint.
  • Dual-write detection: Dual-write accelerated tables are detected behind the metadata-enrichment wrapper.
  • digest_many collisions: Values are length-prefixed so column boundaries can't collide.
  • Turso WAL checkpoint: WAL checkpoints route through the native Turso connection.
  • TLS status probe: The status check probes the metrics endpoint over HTTPS when TLS is enabled.
  • Search snippet offsets: Character chunk offsets persist so search snippets aren't shifted or garbled.
  • Async query chunk offsets: /v1/queries chunk row_offset uses the cumulative offset rather than chunk_index * chunk_size.

Dependency Updates​

Dependency / ComponentVersion
DataFusionv54
Arrow (arrow-rs)v58.3
Vortexv0.74
iceberg-rustv0.9.1
DuckDBv1.5.3
Rust toolchainv1.95.0

Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

No new cookbook recipes.

The Spice Cookbook includes more than 100 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v2.1.0, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:2.1.0 image:

docker pull spiceai/spiceai:2.1.0

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai --version 2.1.0

AWS Marketplace:

Spice is available in the AWS Marketplace.

What's Changed​

Changelog​

  • Make DuckDB schema cast logic more robust by @sgrebnov in #10991
  • perf(cayenne): reduce allocation overheads in hot paths by @lukekim in #10950
  • improve error message for 'params.spiceai_region' by @Jeadie in #10954
  • fix(acceleration): rename WriteMode variants to fix #10960 by @phillipleblanc in #10974
  • add Scylla, bigquery and turso to throughput benchmarking by @Jeadie in #11006
  • deps(ballista): pull in shuffle-on-object-store correctness fixes (datafusion-ballista PRs #42 + #43) by @phillipleblanc in #10919
  • fix(kafka): seek to sidecar offsets via post_rebalance callback on restart by @ewgenius in #11007
  • Propagate source comments into schema metadata by @lukekim in #10944
  • feat(chbench-driver): better alignment to BenchBase by @sgrebnov in #11012
  • fix: handle EXISTS/NOT EXISTS subqueries in federation analyzer by @sgrebnov in #10996
  • Enable filter pushdown in spicepod defined UDTFs by @Jeadie in #11004
  • Refactor spice dataset configuration command by @Jeadie in #10999
  • fix: ensure HashJoinExec partition counts match when join input is statically empty by @phillipleblanc in #11025
  • feat(chbench): Improve OLTP throughput and reduce PostgreSQL CDC overhead by @sgrebnov in #11018
  • feat(cluster): add distributed query observability metrics by @phillipleblanc in #10990
  • fix: delegate truncate in PolyTableProvider to inner write provider by @claudespice in #11036
  • Remove default runtime features - enable explicitly in spiced by @phillipleblanc in #11037
  • fix: preserve field and schema metadata in Vortex physical schema calculation by @claudespice in #11013
  • Expose metadata descriptions via PostgreSQL UDFs by @lukekim in #11032
  • fix: route Turso WAL checkpoint through native turso connection (fixes #10657) by @claudespice in #11048
  • fix: add missing truncate delegation and guards for wrapper TableProviders by @claudespice in #11014
  • Update DataConnector statuses by @krinart in #11052
  • remove redundant readonly checks by @Jeadie in #10975
  • Fix Unity Catalog connector compatibility with OSS Unity Catalog by @ewgenius in #11026
  • refactor(cdc): reduce CDC sub-batch splits for interleaved upsert/delete workloads by @sgrebnov in #11051
  • feat(cayenne): allow inline writes with pending deletions (deletes/upserts) by @sgrebnov in #11031
  • fix(sql-tool): defer read-only gate to caller's API key role by @phillipleblanc in #11040
  • fix: map Gemini Recitation finish reason to ContentFilter by @claudespice in #11046
  • feat(cayenne): fast-path CDC deletes by extracting PK values from filters by @sgrebnov in #11049
  • fix(cluster): gate scheduler readiness on executor partition loads by @phillipleblanc in #10992
  • fix(snowflake): enforce function deny-list in federation pushdown by @claudespice in #11057
  • perf(cdc): Last-write-wins dedup in group_into_sub_batches to reduce sub-batch splits by @sgrebnov in #11059
  • [Bug] Timing between reconnect and AllocateInitialPartitions leaves connection without flight_sql_client by @Jeadie in #10805
  • Define 'trait QueryEngine' to refactor runtime crate by @Jeadie in #11028
  • fix(snowflake): apply Spice function deny-list in extracted connector crate by @claudespice in #11071
  • perf(cayenne): keep CDC upsert PK keysets resident to avoid per-batch full-table rebuilds by @lukekim in #11074
  • fix(postgres-replication): emit recovery log + reduce reconnect-warn volume by @claudespice in #11084
  • fix metadata on search indexing by @Jeadie in #11080
  • perf(cayenne): scale CDC inline flush caps with memory + storage class by @lukekim in #11087
  • feat(cayenne): merge-on-read position deletes for PK upsert tables + memory-pool accounting by @lukekim in #11085
  • Support tuple-IN composite PK extraction in Cayenne delete fast-path by @sgrebnov in #11093
  • feat(cluster): report per-executor table statistics so distributed JoinSelection can size joins by @phillipleblanc in #11089
  • feat(cluster): NDV-aware executor stats so CDC q18 join swap fires by @phillipleblanc in #11098
  • Improve HTAP replication diagnostics on non-convergence by @sgrebnov in #11100
  • Normalize DataType::Null to Int32 in acceleration schema for duckdb by @krinart in #11062
  • Fix debezium schema evolution support by @ewgenius in #11095
  • feat(cayenne): incremental write-path executor statistics for distributed join sizing by @phillipleblanc in #11104
  • fix(cache): periodic moka maintenance to drain invalidation predicates (#11077) by @phillipleblanc in #11106
  • fix: validate Snowflake account identifiers and auth config by @Jeadie in #11024
  • fix: trace external mcp server tool calls by @ewgenius in #11058
  • Remove unwrap from test code; drop clippy::unwrap_used suppressions by @phillipleblanc in #11108
  • Upgrade to DuckDB 1.5.3 + statically link the VSS (HNSW) extension by @sgrebnov in #11107
  • Fix fetched_at for HTTP connector by @Jeadie in #11116
  • fix(search): propagate LIMIT to base TableScan in VectorScanTableProvider (fixes #8368) by @claudespice in #11124
  • feat(runtime): add spicebench feature to register the Cayenne catalog connector by @phillipleblanc in #11122
  • fix(cayenne): tombstone inline-checkpointed rows on upsert to prevent duplicate PKs by @sgrebnov in #11129
  • Remove possibility of a deadlock in RuntimeStatus by @krinart in #11114
  • Fix Windows build: vendored-vss duckdb-rs + adapt to table-providers mongodb API by @phillipleblanc in #11140
  • localpod: synchronize child refreshes when parent uses in-memory (arrow) accelerator by @phillipleblanc in #11139
  • Add datasets dimension to the query_executions metric by @phillipleblanc in #11138
  • fix(spiceai): keep correlated subqueries out of JOIN ON for Spice Cloud federation by @phillipleblanc in #11143
  • fix(duckdb): normalize timestamp columns to microsecond precision (fixes #10627) by @claudespice in #11145
  • feat(cayenne): sharded parallel Vortex encode with key/time clustering by @lukekim in #11144
  • fix(cluster): prevent DoPut write pipeline self-deadlock under ingest backpressure by @phillipleblanc in #11160
  • feat(chbench): configurable HTAP concurrency, DuckDB query overrides, and OLTP rate control by @sgrebnov in #11162
  • fix(http): preserve non-JSON response rows instead of crashing nested decomposition (fixes #11155) by @claudespice in #11161
  • Use declared schema in DynamoDB/MongoDB/Debezium by @krinart in #11066
  • fix(cluster): prevent partitioned datasets from staying Refreshing by @phillipleblanc in #11157
  • fix(runtime): don't list postgres as a valid accelerator engine when postgres-accel is disabled by @sgrebnov in #11169
  • fix(spark): recover stale or broken Spark Connect sessions on failure by @lukekim in #11171
  • fix(secrets): don't abort secret lookup precedence walk on a failing store by @phillipleblanc in #11175
  • feat(cayenne): bound aggregate write concurrency, conservative defaults, and write/read observability by @lukekim in #11170
  • feat(unity_catalog): support Unity Catalog credential vending for Delta Lake tables by @phillipleblanc in #11180
  • fix(secrets): keep failed secret stores registered so lookups report the init root cause by @phillipleblanc in #11181
  • fix(debezium): sign-extend minimal-width base64 decimals instead of zero-padding by @claudespice in #11184
  • fix(deps): update hickory-resolver to 0.26 (evicts hickory-proto 0.25.x) by @phillipleblanc in #11183
  • refactor(secrets): derive secret store metadata from a single registry table by @phillipleblanc in #11188
  • perf(cayenne): cut CDC replication lag on hot upsert tables by @lukekim in #11191
  • feat(cayenne): async inline-fallback (per-tombstone published flag) + 64c/256GB tuning by @lukekim in #11194
  • Add HTTP connector mTLS support by @lukekim in #11127
  • feat(snowflake): push AT TIME ZONE as CONVERT_TIMEZONE and pin session to UTC by @lukekim in #11190
  • feat(cdc): make cdc_max_coalesce_age_ms a real apply-loop linger by @sgrebnov in #11196
  • feat(cayenne): delta-write encoding levels (cayenne_delta_encoding, default auto) + make compression_strategy=zstd real by @lukekim in #11199
  • Add NSQL context endpoint by @lukekim in #11075
  • fix(federation): respect the Spice function deny-list across all SQL connectors; dialect-aware DuckDB pushdown by @claudespice in #11186
  • fix: surface unknown/applied cayenne_* runtime.params at startup (fixes #10970) by @claudespice in #11133
  • perf(cayenne): plain-fsync ordering tier on the staged-commit hot path by @lukekim in #11198
  • fix(kafka): decode JSON payloads to Arrow directly β€” fixes lossy Decimal128 + removes double-parse (#11192) by @claudespice in #11207
  • feat(cayenne): self-tuning accelerator β€” hardware + schema + closed-loop adaptive (auto/adaptive modes) by @lukekim in #11213
  • perf(cayenne): CDC throughput β€” SF-100 @10K txn/s toward <5s lag + 5K QPH by @lukekim in #11206
  • fix(cluster): distribute accelerated tables wrapped by metadata/index providers by @phillipleblanc in #11226
  • Improve Cayenne adaptive tuning and schema safety by @lukekim in #11237
  • feat: Add cayenne_file_pruning param by @peasee in #11239
  • Debezium connector - handle tombstone messages in kafka topic, with schema evolution enabled by @ewgenius in #11197
  • feat(cayenne): broadcast small-dimension joins to executors by @phillipleblanc in #11245
  • fix: scope request context across the managed query runtime by @phillipleblanc in #11253
  • fix: Strip inference columns from table schema on query by @peasee in #11251
  • fix(flightsql): don't drop un-pushed FilterExec predicates in distributed pushdown rules by @claudespice in #11256
  • feat(postgres): share one replication slot across multiple changes-mode datasets by @phillipleblanc in #11255
  • Upgrade to DataFusion v53.1, Arrow v58.3, Vortex v0.74, and dependencies by @lukekim in #11118
  • feat(connectors): support file_format: vortex everywhere parquet is supported by @lukekim in #11282
  • perf(cayenne): metadata-only publish β€” drop per-key insert records on upsert commit by @lukekim in #11260
  • fix(kafka): harden fetch_latest_message for multi-partition topics by @ewgenius in #11285
  • perf(cayenne): bound mem-tier checkpoint churn + O(1) per-scan deletion view by @lukekim in #11249
  • fix(udfs): length-prefix digest_many values so column boundaries can't collide (fixes #11272) by @claudespice in #11288
  • fix: restore federation deny-list enforcement regressed by the DataFusion 53 upgrade by @claudespice in #11294
  • fix(postgres): recover unchanged-TOAST columns from the old tuple; classify walsender contention as transient by @phillipleblanc in #11293
  • feat: deepen extended schema inference and wire it into cayenne compaction sharding/sorting by @lukekim in #11284
  • perf(cayenne): in-memory CDC tier follow-ups + write-phase observability by @lukekim in #11278
  • Support per-dataset CDC tunable overrides by @sgrebnov in #11295
  • feat(cayenne): harden adaptive auto-tuner (controller hygiene, mem-tier actuator, delete/burst signals, single opt-in) by @lukekim in #11302
  • fix(cayenne): scan inlined-view capture starvation under sustained CDC (analytical QPH) by @lukekim in #11299
  • fix(vortex): don't row-evaluate hash-join dynamic filters in the scan by @sgrebnov in #11307
  • fix(deps): bump rust-postgres crates (RUSTSEC-2026-0178/0179) by @lukekim in #11313
  • fix: strict validation of Postgres CDC params instead of silent defaults (fixes #11274) by @claudespice in #11304
  • fix(duckdb): always quote on-refresh sort columns so reserved-word names don't break refresh by @claudespice in #11305
  • feat(flightsql): fall back to original connection when endpoint location is unreachable by @melks in #11287
  • perf(cayenne): light-encode transient staged CDC deltas by @lukekim in #11311
  • feat(acceleration): widening-only schema evolution via on_schema_change by @lukekim in #11261
  • deps(vortex): bump pin to spiceai-53 HEAD β€” intra-file decode split + per-execution kernel cache by @lukekim in #11314
  • feat(github): enhance GitHub component validation and error handling by @lukekim in #11259
  • feat(cayenne): goal-driven adaptive tuning toward operator SLOs (lag, freshness, query latency, QPH) by @lukekim in #11310
  • fix(cayenne): broadcast-join rewrite must bail on ambiguous columns, NULL-equal joins, and residual filters by @claudespice in #11252
  • Add Cayenne maintained aggregates by @lukekim in #11235
  • perf(cayenne): single-hash composite deletion filter via KeyDeletionIndex::get_batch by @phillipleblanc in #11325
  • fix(Vortex): decline only the InList membership conjunct of hash-join dynamic filters by @sgrebnov in #11335
  • fix: scope SQL UDF arg inlining to args-table columns (fixes #11273) by @claudespice in #11337
  • fix(refresh): restore S3 ETag/Version refresh-skip behind provider wrappers by @phillipleblanc in #11339
  • fix(cayenne): ref-count in-flight scans so GC can't delete Vortex files mid-read by @phillipleblanc in #11321
  • fix(runtime): retry object-store dataset load when source files are not yet available by @phillipleblanc in #11342
  • feat(s3): default to path-style for dotted bucket names on standard AWS by @phillipleblanc in #11347
  • fix(runtime): resolve accelerated table through metadata-enrichment wrapper by @phillipleblanc in #11345
  • fix: detect dual-write accelerated tables behind the metadata-enrichment wrapper by @claudespice in #11351
  • feat(cayenne): incremental seq-prefix bake β€” shrink the merge-on-read deletion index by @lukekim in #11326
  • fix(adbc): prevent Spice-specific UDFs from being pushed down to ADBC sources by @krinart in #11297
  • fix: Query Redshift schema details from svv_redshift tables by @peasee in #11362
  • perf(cayenne): tune Turso connection PRAGMAs + jitter metastore retries by @lukekim in #11359
  • Upgrade to DataFusion 54 by @sgrebnov in #11360
  • feat(runtime): dedicated CDC-apply tokio runtime + per-runtime tokio metrics by @lukekim in #11370
  • fix(cayenne): spill oversized hash joins via sort-merge to avoid OOM by @lukekim in #11371
  • fix(cayenne): honor cayenne_metastore: turso for partitioned tables and the dataset checkpoint by @phillipleblanc in #11365
  • perf(cayenne): in-RAM scan parallelism, query admission control, skip no-op deletion encode, sound scan output_ordering by @lukekim in #11332
  • fix(catalog): restore DDL after DataFusion 54 broke transparent catalog-provider downcasts by @phillipleblanc in #11375
  • feat(flightsql): infer schema via SELECT * LIMIT 1 when GetTables is unimplemented by @melks in #11286
  • fix(cayenne): Utf8View read schema avoids hash-join offset overflow by @lukekim in #11379
  • feat(cluster): support distributed (Ballista) scans of Iceberg tables by @phillipleblanc in #11378
  • feat(optimizer): cost-based left-deep join reordering for Cayenne acceleration by @sgrebnov in #11377
  • cli - fix service-account auth in spice cloud * commands by @ewgenius in #11316
  • fix: Support external Redshift table schema inference and Hive external type parsing by @peasee in #11399
  • feat(llms): native GLM support β€” opt-in flash-attn + surface reasoning_content by @lukekim in #11400
  • feat(s3_vectors): paginate QueryVectors for topK up to 10,000 by @bjchambers in #11405
  • fix: surface .env parse errors with line numbers instead of silently skipping by @Oxygen56 in #11306
  • fix(status): probe metrics endpoint over https when TLS is enabled by @phillipleblanc in #11393
  • fix(mcp): record task_history spans for tool calls proxied through /v1/mcp by @phillipleblanc in #11397
  • Properly handle date_trunc in BigQueryDialect by @krinart in #11416
  • fix(snowflake): honor column scale in Int64 timestamp arm and cast TIME by @claudespice in #11418
  • fix: /v1/queries chunk row_offset uses cumulative offset, not chunk_index * chunk_size (fixes #11271) by @claudespice in #11398
  • feat(cayenne): incremental retraction for maintained aggregates + anchor bench by @lukekim in #11389
  • feat(cluster): support distributed (Ballista) scans of Iceberg catalog tables by @phillipleblanc in #11419
  • Simplify chat/responses models by @Jeadie in #10997
  • feat(llms): distributed tensor-parallel GLM inference via mistral.rs ring backend by @lukekim in #11406
  • Optimize Flight streaming for large result sets by @lukekim in #11420
  • fix(deps): evict rustls 0.21 / rustls-webpki 0.101.7 (GHSA-82j2-j2ch-gfr8) by @phillipleblanc in #11428
  • feat(cayenne): in-memory CDC intra-apply sharding (PK-hash shards) by @lukekim in #11421
  • fix(cayenne): shard CDC upserts with pending deletions so the N>1 slot-ack advances by @lukekim in #11445
  • fix(cluster): keep built-in avg over Spark avg (distributed aggregate state schema mismatch) by @phillipleblanc in #11434
  • feat(views): support params.file_format for embedding chunking by @Jeadie in #11424
  • fix: offload blocking sync calls off the primary async runtime by @phillipleblanc in #11435
  • fix(udfs): rebind dot_product alias to Spice's inner_product on DataFusion 54 by @lukekim in #11443
  • feat(secrets): add full-fidelity reference iteration and a resolution-status API by @phillipleblanc in #11195
  • fix: Deny unsupported array functions for Postgres pushdown by @peasee in #11450
  • fix(cli): spice query honors --http-endpoint instead of failing on a Flight connect by @phillipleblanc in #11452
  • fix(cayenne): coordinate query-pool + in-memory CDC tier budgets to prevent adaptive OOM by @lukekim in #11449
  • Surface mTLS config in Kafka data connector by @v1gnesh in #11372
  • feat(secrets): check and report secret references at startup by @phillipleblanc in #11457
  • Default cayenne_force_view_types to false by @sgrebnov in #11459
  • fix: resolve table-reference qualification in results-cache invalidation (fixes #11266) by @claudespice in #11460
  • feat(cayenne): metadata aggregate pushdown β€” fold whole-table SUM/AVG/COUNT/MIN/MAX from statistics by @bjchambers in #11414
  • fix(search): restore numeric trunc and fix SortPreservingMergeExec planning error (DF54) by @Jeadie in #11415
  • fix(cluster): route Ballista shuffle/temp to the data PVC by @phillipleblanc in #11454
  • fix(search): default Elasticsearch kNN candidate pool to 1000 instead of 10 (fixes #11264) by @claudespice in #11467
  • feat(acceleration): add on_schema_change drop_and_recreate policy by @lukekim in #11462
  • feat(cayenne): predicate-aware maintained aggregates serve filtered analytical queries from the CDC delta by @lukekim in #11458
  • feat(cluster): shared Ballista job state with scheduler failover by @phillipleblanc in #11436
  • feat(cayenne): extend HLL NDV sketching to string and date columns by @bjchambers in #11468
  • fix(search): persist character chunk offsets so search snippets aren't shifted/garbled (fixes #11269) by @claudespice in #11479
  • feat(cayenne): storage-aware adaptive CDC tuning β€” calibration probe, IMDS, I/O-cliff fast path, infeasible-SLO feedback by @lukekim in #11463
  • fix(cayenne): LIMIT N under-delivers on key-deletion tables by @lukekim in #11490
  • fix(cayenne): live/tier-accurate join build-side stats (merge-on-read deletes + never-shrink NDV) by @lukekim in #11496
  • perf(cayenne): kernel-space I/O hygiene β€” compaction fadvise + staged-commit barrier reduction by @lukekim in #11495
  • feat(cayenne): feed maintained-aggregate IVM from the staged-disk CDC path by @lukekim in #11491
  • feat(cayenne): global adaptive-tuning SLOs with per-dataset overrides; QPH global-only by @lukekim in #11497
  • Update search snapshots by @sgrebnov in #11473
  • fix: Support reading column types longer than 128 chars in Redshift by @peasee in #11500
  • fix(cluster): distributed (Ballista) query-execution config + scheduled SF10 bench by @phillipleblanc in #11478
  • fix(http): retry transient response-body read failures; de-flake backoff test by @claudespice in #11482
  • Re-land orphaned deletion-vector cleanup during retention deletes by @lukekim in #11501
  • fix(cayenne): re-upsert over a pending delete tombstone records an insert-record (overwrite resurrection) by @bjchambers in #11469
  • fix: Flight DoPut silently dropped client batches on early sink completion by @claudespice in #11507
  • Upgrade OpenTelemetry to 0.32 and reqwest to 0.13 by @phillipleblanc in #11506
  • fix(queries): run async /v1/queries jobs under the submitting request context by @phillipleblanc in #11505
  • fix(cayenne): restore append-only current-snapshot compaction by @Jeadie in #11439
  • perf(cayenne): orphaned deletion-vector cleanup off the write path, behind a knob by @bjchambers in #11517
  • fix(datafusion): accurate projected scan byte size so hash joins build the smaller side by @sgrebnov in #11503
  • fix(cayenne): user-visible DELETE WHERE pk IN (...) reports the real row count by @lukekim in #11514
  • fix(cayenne): seed persisted num_rows for hash-join sizing by @sgrebnov in #11515
  • fix(cayenne): correctness & memory_limit fixes from perf audit (2 P0, 3 P1) by @lukekim in #11516
  • feat(cayenne): wire orphaned-DV cleanup knob to spicepod params + doc sync by @bjchambers in #11523
  • Fix s3 vectors API by @krinart in #11536
  • fix: Placeholder table initialization lock swap by @peasee in #11540
  • chore(cluster): bump ballista pin for the null-aware anti-join fix by @phillipleblanc in #11544
  • fix(runtime-tools): fix memory table identifier validation rejecting valid names by @Jeadie in #11546
  • Bump datafusion to include spiceai/datafusion#181 by @Jeadie in #11563
  • Update deny.toml by @krinart in #11571
  • Bump datafusion (spiceai/datafusion#182) and datafusion-table-providers (#27): fix q16 CollectLeft planning error and SQLite q6 wrong revenue by @Jeadie in #11598

Full Changelog: https://github.com/spiceai/spiceai/compare/v2.0.0...v2.1.0

Spice v2.0-rc.4 (Apr 30, 2026)

Β· 22 min read
William Croxson
Member of Technical Staff at Spice AI

Announcing the release of Spice v2.0-rc.4! πŸš€

v2.0.0-rc.4 is the fourth release candidate for advanced testing of v2.0, building on v2.0.0-rc.3.

Highlights in this release candidate include:

  • Elasticsearch Data Connector (Alpha) with native hybrid search (BM25 full-text + kNN vector + RRF)
  • PostgreSQL Native CDC via WAL logical replication, eliminating the need for Debezium or Kafka
  • Multi-vector Embeddings with MaxSim for ColBERT-style late-interaction retrieval
  • Rerank UDTF for hybrid search pipelines with automatic query propagation
  • HashiCorp Vault and Azure Key Vault Secret Stores for enterprise secret management
  • DuckDB Vector Engine with HNSW index support
  • Azure Cosmos DB Connector (RC), Git Connector promoted to RC
  • MCP Streamable HTTP transport
  • Read-only API Key Enforcement on Flight DoGet and async query paths

What's New in v2.0.0-rc.4​

Elasticsearch Data Connector (Alpha, Spice.ai Enterprise)​

The new Elasticsearch data connector enables querying Elasticsearch indexes as SQL tables with full hybrid search support. Currently available in Spice.ai Enterprise.

Key capabilities:

  • SQL Table Access: Query any Elasticsearch index with standard SQL via a native DataFusion TableProvider.
  • kNN Vector Search: Use the vector_search() UDTF against Elasticsearch-backed vector fields.
  • BM25 Full-Text Search: Use the text_search() UDTF for native Elasticsearch full-text queries.
  • Hybrid Search: Combine kNN and BM25 results with the rrf() UDTF for reciprocal rank fusion.
  • Elasticsearch as a Vector Engine: Accelerated datasets can use Elasticsearch as the backing vector engine for embedding storage and retrieval.

Example configuration:

datasets:
- from: elasticsearch:my_index
name: my_data
params:
elasticsearch_endpoint: https://my-cluster.es.io:9200
elasticsearch_username: ${secrets:es_user}
elasticsearch_password: ${secrets:es_password}

PostgreSQL Native Replication via WAL​

Postgres datasets configured with refresh_mode: changes can now stream changes directly from PostgreSQL logical replication (WAL) into any local accelerator without Debezium or Kafka required.

Key capabilities:

  • Native Logical Replication: Uses pgoutput decoding to stream INSERT/UPDATE/DELETE events.
  • Automatic Slot Management: Each Spice replica creates a distinct replication slot (spice_<dataset>_<hash>), so multi-replica deployments work automatically. Publications are shared.
  • Bootstrap Snapshot: An initial REPEATABLE READ snapshot seeds the accelerator before replication begins.
  • LSN Acknowledgement: The LsnCommitter sends durable LSN back to Postgres so WAL segments are reclaimed.
  • All Accelerators Supported: Works with DuckDB, SQLite, Postgres, Cayenne, and Arrow accelerators.

Example configuration:

datasets:
- from: postgres:my_table
name: my_table
params:
pg_host: localhost
pg_port: 5432
pg_db: mydb
pg_publication: my_publication # optional; auto-created if omitted
acceleration:
enabled: true
engine: duckdb
refresh_mode: changes

Multi-vector Embeddings with MaxSim (Late Interaction)​

Column-level embeddings now support list-of-string columns, producing one embedding vector per list element and enabling ColBERT-style late-interaction retrieval.

Key capabilities:

  • Multi-vector per Row: Columns of type List<String> produce List<FixedSizeList<F32, D>> β€” one embedding per list element.
  • MaxSim / Mean / Sum Scoring: Per-row score is the max, mean, or sum cosine over the list elements. Default is MaxSim (ColBERT).
  • _match Column: Returns the specific list element that produced the highest cosine similarity.
  • No Schema Changes Required: Works with existing embedding configurations; activates automatically for list-type columns.

A new rerank() table-valued function reorders scored results from vector_search, text_search, or rrf by a reranker model's relevance judgements. See Search Functionality for an overview of search UDTFs.

Key capabilities:

  • Auto Query Propagation: The query string is automatically inherited from a nested search UDTF β€” no repetition required.
  • Any Chat Model as Reranker: Any registered chat/completion model can serve as a reranker via the built-in LlmRerank adapter (listwise prompt by default; pointwise available).
  • Filter and Projection Pushdown: The RerankExec physical node supports pushdown, reducing data movement.
  • Extensible: A new RerankerModelStore sits alongside ChatModelStore and EmbeddingModelStore; native providers (Cohere, Voyage, BGE) can be added without runtime plumbing changes.
SELECT * FROM rerank(
rrf(vector_search('my_table', 'query text'), text_search('my_table', 'query text')),
document => content
) LIMIT 10;

New Secret Stores: HashiCorp Vault and Azure Key Vault​

Two new enterprise-grade Secret Stores are now available.

HashiCorp Vault (hashicorp_vault):

  • KV v2 (default) and KV v1 mount support.
  • Auth methods: token, approle, kubernetes, jwt.
  • Token leases are cached and automatically re-acquired on expiry.
secrets:
- from: hashicorp_vault:secret/my-app
name: my_secrets
params:
hashicorp_vault_addr: https://vault.example.com
hashicorp_vault_auth_method: approle
hashicorp_vault_role_id: ${env:VAULT_ROLE_ID}
hashicorp_vault_secret_id: ${secrets:vault_secret_id}

Azure Key Vault (azure_keyvault):

  • Per-key caching with single-flight fetch coalescing.
  • Auth methods: service principal, managed identity, workload identity, Azure CLI, or auto-detect.
  • Supports sovereign clouds via endpoint parameter.
secrets:
- from: azure_keyvault:my-vault
name: my_secrets
params:
azure_keyvault_auth_method: managed_identity

DuckDB Vector Engine​

DuckDB-accelerated tables can now use DuckDB's HNSW index for vector search via the vector_engine: duckdb option, enabling fast approximate nearest-neighbor search without an external vector store.

Example configuration:

datasets:
- from: postgres:public.documents
name: documents
columns:
- name: content
embeddings:
- from: hf_minilm
row_id: id
acceleration:
enabled: true
engine: duckdb
mode: file
vectors:
enabled: true
engine: duckdb
params:
duckdb_distance_metric: cosine
duckdb_hnsw_m: 16
duckdb_hnsw_ef_construction: 64
duckdb_hnsw_ef_search: 32

embeddings:
- from: huggingface:huggingface.co/minishlab/potion-base-2M
name: hf_minilm

New and Promoted Connectors​

Azure Cosmos DB (Alpha):

A new read-only Azure Cosmos DB NoSQL / Core SQL API connector built on the azure_data_cosmos 0.30 SDK. Supports cross-partition scans, schema inference from document samples, and key-based auth (connection string or account endpoint + key).

Git Connector (RC):

The Git data connector is promoted to RC status with HTTPS/SSH auth (git_token, git_username/git_password, git_ssh_key), Git LFS support (enable_lfs), and per-repo connection resilience (semaphore, bounded retries with exponential backoff, permanent-error circuit breaking).

DynamoDB Write Support (DML)​

DynamoDB datasets now support write-back via INSERT, UPDATE, and DELETE operations, complementing the existing read and CDC streaming capabilities.

MCP Streamable HTTP Transport​

The MCP server has been upgraded to rmcp 1.5.0 and switched to the Streamable HTTP transport (/v1/mcp), replacing the previous SSE-based endpoint. The client-side transport is updated to StreamableHttpClientTransport.

Security Improvements​

Read-only API Key Enforcement: API keys with read-only scope are now strictly enforced on the Flight DoGet path and on async query endpoints, preventing write operations from being issued under a read-only key.

GitHub Workflow Hardening: CI workflows have been hardened with improved security posture to reduce supply-chain risk.

Developer Experience Improvements​

  • Actionable Config Errors: Parameter typos, missing secret references, and unknown engine names now produce specific, actionable error messages with Levenshtein-based suggestions, rather than silent drops or generic "missing required parameter" messages.
  • spice init Improvements: Written spicepods now include a yaml-language-server: $schema=... directive for IDE completions. Creation messages print regardless of log level.
  • REPL Improvements: Log filter honors RUST_LOG when -v is not passed; version banner moves to stderr and prints only on an interactive TTY.
  • 403 / 401 Routing: HTTP 403 responses route to a new PermissionDenied variant; 401 messages point at spice login / SPICE_API_KEY.

OpenTelemetry Improvements​

See Observability & Monitoring and the runtime.telemetry reference for full configuration details.

  • Metric Name Prefix: Configure a prefix for all exported OTLP metric names via runtime.telemetry.metric_prefix.
  • Delta Temporality Default: The OTLP push exporter now defaults to delta temporality, matching Prometheus and most backends.
  • Resource Attributes: runtime.telemetry.properties are applied as OTLP resource attributes on exported metrics.

Full-text Search Performance​

Tantivy full-text search ingestion performance is significantly improved with better batch handling and a rollback-on-error path.

SQL and Query Engine​

  • DataFusion Upgrade: Updated to a newer DataFusion revision with additional bug fixes and performance improvements.
  • Views on DDL Catalogs: DDL-defined catalogs (e.g., Unity Catalog) can now expose and query views.
  • flatten_json / json_tree / expand_maps UDTFs: New table-valued functions for JSON transformation, map expansion, and schema decomposition in query pipelines. See JSON Functions and Operators.
  • cosine_distance Pushdown to DuckDB: cosine_distance is now pushed down to DuckDB accelerators via array_cosine_distance.
  • Snowflake Type Support: Added support for OBJECT, MAP, GEOGRAPHY, GEOMETRY, VECTOR, and TIMESTAMP_LTZ types in the Snowflake connector.
  • MySQL Zero-Date Behavior: The MySQL connector adds a new mysql_zero_date_behavior parameter (null or error) controlling how MySQL zero-date values (0000-00-00) are handled.
  • Databricks Timeouts: The Databricks connector adds new connect_timeout and client_timeout parameters for sql_warehouse mode.

Dependency Updates​

Dependency / ComponentVersion / Update
DataFusionUpdated
rmcpv1.5.0 (from fork pin)
mistral.rsUpdated
openssl0.10.78

Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

No new cookbook recipes.

The Spice Cookbook includes 86 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v2.0.0-rc.4, use one of the following methods:

CLI:

spice upgrade v2.0.0-rc.4

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:2.0.0-rc.4 image:

docker pull spiceai/spiceai:2.0.0-rc.4

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai --version 2.0.0-rc.4

AWS Marketplace:

Spice is available in the AWS Marketplace.

What's Changed​

Changelog​

Full Changelog: https://github.com/spiceai/spiceai/compare/v2.0.0-rc.3...v2.0.0-rc.4

Spice v1.9.0 (Nov 19, 2025)

Β· 59 min read
Phillip LeBlanc
Co-Founder and CTO of Spice AI

Announcing the release of Spice v1.9.0-stable! 🌢

v1.9.0-stable introduces Spice Cayenne, a new high-performance data accelerator built on the Vortex columnar format that delivers better than DuckDB performance without single-file scaling limitations, and a preview of Multi-Node Distributed Query based on Apache Ballista. v1.9.0 also upgrades to DataFusion v50, DuckDB v1.4.2, and Delta-Kernel v0.16 for even higher query performance, expands search capabilities with full-text search on views and multi-column embeddings, and delivers many additional features and improvements.

What's New in v1.9.0​

Cayenne Data Accelerator (Beta)​

Introducing Cayenne: SQL as an Acceleration Format: A new high-performance Data Accelerator that simplifies multi-file data acceleration by using an embedded database (SQLite) for metadata while storing data in the Vortex columnar format, a Linux Foundation project. Cayenne delivers query and ingestion performance better than DuckDB's file-based acceleration without DuckDB's memory overhead and the scaling challenges of single DuckDB files.

Cayenne uses SQLite to manage acceleration metadata (schemas, snapshots, statistics, file tracking) through simple SQL transactions, while storing data in Vortex's compressed columnar format. This architecture provides:

Key Features:

  • SQLite + Vortex Architecture: All metadata is stored in SQLite tables with standard SQL transactions, while data lives in Vortex's compressed, chunked columnar format designed for zero-copy access and efficient scanning.
  • Simplified Operations: No complex file hierarchies, no JSON/Avro metadata files, no separate catalog serversβ€”just SQL tables and Vortex data files. The entire metadata schema is intentionally simple for maximum reliability.
  • Fast Metadata Access: Single SQL query retrieves all metadata needed for query planningβ€”no multiple round trips to storage, no S3 throttling, no reconstruction of metadata state from scattered files.
  • Efficient Small Changes: Dramatically reduces small file proliferation. Snapshots are just rows in SQLite tables, not new files on disk. Supports millions of snapshots without performance degradation.
  • High Concurrency: Changes consist of two steps: stage Vortex files (if any), then run a single SQL transaction. Much faster conflict resolution and support for many more concurrent updates than file-based formats.
  • Advanced Data Lifecycle: Full ACID transactions, delete support, and retention SQL execution on refresh commit.

Example Spicepod.yml configuration:

datasets:
- from: s3:my_table
name: accelerated_data_30d
acceleration:
enabled: true
engine: cayenne
mode: file
refresh_mode: append
retention_sql: DELETE FROM accelerated_data WHERE created_at < NOW() - INTERVAL '30 days'

Note, the Cayenne Data Accelerator is in Beta with limitations.

For more details, refer to the Cayenne Documentation, the Vortex project, and the DuckLake announcement that partly inspired this design.

Multi-Node Distributed Query (Preview)​

Apache Ballista Integration: Spice now supports distributed query execution based on Apache Ballista, enabling distributed queries across multiple executor nodes for improved performance on large datasets. This feature is in preview in v1.9.0.

Architecture:

A distributed Spice cluster consists of:

  • Scheduler: Responsible for distributed query planning and work queue management for the executor fleet
  • Executors: One or more nodes responsible for running physical query plans

Getting Started:

Start a scheduler instance using an existing Spicepod. The scheduler is the only spiced instance that needs to be configured:

# Start scheduler (note the flight bind address override if you want it reachable outside localhost)
spiced --cluster-mode scheduler --flight 0.0.0.0:50051

Start one or more executors configured with the scheduler's flight URI:

# Start executor (automatically selects a free port if 50051 is taken)
spiced --cluster-mode executor --scheduler-url spiced://localhost:50051

Query Execution:

Queries run through the scheduler will now show a distributed_plan in EXPLAIN output, demonstrating how the query is distributed across executor nodes:

EXPLAIN SELECT count(id) FROM my_dataset;

Current Limitations:

  • Accelerated datasets are currently not supported. This feature is designed for querying partitioned data lake formats (Parquet, Delta Lake, Iceberg, etc.)
  • The feature is in preview and may have stability or performance limitations
  • Specific acceleration support is planned for future releases

For more details, refer to the Distributed Query Documentation.

DataFusion v50 Upgrade​

Spice.ai is built on the Apache DataFusion query engine. The v50 release brings significant performance improvements and enhanced reliability:

Performance Improvements πŸš€:

  • Dynamic Filter Pushdown: Enhanced dynamic filter pushdown for custom ExecutionPlans, ensuring filters propagate correctly through all physical operators for improved query performance.

  • Partition Pruning: Expanded partition pruning support ensures that unnecessary partitions are skipped when filters are not used, reducing data scanning overhead and improving query execution times.

Apache Spark Compatible Functions: Added support for Spark-compatible functions including array, bit_get/bit_count, bitmap_count, crc32/sha1, date_add/date_sub, if, last_day, like/ilike, luhn_check, mod/pmod, next_day, parse_url, rint, and width_bucket.

Bug Fixes & Reliability: Resolved issues with partition name validation and empty execution plans when vector index lists are empty. Fixed timestamp support for partition expressions, enabling better partitioning for time-series data.

See the Apache DataFusion 50.0.3 Release for more details.

DuckDB v1.4.2 Upgrade and Accelerator Improvements​

DuckDB v1.4.2: DuckDB has been upgraded to v1.4.2, which includes several performance optimizations.

Composite ART Index Support: DuckDB in Spice now supports composite (multi-column) Adaptive Radix Tree (ART) indexes for accelerated table scans. When queries filter on multiple columns fully covered by a composite index, the optimizer automatically uses index scans instead of full table scans, delivering significant performance improvements for selective queries.

Example configuration:

datasets:
- from: file://data.parquet
name: sales
acceleration:
enabled: true
engine: duckdb
indexes:
'(region, product_id)': enabled

Performance example with composite index on 7.5M rows:

SELECT * FROM sales WHERE region = 'US' AND product_id = 12345;

-- Without index: 0.282s
-- With composite index (region, product_id): 0.037s
-- Performance improvement: 7.6x faster with composite index

DuckDB Intermediate Materialization: Queries with indexes now use intermediate materialization (WITH ... AS MATERIALIZED) to leverage faster index scans. Currently supported for non-federated queries (query_federation: disabled) against a single table with indexes only. When predicates cover more columns than the index, the optimizer rewrites queries to first materialize index-filtered results, then apply remaining predicates. This optimization can deliver significant performance improvements for selective queries.

Example configuration:

datasets:
- from: file://sales_data.parquet
name: sales
acceleration:
enabled: true
engine: duckdb
mode: file
params:
query_federation: disabled # Required currently for intermediate materialization
indexes:
'(region, product_id)': enabled

Performance example:

-- Query with indexed columns (region, product_id) plus additional filter (amount)
SELECT * FROM sales
WHERE region = 'US' AND product_id = 12345 AND amount > 1000;

-- Optimized execution time: 0.031s (with intermediate materialization)
-- Standard execution time: 0.108s (without optimization)
-- Performance improvement: ~3.5x faster

The optimizer automatically rewrites the query to:

WITH _intermediate_materialize AS MATERIALIZED (
SELECT * FROM sales WHERE region = 'US' AND product_id = 12345
)
SELECT * FROM _intermediate_materialize WHERE amount > 1000;

Parquet Buffering for Partitioned Writes: DuckDB partitioned writes in table mode now support Parquet buffering, reducing memory usage and improving write performance for large datasets.

Retention SQL on Refresh Commit: DuckDB accelerations now support running retention SQL on refresh commit, enabling automatic data cleanup and lifecycle management during refresh operations.

UTC Timezone for DuckDB: DuckDB now uses UTC as the default timezone, ensuring consistent behavior for time-based queries across different environments.

Example Spicepod.yml configuration:

datasets:
- from: s3://my_bucket/large_table/
name: partitioned_data
acceleration:
enabled: true
engine: duckdb
mode: file
retention:
sql: DELETE FROM partitioned_data WHERE event_time < NOW() - INTERVAL '7 days'

For more details, refer to the DuckDB Data Accelerator Documentation.

HTTP Data Connector​

  • Querying endpoints as tables: The HTTP/HTTPS Data Connectors now supports querying HTTP endpoints directly as tables in SQL queries with dynamic filters. This feature transforms REST APIs into queryable data sources, making it easy to integrate external service data.

  • Query HTTP endpoint that returns structured data (JSON, CSV, etc.) as if it were a database table

  • Configurable retry logic, timeouts, and POST request support for more complex API interactions

Example Spicepod.yml configuration:

datasets:
- from: https://api.tvmaze.com
name: tvmaze
params:
file_format: json
max_retries: 3
client_timeout: 10s
allowed_request_paths: /search/people
request_query_filters: enabled
request_body_filters: enabled

Example SQL query:

SELECT request_path, request_query, content
FROM tvmaze
WHERE request_path = '/search/people' and request_query = 'q=michael'
LIMIT 10;

If a request_body is supplied it will be posted to the endpoint:

Example SQL query:

SELECT request_path, request_query, content
FROM tvmaze
WHERE request_path = '/search/people' and request_query = 'q=michael' and request_body = '{"name": "michael"}'
LIMIT 10;

HTTP endpoints can be accelerated using refresh_sql:

datasets:
- from: https://api.tvmaze.com
name: tvmaze
params:
file_format: json
allowed_request_paths: /search/people
request_query_filters: enabled
request_body_filters: enabled
acceleration:
enabled: true
refresh_mode: full
refresh_sql: |
SELECT request_path, request_query, content
FROM tvmaze
WHERE request_path = '/search/people'
AND request_query IN ('q=michael', 'q=luke')

For more details, refer to the HTTP Data Connector Documentation.

DynamoDB Data Connector Improvements​

Improved Query Performance: The DynamoDB Data Connector now includes improved filter handling for edge cases, parallel scan support for faster data ingestion, and better error handling for misconfigured queries. These improvements enable more reliable and performant access to DynamoDB data.

Example Spicepod.yml configuration:

datasets:
- from: dynamodb:my_table
name: ddb_data
params:
scan_segments: 10 # Default `auto` which calculates optimal segments based on number of rows

For more details, refer to the DynamoDB Data Connector Documentation.

S3 Data Connector Improvements​

S3 Versioning Support: Spice now supports S3 Versioning for all connectors using object-store (S3, Delta Lake, etc.), ensuring range reads over versioned files are atomically correct. When S3 versioning is enabled, Spice automatically tracks version IDs during file discovery and uses them for all subsequent range reads, preventing inconsistencies from concurrent file modifications.

Current limitations:

  • Multi-file connections (e.g., partitioned datasets) do not yet support version tracking across all files
  • Version tracking is automatic when S3 versioning is enabled on the bucket

S3 Single-File Refresh Skipping: Spice now optimizes S3 single-file dataset refreshes by caching file metadata (ETag, Version ID, size, timestamp) and skipping unnecessary data fetches when the underlying file hasn't changed. This optimization dramatically reduces bandwidth usage and improves refresh performance for scenarios where data doesn't change frequently. The feature is enabled by default for accelerated S3 single-file datasets and includes metrics tracking for skipped refreshes.

Example configuration:

datasets:
- from: s3://my-bucket/data.parquet
name: s3_data
acceleration:
enabled: true
engine: duckdb
refresh_check_interval: 10s

When the file's metadata hasn't changed between refresh checks, Spice will skip the data fetch entirely, logging:

Skipping refresh for dataset 's3_data': file metadata unchanged

For more details, refer to the S3 Data Connector Documentation.

Search & Embeddings Enhancements​

Full-Text Search on Views: Full-text search indexes are now supported on views, enabling advanced search scenarios over pre-aggregated or transformed data. This extends the power of Spice's search capabilities beyond base datasets.

Multi-Column Embeddings on Views: Views now support embedding columns, enabling vector search and semantic retrieval on view data. This is useful for search over aggregated or joined datasets.

Vector Engines on Views: Vector search engines are now available for views, enabling similarity search over complex queries and transformations.

Example Spicepod.yml configuration:

views:
- name: aggregated_reviews
sql: SELECT review_id, review_text FROM reviews WHERE rating > 4
embeddings:
- column: review_text
model: openai:text-embedding-3-small

For more details, refer to the Search Documentation and Embeddings Documentation.

Dedicated Query Thread Pool (Now Enabled by Default)​

Dedicated Query Thread Pool: Query execution and accelerated refreshes now run on their own dedicated thread pool, separate from the HTTP server. This prevents heavy query workloads from slowing down API responses, keeping health checks fast and avoiding unnecessary Kubernetes pod restarts under load.

This feature was opt-in in previous releases and is now enabled by default. To disable it and revert to the previous behavior, add the following spicepod.yaml configuration:

runtime:
params:
dedicated_thread_pool: none

For more details, refer to the Runtime Configuration Documentation.

Query Performance Optimizations​

Stale-While-Revalidate Cache Control: Query results now support "stale-while-revalidate" cache control, allowing stale cached data to be served immediately while asynchronously refreshing the cache entry in the background. This improves response times for frequently-accessed queries while maintaining data freshness. Requires cache key type to be set to "sql (raw)" for proper operation.

Optimized Prepared Statements: Prepared statement handling has been optimized for better performance with parameterized queries, reducing planning overhead and improving execution time for repeated queries.

Large RecordBatch Chunking: Large Arrow RecordBatch objects are now automatically chunked to control memory usage during query execution, preventing memory exhaustion for queries returning large result sets.

Query Result Caching: Compressed Encoding, Stale-While-Revalidate Cache Control​

Zstd Compression Encoding: Query result caching now supports optional Zstandard (zstd) compression encoding to reduce memory usage for cached query results. This is particularly beneficial for large result sets, reducing cache memory footprint while maintaining fast decompression times. Encoding can be configured via the encoding parameter with options none (default) or zstd.

Example configuration:

runtime:
caching:
sql_results:
enabled: true
max_size: 128MiB
item_ttl: 1m
encoding: zstd # Enable zstd compression

HTTP Cache-Control Support: The query result cache now supports the stale-while-revalidate Cache-Control directive, enabling faster response times by serving stale cached results immediately while asynchronously refreshing the cache in the background. This feature is particularly useful for applications that can tolerate slightly stale data in exchange for improved performance.

Example configuration:

runtime:
caching:
sql_results:
enabled: true
max_size: 128MiB
item_ttl: 1m
stale_while_revalidate_ttl: 1m # serve stale items for up to 1 minute after `item_ttl` expires

How it works:

When a cache entry is stale but within the stale-while-revalidate window, Spice will:

  1. Immediately return the stale cached result to the client
  2. Asynchronously re-execute the query in the background to refresh the cache
  3. Future requests will use the refreshed data

Configuration:

Use the Cache-Control HTTP header with the stale-while-revalidate directive:

Cache-Control: max-age=300, stale-while-revalidate=60

This configuration caches results for 5 minutes (300 seconds), and allows serving stale results for an additional 60 seconds while refreshing in the background.

Requirements:

  • Must use plan or raw SQL cache keys (set cache_key_type to sql or plan in results_caching configuration)
  • Background revalidation re-executes queries through the normal query path
  • Timestamp tracking automatically determines cache entry age for staleness checks

Example configuration via HTTP header:

GET /v1/sql
Cache-Control: max-age=600, stale-while-revalidate=120
X-Cache-Key-Type: sql

This feature improves application responsiveness while ensuring data freshness through background updates.

For more details, refer to the Results Caching Documentation.

Security & Reliability Improvements​

Enhanced HTTP Client Security: HTTP client usage across the runtime has been hardened with improved TLS validation, certificate pinning for critical endpoints, and better error handling for network failures.

ODBC Connector Improvements: Removed unwrap calls from the ODBC connector, improving error handling and reliability. Fixed secret handling and Kubernetes secret integration.

CLI Permissions Hardening: Tightened file permissions for the CLI and install script, ensuring secure defaults for configuration files and credentials.

Oracle Instant Client Pinning: Oracle Instant Client downloads are now pinned to specific SHAs, ensuring reproducible builds and preventing supply chain attacks.

AWS Authentication Improvements​

Improved Credential Retry Logic: AWS SDK credential initialization has been significantly improved with more robust retry logic and better error handling. The system now automatically retries transient credential resolution failures using Fibonacci backoff, allowing Spice to tolerate extended AWS outages (up to ~48 hours) without manual intervention.

Key features:

  • Automatic retry with backoff: Implements Fibonacci backoff for transient credential failures (network issues, temporary AWS service disruptions)
  • Better error handling: Distinguishes between retryable errors (connector errors) and non-retryable errors (misconfiguration)
  • Unauthenticated access support: Properly supports unauthenticated access to public S3 buckets without requiring credentials
  • Improved error messages: Provides detailed logging with attempt numbers, retry intervals, and error context for better troubleshooting

The improvements ensure more reliable AWS service integration, particularly in environments with intermittent network connectivity or during AWS service degradations.

Observability & Tracing​

DataFusion Log Emission: The Spice runtime now emits DataFusion internal logs, providing deeper visibility into query planning and execution for debugging and performance analysis.

AI Completions Tracing: Fixed tracing so that ai_completions operations are correctly parented under sql_query traces, improving observability for AI-powered queries.

Git Data Connector (Alpha)​

Version-Controlled Data Access: The new Git Data Connector (Alpha) enables querying datasets stored in Git repositories. This connector is ideal for use cases involving configuration files, documentation, or any data tracked in version control.

Example Spicepod.yml configuration:

datasets:
- from: git:https://github.com/myorg/myrepo
name: git_metrics
params:
file_format: csv

For more details, refer to the Git Data Connector Documentation.

Spice Java SDK 0.4.0​

The Spice Java SDK has been upgraded with support for configurable Arrow memory limit: spice-java v0.4.0

SpiceClient client = SpiceClient.builder()
.withArrowMemoryLimitMB(1024) // 1GB limit
.build();

For more details, refer to the Java SDK Documentation.

CLI Improvements​

Install Specific Versions: The spice install command now supports installing specific versions of the Spice runtime and CLI. This enables easy version management, downgrading, or installation of specific releases for testing or compatibility requirements.

Usage:

# Install a specific version
spice install v1.8.3

# Install a specific version with AI flavor
spice install v1.8.3 ai

# Install latest version (existing behavior)
spice install
spice install ai

Note: Homebrew installations require manual version management via brew install spiceai/spiceai/spice@<version>.

Persistent Query History: The Spice CLI REPL (SQL, search, and chat interfaces) now persists command history to ~/.spice/query_history.txt, making your query history available across sessions. The history file is automatically created if it doesn't exist, with graceful fallback if the home directory cannot be determined.

New REPL Commands:

  • .clear - Clear the screen using ANSI escape codes for a clean workspace
  • .clear history - Clear and persist the query history, removing all stored commands

Tab Completion: Tab completion now includes suggestions based on your command history, making it faster to re-run or modify previous queries.

Example usage:

sql> SELECT * FROM my_table;
sql> .clear # Clears the screen
sql> .clear history # Clears command history
sql> # Use arrow keys or tab to access previous commands

For more details, refer to the CLI Documentation.

Additional Improvements & Bug Fixes​

  • Reliability: Fixed refresh worker panics with recovery handling to prevent runtime crashes during acceleration refreshes.
  • Reliability: Improved error messages for missing or invalid spicepod.yaml files, providing actionable feedback for misconfiguration.
  • Reliability: Fixed DuckDB metadata pointer loading issues for snapshots.
  • Performance: Ensured ListingTable partitions are pruned correctly when filters are not used.
  • Reliability: Fixed vector dimension determination for partitioned indexes.
  • Search: Fixed casing issues in Reciprocal Rank Fusion (RRF) for hybrid search queries.
  • Search: Fixed search field handling as metadata for chunked search indexes.
  • Validation: Added timestamp support for partition expressions.
  • Validation: Fixed regexp_match function for DuckDB datasets.
  • Validation: Fixed partition name validation for improved reliability.

Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

New HTTP Data Connector Recipe: New recipe demonstrating how to query REST APIs and HTTP(s) endpoints. See HTTP Connector Recipe for details.

The Spice Cookbook includes 82 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.9.0, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.9.0 image:

docker pull spiceai/spiceai:1.9.0

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

πŸŽ‰ Spice is now available in the AWS Marketplace!

What's Changed​

Dependencies​

Changelog​

Spice v1.9.0-rc.4 (Nov 18, 2025)

Β· 22 min read
Phillip LeBlanc
Co-Founder and CTO of Spice AI

Announcing the release of Spice v1.9.0-rc.4! 🌢

This release candidate brings DuckDB v1.4.2, Cayenne partitioning improvements, and comprehensive security hardening across the CLI, data connectors, runtime, and MCP. v1.9.0-rc.4 also includes MySQL and PostgreSQL connector improvements with fixed nullability inferences and full-text search support, DynamoDB consistency improvements, HTTP connector validation and UX enhancements, and numerous reliability and performance optimizations. Significant improvements were also made to test and automation infrastructure to ensure high quality releases.

v1.9.0 introduces Spice Cayenne, a new high-performance data accelerator built on the Vortex columnar format that delivers better than DuckDB performance without single-file scaling limitations, and a preview of Multi-Node Distributed Query based on Apache Ballista. v1.9.0 also upgrades to DataFusion v50 for even higher query performance, expands search capabilities with full-text search on views and multi-column embeddings, and delivers many additional features and improvements.

What's New in v1.9.0​

Cayenne Data Accelerator (Beta)​

Introducing Cayenne: SQL as an Acceleration Format: A new high-performance Data Accelerator that simplifies multi-file data acceleration by using an embedded database (SQLite) for metadata while storing data in the Vortex columnar format, a Linux Foundation project. Cayenne delivers query and ingestion performance better than DuckDB's file-based acceleration without DuckDB's memory overhead and the scaling challenges of single DuckDB files.

Cayenne uses SQLite to manage acceleration metadata (schemas, snapshots, statistics, file tracking) through simple SQL transactions, while storing data in Vortex's compressed columnar format. This architecture provides:

Key Features:

  • SQLite + Vortex Architecture: All metadata is stored in SQLite tables with standard SQL transactions, while data lives in Vortex's compressed, chunked columnar format designed for zero-copy access and efficient scanning.
  • Simplified Operations: No complex file hierarchies, no JSON/Avro metadata files, no separate catalog serversβ€”just SQL tables and Vortex data files. The entire metadata schema is intentionally simple for maximum reliability.
  • Fast Metadata Access: Single SQL query retrieves all metadata needed for query planningβ€”no multiple round trips to storage, no S3 throttling, no reconstruction of metadata state from scattered files.
  • Efficient Small Changes: Dramatically reduces small file proliferation. Snapshots are just rows in SQLite tables, not new files on disk. Supports millions of snapshots without performance degradation.
  • High Concurrency: Changes consist of two steps: stage Vortex files (if any), then run a single SQL transaction. Much faster conflict resolution and support for many more concurrent updates than file-based formats.
  • Advanced Data Lifecycle: Full ACID transactions, delete support, and retention SQL execution on refresh commit.

Example Spicepod.yml configuration:

datasets:
- from: s3:my_table
name: accelerated_data_30d
acceleration:
enabled: true
engine: cayenne
mode: file
refresh_mode: append
retention_sql: DELETE FROM accelerated_data WHERE created_at < NOW() - INTERVAL '30 days'

Note, the Cayenne Data Accelerator is in Beta with limitations.

For more details, refer to the Cayenne Documentation, the Vortex project, and the DuckLake announcement that partly inspired this design.

Multi-Node Distributed Query (Preview)​

Apache Ballista Integration: Spice now supports distributed query execution based on Apache Ballista, enabling distributed queries across multiple executor nodes for improved performance on large datasets. This feature is in preview in v1.9.0-rc.3.

Architecture:

A distributed Spice cluster consists of:

  • Scheduler: Responsible for distributed query planning and work queue management for the executor fleet
  • Executors: One or more nodes responsible for running physical query plans

Getting Started:

Start a scheduler instance using an existing Spicepod. The scheduler is the only spiced instance that needs to be configured:

# Start scheduler (note the flight bind address override if you want it reachable outside localhost)
spiced --cluster-mode scheduler --flight 0.0.0.0:50051

Start one or more executors configured with the scheduler's flight URI:

# Start executor (automatically selects a free port if 50051 is taken)
spiced --cluster-mode executor --scheduler-url spiced://localhost:50051

Query Execution:

Queries run through the scheduler will now show a distributed_plan in EXPLAIN output, demonstrating how the query is distributed across executor nodes:

EXPLAIN SELECT count(id) FROM my_dataset;

Current Limitations:

  • Accelerated datasets are currently not supported. This feature is designed for querying partitioned data lake formats (Parquet, Delta Lake, Iceberg, etc.)
  • The feature is in preview and may have stability or performance limitations
  • Specific acceleration support is planned for future releases

DataFusion v50 Upgrade​

Spice.ai is built on the Apache DataFusion query engine. The v50 release brings significant performance improvements and enhanced reliability:

Performance Improvements πŸš€:

  • Dynamic Filter Pushdown: Enhanced dynamic filter pushdown for custom ExecutionPlans, ensuring filters propagate correctly through all physical operators for improved query performance.

  • Partition Pruning: Expanded partition pruning support ensures that unnecessary partitions are skipped when filters are not used, reducing data scanning overhead and improving query execution times.

Apache Spark Compatible Functions: Added support for Spark-compatible functions including array, bit_get/bit_count, bitmap_count, crc32/sha1, date_add/date_sub, if, last_day, like/ilike, luhn_check, mod/pmod, next_day, parse_url, rint, and width_bucket.

Bug Fixes & Reliability: Resolved issues with partition name validation and empty execution plans when vector index lists are empty. Fixed timestamp support for partition expressions, enabling better partitioning for time-series data.

See the Apache DataFusion 50.0.3 Release for more details.

DuckDB v1.4.2 Upgrade and Accelerator Improvements​

DuckDB v1.4.2: DuckDB has been upgraded to v1.4.2, which includes several performance optimizations.

Composite ART Index Support: DuckDB in Spice now supports composite (multi-column) Adaptive Radix Tree (ART) indexes for accelerated table scans. When queries filter on multiple columns fully covered by a composite index, the optimizer automatically uses index scans instead of full table scans, delivering significant performance improvements for selective queries.

Example configuration:

datasets:
- from: file://data.parquet
name: sales
acceleration:
enabled: true
engine: duckdb
indexes:
'(region, product_id)': enabled

Performance example with composite index on 7.5M rows:

SELECT * FROM sales WHERE region = 'US' AND product_id = 12345;

-- Without index: 0.282s
-- With composite index (region, product_id): 0.037s
-- Performance improvement: 7.6x faster with composite index

DuckDB Intermediate Materialization: Queries with indexes now use intermediate materialization (WITH ... AS MATERIALIZED) to leverage faster index scans. Currently supported for non-federated queries (query_federation: disabled) against a single table with indexes only. When predicates cover more columns than the index, the optimizer rewrites queries to first materialize index-filtered results, then apply remaining predicates. This optimization can deliver significant performance improvements for selective queries.

Example configuration:

datasets:
- from: file://sales_data.parquet
name: sales
acceleration:
enabled: true
engine: duckdb
mode: file
params:
query_federation: disabled # Required currently for intermediate materialization
indexes:
'(region, product_id)': enabled

Performance example:

-- Query with indexed columns (region, product_id) plus additional filter (amount)
SELECT * FROM sales
WHERE region = 'US' AND product_id = 12345 AND amount > 1000;

-- Optimized execution time: 0.031s (with intermediate materialization)
-- Standard execution time: 0.108s (without optimization)
-- Performance improvement: ~3.5x faster

The optimizer automatically rewrites the query to:

WITH _intermediate_materialize AS MATERIALIZED (
SELECT * FROM sales WHERE region = 'US' AND product_id = 12345
)
SELECT * FROM _intermediate_materialize WHERE amount > 1000;

Parquet Buffering for Partitioned Writes: DuckDB partitioned writes in table mode now support Parquet buffering, reducing memory usage and improving write performance for large datasets.

Retention SQL on Refresh Commit: DuckDB accelerations now support running retention SQL on refresh commit, enabling automatic data cleanup and lifecycle management during refresh operations.

UTC Timezone for DuckDB: DuckDB now uses UTC as the default timezone, ensuring consistent behavior for time-based queries across different environments.

Example Spicepod.yml configuration:

datasets:
- from: s3://my_bucket/large_table/
name: partitioned_data
acceleration:
enabled: true
engine: duckdb
mode: file
retention:
sql: DELETE FROM partitioned_data WHERE event_time < NOW() - INTERVAL '7 days'

HTTP Data Connector​

  • Querying endpoints as tables: The HTTP/HTTPS Data Connectors now supports querying HTTP endpoints directly as tables in SQL queries with dynamic filters. This feature transforms REST APIs into queryable data sources, making it easy to integrate external service data.

  • Query HTTP endpoint that returns structured data (JSON, CSV, etc.) as if it were a database table

  • Configurable retry logic, timeouts, and POST request support for more complex API interactions

Example Spicepod.yml configuration:

datasets:
- from: https://api.tvmaze.com
name: tvmaze
params:
file_format: json
max_retries: 3
client_timeout: 10s

Example SQL query:

SELECT request_path, request_query, content
FROM tvmaze
WHERE request_path = '/search/people' and request_query = 'q=michael'
LIMIT 10;

If a request_body is supplied it will be posted to the endpoint:

Example SQL query:

SELECT request_path, request_query, content
FROM tvmaze
WHERE request_path = '/search/people' and request_query = 'q=michael' and request_body = '{"name": "michael"}'
LIMIT 10;

HTTP endpoints can be accelerated using refresh_sql:

datasets:
- from: https://api.tvmaze.com
name: tvmaze
acceleration:
enabled: true
refresh_mode: full
refresh_sql: |
SELECT request_path, request_query, content
FROM tvmaze
WHERE request_path = '/search/people'
AND request_query IN ('q=michael', 'q=luke')

DynamoDB Data Connector Improvements​

Improved Query Performance: The DynamoDB Data Connector now includes improved filter handling for edge cases, parallel scan support for faster data ingestion, and better error handling for misconfigured queries. These improvements enable more reliable and performant access to DynamoDB data.

Example Spicepod.yml configuration:

datasets:
- from: dynamodb:my_table
name: ddb_data
params:
scan_segments: 10 # Default `auto` which calculates optimal segments based on number of rows

S3 Versioning Support​

Atomic Range Reads for Versioned Files: Spice now supports S3 Versioning for all connectors using object-store (S3, Delta Lake, etc.), ensuring range reads over versioned files are atomically correct. When S3 versioning is enabled, Spice automatically tracks version IDs during file discovery and uses them for all subsequent range reads, preventing inconsistencies from concurrent file modifications.

Current limitations:

  • Multi-file connections (e.g., partitioned datasets) do not yet support version tracking across all files
  • Version tracking is automatic when S3 versioning is enabled on the bucket

Search & Embeddings Enhancements​

Full-Text Search on Views: Full-text search indexes are now supported on views, enabling advanced search scenarios over pre-aggregated or transformed data. This extends the power of Spice's search capabilities beyond base datasets.

Multi-Column Embeddings on Views: Views now support embedding columns, enabling vector search and semantic retrieval on view data. This is useful for search over aggregated or joined datasets.

Vector Engines on Views: Vector search engines are now available for views, enabling similarity search over complex queries and transformations.

Example Spicepod.yml configuration:

views:
- name: aggregated_reviews
sql: SELECT review_id, review_text FROM reviews WHERE rating > 4
embeddings:
- column: review_text
model: openai:text-embedding-3-small

Dedicated Query Thread Pool (Now Enabled by Default)​

Dedicated Query Thread Pool: Query execution and accelerated refreshes now run on their own dedicated thread pool, separate from the HTTP server. This prevents heavy query workloads from slowing down API responses, keeping health checks fast and avoiding unnecessary Kubernetes pod restarts under load.

This feature was opt-in in previous releases and is now enabled by default. To disable it and revert to the previous behavior, add the following spicepod.yaml configuration:

runtime:
params:
dedicated_thread_pool: none

Query Performance Optimizations​

Stale-While-Revalidate Cache Control: Query results now support "stale-while-revalidate" cache control, allowing stale cached data to be served immediately while asynchronously refreshing the cache entry in the background. This improves response times for frequently-accessed queries while maintaining data freshness. Requires cache key type to be set to "sql (raw)" for proper operation.

Optimized Prepared Statements: Prepared statement handling has been optimized for better performance with parameterized queries, reducing planning overhead and improving execution time for repeated queries.

Large RecordBatch Chunking: Large Arrow RecordBatch objects are now automatically chunked to control memory usage during query execution, preventing memory exhaustion for queries returning large result sets.

Query Result Cache: Stale-While-Revalidate​

HTTP Cache-Control Support: The query result cache now supports the stale-while-revalidate Cache-Control directive, enabling faster response times by serving stale cached results immediately while asynchronously refreshing the cache in the background. This feature is particularly useful for applications that can tolerate slightly stale data in exchange for improved performance.

How it works:

When a cache entry is stale but within the stale-while-revalidate window, Spice will:

  1. Immediately return the stale cached result to the client
  2. Asynchronously re-execute the query in the background to refresh the cache
  3. Future requests will use the refreshed data

Configuration:

Use the Cache-Control HTTP header with the stale-while-revalidate directive:

Cache-Control: max-age=300, stale-while-revalidate=60

This configuration caches results for 5 minutes (300 seconds), and allows serving stale results for an additional 60 seconds while refreshing in the background.

Requirements:

  • Must use plan or raw SQL cache keys (set cache_key_type to sql or plan in results_caching configuration)
  • Background revalidation re-executes queries through the normal query path
  • Timestamp tracking automatically determines cache entry age for staleness checks

Example configuration via HTTP header:

GET /v1/sql
Cache-Control: max-age=600, stale-while-revalidate=120
X-Cache-Key-Type: sql

This feature improves application responsiveness while ensuring data freshness through background updates.

Security & Reliability Improvements​

Enhanced HTTP Client Security: HTTP client usage across the runtime has been hardened with improved TLS validation, certificate pinning for critical endpoints, and better error handling for network failures.

ODBC Connector Improvements: Removed unwrap calls from the ODBC connector, improving error handling and reliability. Fixed secret handling and Kubernetes secret integration.

CLI Permissions Hardening: Tightened file permissions for the CLI and install script, ensuring secure defaults for configuration files and credentials.

Oracle Instant Client Pinning: Oracle Instant Client downloads are now pinned to specific SHAs, ensuring reproducible builds and preventing supply chain attacks.

AWS Authentication Improvements​

Improved Credential Retry Logic: AWS SDK credential initialization has been significantly improved with more robust retry logic and better error handling. The system now automatically retries transient credential resolution failures using Fibonacci backoff, allowing Spice to tolerate extended AWS outages (up to ~48 hours) without manual intervention.

Key features:

  • Automatic retry with backoff: Implements Fibonacci backoff for transient credential failures (network issues, temporary AWS service disruptions)
  • Configurable retry limits: Supports up to 300 retry attempts with a maximum retry interval of 600 seconds
  • Better error handling: Distinguishes between retryable errors (connector errors) and non-retryable errors (misconfiguration)
  • Unauthenticated access support: Properly supports unauthenticated access to public S3 buckets without requiring credentials
  • Improved error messages: Provides detailed logging with attempt numbers, retry intervals, and error context for better troubleshooting

The improvements ensure more reliable AWS service integration, particularly in environments with intermittent network connectivity or during AWS service degradations.

Observability & Tracing​

DataFusion Log Emission: The Spice runtime now emits DataFusion internal logs, providing deeper visibility into query planning and execution for debugging and performance analysis.

AI Completions Tracing: Fixed tracing so that ai_completions operations are correctly parented under sql_query traces, improving observability for AI-powered queries.

Git Data Connector (Alpha)​

Version-Controlled Data Access: The new Git Data Connector (Alpha) enables querying datasets stored in Git repositories. This connector is ideal for use cases involving configuration files, documentation, or any data tracked in version control.

Example Spicepod.yml configuration:

datasets:
- from: git:https://github.com/myorg/myrepo
name: git_metrics
params:
file_format: csv

For more details, refer to the Git Data Connector Documentation.

Spice Java SDK 0.4.0​

The Spice Java SDK have been upgraded with support configurable Arrow memory limit: spice-java v0.4.0

SpiceClient client = SpiceClient.builder()
.withArrowMemoryLimitMB(1024) // 1GB limit
.build();

CLI Improvements​

Install Specific Versions: The spice install command now supports installing specific versions of the Spice runtime and CLI. This enables easy version management, downgrading, or installation of specific releases for testing or compatibility requirements.

Usage:

# Install a specific version
spice install v1.8.3

# Install a specific version with AI flavor
spice install v1.8.3 ai

# Install latest version (existing behavior)
spice install
spice install ai

Note: Homebrew installations require manual version management via brew install spiceai/spiceai/spice@<version>.

Persistent Query History: The Spice CLI REPL (SQL, search, and chat interfaces) now persists command history to ~/.spice/query_history.txt, making your query history available across sessions. The history file is automatically created if it doesn't exist, with graceful fallback if the home directory cannot be determined.

New REPL Commands:

  • .clear - Clear the screen using ANSI escape codes for a clean workspace
  • .clear history - Clear and persist the query history, removing all stored commands

Tab Completion: Tab completion now includes suggestions based on your command history, making it faster to re-run or modify previous queries.

Example usage:

sql> SELECT * FROM my_table;
sql> .clear # Clears the screen
sql> .clear history # Clears command history
sql> # Use arrow keys or tab to access previous commands

Additional Improvements & Bug Fixes​

  • Reliability: Fixed refresh worker panics with recovery handling to prevent runtime crashes during acceleration refreshes.
  • Reliability: Improved error messages for missing or invalid spicepod.yaml files, providing actionable feedback for misconfiguration.
  • Reliability: Fixed DuckDB metadata pointer loading issues for snapshots.
  • Performance: Ensured ListingTable partitions are pruned correctly when filters are not used.
  • Reliability: Fixed vector dimension determination for partitioned indexes.
  • Search: Fixed casing issues in Reciprocal Rank Fusion (RRF) for hybrid search queries.
  • Search: Fixed search field handling as metadata for chunked search indexes.
  • Validation: Added timestamp support for partition expressions.
  • Validation: Fixed regexp_match function for DuckDB datasets.
  • Validation: Fixed partition name validation for improved reliability.

Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

New HTTP Data Connector Recipe: New recipe demonstrating how to query REST APIs and HTTP(s) endpoints. See HTTP Connector Recipe for details.

The Spice Cookbook includes 82 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.9.0-rc.4, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.9.0-rc.4 image:

docker pull spiceai/spiceai:1.9.0-rc.4

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

πŸŽ‰ Spice is now available in the AWS Marketplace!

What's Changed​

Dependencies​

Changelog (rc.4)​

Spice v1.9.0-rc.2 (Nov 11, 2025)

Β· 32 min read
Sergei Grebnov
Member of Technical Staff at Spice AI

Announcing the release of Spice v1.9.0-rc.2! 🌢

This is the second release candidate for v1.9.0, which introduces Spice Cayenne, a new high-performance data accelerator built on the Vortex columnar format that delivers better than DuckDB performance without single-file scaling limitations and a preview of Multi-Node Distributed Query based on Apache Ballista. v1.9.0-rc.2 also upgrades to DataFusion v50 and DuckDB v1.4.1 for even higher query performance, expands search capabilities with full-text search on views and multi-column embeddings, includes significant DynamoDB and DuckDB accelerator improvements, expands the HTTP data connector to support endpoints as tables, and delivers many security and reliability improvements.

What's New in v1.9.0-rc.2​

Cayenne Data Accelerator (Beta)​

Introducing Cayenne: SQL as an Acceleration Format: A new high-performance Data Accelerator that simplifies multi-file data acceleration by using an embedded database (SQLite) for metadata while storing data in the Vortex columnar format, a Linux Foundation project. Cayenne delivers query and ingestion performance better than DuckDB's file-based acceleration without DuckDB's memory overhead and the scaling challenges of single DuckDB files.

Cayenne uses SQLite to manage acceleration metadata (schemas, snapshots, statistics, file tracking) through simple SQL transactions, while storing data in Vortex's compressed columnar format. This architecture provides:

Key Features:

  • SQLite + Vortex Architecture: All metadata is stored in SQLite tables with standard SQL transactions, while data lives in Vortex's compressed, chunked columnar format designed for zero-copy access and efficient scanning.
  • Simplified Operations: No complex file hierarchies, no JSON/Avro metadata files, no separate catalog serversβ€”just SQL tables and Vortex data files. The entire metadata schema is intentionally simple for maximum reliability.
  • Fast Metadata Access: Single SQL query retrieves all metadata needed for query planningβ€”no multiple round trips to storage, no S3 throttling, no reconstruction of metadata state from scattered files.
  • Efficient Small Changes: Dramatically reduces small file proliferation. Snapshots are just rows in SQLite tables, not new files on disk. Supports millions of snapshots without performance degradation.
  • High Concurrency: Changes consist of two steps: stage Vortex files (if any), then run a single SQL transaction. Much faster conflict resolution and support for many more concurrent updates than file-based formats.
  • Advanced Data Lifecycle: Full ACID transactions, delete support, and retention SQL execution on refresh commit.

Example Spicepod.yml configuration:

datasets:
- from: s3:my_table
name: accelerated_data_30d
acceleration:
enabled: true
engine: cayenne
mode: file
refresh_mode: append
retention_sql: DELETE FROM accelerated_data WHERE created_at < NOW() - INTERVAL '30 days'

Note, the Cayenne Data Accelerator is in Beta with limitations.

For more details, refer to the Cayenne Documentation, the Vortex project, and the DuckLake announcement that partly inspired this design.

Multi-Node Distributed Query (Preview)​

Apache Ballista Integration: Spice now supports distributed query execution based on Apache Ballista, enabling distributed queries across multiple executor nodes for improved performance on large datasets. This feature is in preview in v1.9.0-rc.2.

Architecture:

A distributed Spice cluster consists of:

  • Scheduler: Responsible for distributed query planning and work queue management for the executor fleet
  • Executors: One or more nodes responsible for running physical query plans

Getting Started:

Start a scheduler instance using an existing Spicepod. The scheduler is the only spiced instance that needs to be configured:

# Start scheduler (note the flight bind address override if you want it reachable outside localhost)
spiced --cluster-mode scheduler --flight 0.0.0.0:50051

Start one or more executors configured with the scheduler's flight URI:

# Start executor (automatically selects a free port if 50051 is taken)
spiced --cluster-mode executor --scheduler-url spiced://localhost:50051

Query Execution:

Queries run through the scheduler will now show a distributed_plan in EXPLAIN output, demonstrating how the query is distributed across executor nodes:

EXPLAIN SELECT count(id) FROM my_dataset;

Current Limitations:

  • Accelerated datasets are currently not supported. This feature is designed for querying partitioned data lake formats (Parquet, Delta Lake, Iceberg, etc.)
  • The feature is in preview and may have stability or performance limitations
  • Specific acceleration support is planned for future releases

DataFusion v50 Upgrade​

Spice.ai is built on the Apache DataFusion query engine. The v50 release brings significant performance improvements and enhanced reliability:

Performance Improvements πŸš€:

  • Dynamic Filter Pushdown: Enhanced dynamic filter pushdown for custom ExecutionPlans, ensuring filters propagate correctly through all physical operators for improved query performance.

  • Partition Pruning: Expanded partition pruning support ensures that unnecessary partitions are skipped when filters are not used, reducing data scanning overhead and improving query execution times.

Apache Spark Compatible Functions: Added support for Spark-compatible functions including array, bit_get/bit_count, bitmap_count, crc32/sha1, date_add/date_sub, if, last_day, like/ilike, luhn_check, mod/pmod, next_day, parse_url, rint, and width_bucket.

Bug Fixes & Reliability: Resolved issues with partition name validation and empty execution plans when vector index lists are empty. Fixed timestamp support for partition expressions, enabling better partitioning for time-series data.

See the Apache DataFusion 50.0.0 Release for more details.

DuckDB v1.4.1 Upgrade and Accelerator Improvements​

DuckDB v1.4.1: DuckDB has been upgraded to v1.4.1, which includes several performance optimizations.

Composite ART Index Support: DuckDB in Spice now supports composite (multi-column) Adaptive Radix Tree (ART) indexes for accelerated table scans. When queries filter on multiple columns fully covered by a composite index, the optimizer automatically uses index scans instead of full table scans, delivering significant performance improvements for selective queries.

Example configuration:

datasets:
- from: file://data.parquet
name: sales
acceleration:
enabled: true
engine: duckdb
indexes:
'(region, product_id)': enabled

Performance example with composite index on 7.5M rows:

SELECT * FROM sales WHERE region = 'US' AND product_id = 12345;

-- Without index: 0.282s
-- With composite index (region, product_id): 0.037s
-- Performance improvement: 7.6x faster with composite index

DuckDB Intermediate Materialization: Queries with indexes now use intermediate materialization (WITH ... AS MATERIALIZED) to leverage faster index scans. Currently supported for non-federated queries (query_federation: disabled) against a single table with indexes only. When predicates cover more columns than the index, the optimizer rewrites queries to first materialize index-filtered results, then apply remaining predicates. This optimization can deliver significant performance improvements for selective queries.

Example configuration:

datasets:
- from: file://sales_data.parquet
name: sales
acceleration:
enabled: true
engine: duckdb
mode: file
params:
query_federation: disabled # Required currently for intermediate materialization
indexes:
'(region, product_id)': enabled

Performance example:

-- Query with indexed columns (region, product_id) plus additional filter (amount)
SELECT * FROM sales
WHERE region = 'US' AND product_id = 12345 AND amount > 1000;

-- Optimized execution time: 0.031s (with intermediate materialization)
-- Standard execution time: 0.108s (without optimization)
-- Performance improvement: ~3.5x faster

The optimizer automatically rewrites the query to:

WITH _intermediate_materialize AS MATERIALIZED (
SELECT * FROM sales WHERE region = 'US' AND product_id = 12345
)
SELECT * FROM _intermediate_materialize WHERE amount > 1000;

Parquet Buffering for Partitioned Writes: DuckDB partitioned writes in table mode now support Parquet buffering, reducing memory usage and improving write performance for large datasets.

Retention SQL on Refresh Commit: DuckDB accelerations now support running retention SQL on refresh commit, enabling automatic data cleanup and lifecycle management during refresh operations.

UTC Timezone for DuckDB: DuckDB now uses UTC as the default timezone, ensuring consistent behavior for time-based queries across different environments.

Example Spicepod.yml configuration:

datasets:
- from: s3://my_bucket/large_table/
name: partitioned_data
acceleration:
enabled: true
engine: duckdb
mode: file
retention:
sql: DELETE FROM partitioned_data WHERE event_time < NOW() - INTERVAL '7 days'

HTTP Data Connector​

  • Querying endpoints as tables: The HTTP/HTTPS Data Connectors now supports querying HTTP endpoints directly as tables in SQL queries with dynamic filters. This feature transforms REST APIs into queryable data sources, making it easy to integrate external service data.

  • Query HTTP endpoint that returns structured data (JSON, CSV, etc.) as if it were a database table

  • Configurable retry logic, timeouts, and POST request support for more complex API interactions

Example Spicepod.yml configuration:

datasets:
- from: https://api.tvmaze.com
name: tvmaze
params:
file_format: json
max_retries: 3
client_timeout: 10s

Example SQL query:

SELECT request_path, request_query, content
FROM tvmaze
WHERE request_path = '/search/people' and request_query = 'q=michael'
LIMIT 10;

If a request_body is supplied it will be posted to the endpoint:

Example SQL query:

SELECT request_path, request_query, content
FROM tvmaze
WHERE request_path = '/search/people' and request_query = 'q=michael' and request_body = '{"name": "michael"}'
LIMIT 10;

HTTP endpoints can be accelerated using refresh_sql:

datasets:
- from: https://api.tvmaze.com
name: tvmaze
acceleration:
enabled: true
refresh_mode: full
refresh_sql: |
SELECT request_path, request_query, content
FROM tvmaze
request_path = '/search/people'
AND request_query IN ('q=michael', 'q=luke')

DynamoDB Data Connector Improvements​

Improved Query Performance: The DynamoDB Data Connector now includes improved filter handling for edge cases, parallel scan support for faster data ingestion, and better error handling for misconfigured queries. These improvements enable more reliable and performant access to DynamoDB data.

Example Spicepod.yml configuration:

datasets:
- from: dynamodb:my_table
name: ddb_data
params:
scan_segments: 10 # Default `auto` which calculates optimal segments based on number of rows

S3 Versioning Support​

Atomic Range Reads for Versioned Files: Spice now supports S3 Versioning for all connectors using object-store (S3, Delta Lake, etc.), ensuring range reads over versioned files are atomically correct. When S3 versioning is enabled, Spice automatically tracks version IDs during file discovery and uses them for all subsequent range reads, preventing inconsistencies from concurrent file modifications.

Current limitations:

  • Multi-file connections (e.g., partitioned datasets) do not yet support version tracking across all files
  • Version tracking is automatic when S3 versioning is enabled on the bucket

Search & Embeddings Enhancements​

Full-Text Search on Views: Full-text search indexes are now supported on views, enabling advanced search scenarios over pre-aggregated or transformed data. This extends the power of Spice's search capabilities beyond base datasets.

Multi-Column Embeddings on Views: Views now support embedding columns, enabling vector search and semantic retrieval on view data. This is useful for search over aggregated or joined datasets.

Vector Engines on Views: Vector search engines are now available for views, enabling similarity search over complex queries and transformations.

Example Spicepod.yml configuration:

views:
- name: aggregated_reviews
sql: SELECT review_id, review_text FROM reviews WHERE rating > 4
embeddings:
- column: review_text
model: openai:text-embedding-3-small

Dedicated Query Thread Pool (Now Enabled by Default)​

Dedicated Query Thread Pool: Query execution and accelerated refreshes now run on their own dedicated thread pool, separate from the HTTP server. This prevents heavy query workloads from slowing down API responses, keeping health checks fast and avoiding unnecessary Kubernetes pod restarts under load.

This feature was opt-in in previous releases and is now enabled by default in v1.9.0-rc.2. To disable it and revert to the previous behavior, add the following spicepod.yaml configuration:

runtime:
params:
dedicated_thread_pool: none

Query Performance Optimizations​

Stale-While-Revalidate Cache Control: Query results now support "stale-while-revalidate" cache control, allowing stale cached data to be served immediately while asynchronously refreshing the cache entry in the background. This improves response times for frequently-accessed queries while maintaining data freshness. Requires cache key type to be set to "sql (raw)" for proper operation.

Optimized Prepared Statements: Prepared statement handling has been optimized for better performance with parameterized queries, reducing planning overhead and improving execution time for repeated queries.

Large RecordBatch Chunking: Large Arrow RecordBatch objects are now automatically chunked to control memory usage during query execution, preventing memory exhaustion for queries returning large result sets.

Query Result Cache: Stale-While-Revalidate​

HTTP Cache-Control Support: The query result cache now supports the stale-while-revalidate Cache-Control directive, enabling faster response times by serving stale cached results immediately while asynchronously refreshing the cache in the background. This feature is particularly useful for applications that can tolerate slightly stale data in exchange for improved performance.

How it works:

When a cache entry is stale but within the stale-while-revalidate window, Spice will:

  1. Immediately return the stale cached result to the client
  2. Asynchronously re-execute the query in the background to refresh the cache
  3. Future requests will use the refreshed data

Configuration:

Use the Cache-Control HTTP header with the stale-while-revalidate directive:

Cache-Control: max-age=300, stale-while-revalidate=60

This configuration caches results for 5 minutes (300 seconds), and allows serving stale results for an additional 60 seconds while refreshing in the background.

Requirements:

  • Must use plan or raw SQL cache keys (set cache_key_type to sql or plan in results_caching configuration)
  • Background revalidation re-executes queries through the normal query path
  • Timestamp tracking automatically determines cache entry age for staleness checks

Example configuration via HTTP header:

GET /v1/sql
Cache-Control: max-age=600, stale-while-revalidate=120
X-Cache-Key-Type: sql

This feature improves application responsiveness while ensuring data freshness through background updates.

Security & Reliability Improvements​

Enhanced HTTP Client Security: HTTP client usage across the runtime has been hardened with improved TLS validation, certificate pinning for critical endpoints, and better error handling for network failures.

ODBC Connector Improvements: Removed unwrap calls from the ODBC connector, improving error handling and reliability. Fixed secret handling and Kubernetes secret integration.

CLI Permissions Hardening: Tightened file permissions for the CLI and install script, ensuring secure defaults for configuration files and credentials.

Oracle Instant Client Pinning: Oracle Instant Client downloads are now pinned to specific SHAs, ensuring reproducible builds and preventing supply chain attacks.

AWS Authentication Improvements​

Improved Credential Retry Logic: AWS SDK credential initialization has been significantly improved with more robust retry logic and better error handling. The system now automatically retries transient credential resolution failures using Fibonacci backoff, allowing Spice to tolerate extended AWS outages (up to ~48 hours) without manual intervention.

Key features:

  • Automatic retry with backoff: Implements Fibonacci backoff for transient credential failures (network issues, temporary AWS service disruptions)
  • Configurable retry limits: Supports up to 300 retry attempts with a maximum retry interval of 600 seconds
  • Better error handling: Distinguishes between retryable errors (connector errors) and non-retryable errors (misconfiguration)
  • Unauthenticated access support: Properly supports unauthenticated access to public S3 buckets without requiring credentials
  • Improved error messages: Provides detailed logging with attempt numbers, retry intervals, and error context for better troubleshooting

The improvements ensure more reliable AWS service integration, particularly in environments with intermittent network connectivity or during AWS service degradations.

Observability & Tracing​

DataFusion Log Emission: The Spice runtime now emits DataFusion internal logs, providing deeper visibility into query planning and execution for debugging and performance analysis.

AI Completions Tracing: Fixed tracing so that ai_completions operations are correctly parented under sql_query traces, improving observability for AI-powered queries.

Git Data Connector (Alpha)​

Version-Controlled Data Access: The new Git Data Connector (Alpha) enables querying datasets stored in Git repositories. This connector is ideal for use cases involving configuration files, documentation, or any data tracked in version control.

Example Spicepod.yml configuration:

datasets:
- from: git:https://github.com/myorg/myrepo
name: git_metrics
params:
file_format: csv

For more details, refer to the Git Data Connector Documentation.

Spice Java SDK 0.4.0​

The Spice Java SDK have been upgraded with support configurable Arrow memory limit: spice-java v0.4.0

SpiceClient client = SpiceClient.builder()
.withArrowMemoryLimitMB(1024) // 1GB limit
.build();

CLI Improvements​

Install Specific Versions: The spice install command now supports installing specific versions of the Spice runtime and CLI. This enables easy version management, downgrading, or installation of specific releases for testing or compatibility requirements.

Usage:

# Install a specific version
spice install v1.8.3

# Install a specific version with AI flavor
spice install v1.8.3 ai

# Install latest version (existing behavior)
spice install
spice install ai

Note: Homebrew installations require manual version management via brew install spiceai/spiceai/spice@<version>.

Persistent Query History: The Spice CLI REPL (SQL, search, and chat interfaces) now persists command history to ~/.spice/query_history.txt, making your query history available across sessions. The history file is automatically created if it doesn't exist, with graceful fallback if the home directory cannot be determined.

New REPL Commands:

  • .clear - Clear the screen using ANSI escape codes for a clean workspace
  • .clear history - Clear and persist the query history, removing all stored commands

Tab Completion: Tab completion now includes suggestions based on your command history, making it faster to re-run or modify previous queries.

Example usage:

sql> SELECT * FROM my_table;
sql> .clear # Clears the screen
sql> .clear history # Clears command history
sql> # Use arrow keys or tab to access previous commands

Additional Improvements & Bug Fixes​

  • Reliability: Fixed refresh worker panics with recovery handling to prevent runtime crashes during acceleration refreshes.
  • Reliability: Improved error messages for missing or invalid spicepod.yaml files, providing actionable feedback for misconfiguration.
  • Reliability: Fixed DuckDB metadata pointer loading issues for snapshots.
  • Performance: Ensured ListingTable partitions are pruned correctly when filters are not used.
  • Reliability: Fixed vector dimension determination for partitioned indexes.
  • Search: Fixed casing issues in Reciprocal Rank Fusion (RRF) for hybrid search queries.
  • Search: Fixed search field handling as metadata for chunked search indexes.
  • Validation: Added timestamp support for partition expressions.
  • Validation: Fixed regexp_match function for DuckDB datasets.
  • Validation: Fixed partition name validation for improved reliability.

Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

New HTTP Data Connector Recipe: New recipe demonstrating how to query REST APIs and HTTP(s) endpoints. See HTTP Connector Recipe for details.

The Spice Cookbook includes 82 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.9.0-rc.2, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.9.0-rc.2 image:

docker pull spiceai/spiceai:1.9.0-rc.2

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

πŸŽ‰ Spice is now available in the AWS Marketplace!

What's Changed​

Dependencies​

Changelog​

Spice v1.9.0-rc.1 (Nov 4, 2025)

Β· 16 min read
William Croxson
Member of Technical Staff at Spice AI

This is the first release candidate for v1.9.0, which introduces Cayenne, a new high-performance data accelerator built on the Vortex columnar format that delivers DuckDB-comparable performance without scaling limitations. This release also upgrades to DataFusion v50 for improved query performance, expands search capabilities with full-text search on views and multi-column embeddings, includes significant DynamoDB and DuckDB accelerator improvements, and delivers security and reliability enhancements.

What's New in v1.9.0-rc.1​

Cayenne Data Accelerator (Alpha)​

Introducing Cayenne: SQL as an Acceleration Format: A new high-performance data accelerator that simplifies multi-file data acceleration by using an embedded database (SQLite) for metadata while storing data in the Vortex columnar format. Cayenne delivers query and ingestion performance comparable or better to DuckDB's file-based acceleration without DuckDB's memory overhead and the scaling challenges of single DuckDB files.

Cayenne uses SQLite to manage acceleration metadata (schemas, snapshots, statistics, file tracking) through simple SQL transactions, while storing actual data in Vortex's compressed columnar format. This architecture provides:

Key Features:

  • SQLite + Vortex Architecture: All metadata is stored in SQLite tables with standard SQL transactions, while data lives in Vortex's compressed, chunked columnar format designed for zero-copy access and efficient scanning.
  • Simplified Operations: No complex file hierarchies, no JSON/Avro metadata files, no separate catalog serversβ€”just SQL tables and Vortex data files. The entire metadata schema is intentionally simple for maximum reliability.
  • Fast Metadata Access: Single SQL query retrieves all metadata needed for query planningβ€”no multiple round trips to storage, no S3 throttling, no reconstruction of metadata state from scattered files.
  • Efficient Small Changes: Dramatically reduces small file proliferation. Snapshots are just rows in SQLite tables, not new files on disk. Supports millions of snapshots without performance degradation.
  • High Concurrency: Changes consist of two steps: stage Vortex files (if any), then run a single SQL transaction. Much faster conflict resolution and support for many more concurrent updates than file-based formats.
  • Advanced Data Lifecycle: Full ACID transactions, delete support, and retention SQL execution on refresh commit.

Example Spicepod.yml configuration:

datasets:
- from: s3:my_table
name: accelerated_data
acceleration:
enabled: true
engine: cayenne
retention:
sql: DELETE FROM accelerated_data WHERE created_at < NOW() - INTERVAL '30 days'

Note, the Cayenne Data Accelerator is in Alpha with limitations.

For more details, refer to the Cayenne Documentation, the Vortex project, and the DuckLake announcement that partly inspired this design.

DataFusion v50 Upgrade​

Spice.ai is built on the DataFusion query engine. The v50 release brings significant performance improvements and enhanced reliability:

Performance Improvements πŸš€:

  • Dynamic Filter Pushdown: Enhanced dynamic filter pushdown for custom ExecutionPlans, ensuring filters propagate correctly through all physical operators for improved query performance.
  • Partition Pruning: Expanded partition pruning support ensures that unnecessary partitions are skipped when filters are not used, reducing data scanning overhead and improving query execution times.

Bug Fixes & Reliability: Resolved issues with partition name validation and empty execution plans when vector index lists are empty. Fixed timestamp support for partition expressions, enabling better partitioning for time-series data.

See the Apache DataFusion 50.0.0 Release for more details.

DynamoDB Data Connector Improvements​

Improved Query Performance: The DynamoDB Data Connector now includes improved filter handling for edge cases, parallel scan support for faster data ingestion, and better error handling for misconfigured queries. These improvements enable more reliable and performant access to DynamoDB data.

Example Spicepod.yml configuration:

datasets:
- from: dynamodb:my_table
name: ddb_data
params:
scan_segments: 10 # Default `auto` which calculates optimal segments based on number of rows

Search & Embeddings Enhancements​

Full-Text Search on Views: Full-text search indexes are now supported on views, enabling advanced search scenarios over pre-aggregated or transformed data. This extends the power of Spice's search capabilities beyond base datasets.

Multi-Column Embeddings on Views: Views now support embedding columns, enabling vector search and semantic retrieval on view data. This is useful for search over aggregated or joined datasets.

Vector Engines on Views: Vector search engines are now available for views, enabling similarity search over complex queries and transformations.

Example Spicepod.yml configuration:

views:
- name: aggregated_reviews
sql: SELECT review_id, review_text FROM reviews WHERE rating > 4
embeddings:
- column: review_text
model: openai:text-embedding-3-small

DuckDB Accelerator Improvements​

Parquet Buffering for Partitioned Writes: DuckDB partitioned writes in table mode now support Parquet buffering, reducing memory usage and improving write performance for large datasets.

Retention SQL on Refresh Commit: DuckDB accelerations now support running retention SQL on refresh commit, enabling automatic data cleanup and lifecycle management during refresh operations.

UTC Timezone for DuckDB: DuckDB now uses UTC as the default timezone, ensuring consistent behavior for time-based queries across different environments.

Example Spicepod.yml configuration:

datasets:
- from: s3://my_bucket/large_table/
name: partitioned_data
acceleration:
enabled: true
engine: duckdb
mode: file
retention:
sql: DELETE FROM partitioned_data WHERE event_time < NOW() - INTERVAL '7 days'

Query Performance Optimizations​

Optimized Prepared Statements: Prepared statement handling has been optimized for better performance with parameterized queries, reducing planning overhead and improving execution time for repeated queries.

Large RecordBatch Chunking: Large Arrow RecordBatch objects are now automatically chunked to control memory usage during query execution, preventing memory exhaustion for queries returning large result sets.

Security & Reliability Improvements​

Enhanced HTTP Client Security: HTTP client usage across the runtime has been hardened with improved TLS validation, certificate pinning for critical endpoints, and better error handling for network failures.

ODBC Connector Improvements: Removed unwrap calls from the ODBC connector, improving error handling and reliability. Fixed secret handling and Kubernetes secret integration.

CLI Permissions Hardening: Tightened file permissions for the CLI and install script, ensuring secure defaults for configuration files and credentials.

Oracle Instant Client Pinning: Oracle Instant Client downloads are now pinned to specific SHAs, ensuring reproducible builds and preventing supply chain attacks.

Observability & Tracing​

DataFusion Log Emission: The Spice runtime now emits DataFusion internal logs, providing deeper visibility into query planning and execution for debugging and performance analysis.

AI Completions Tracing: Fixed tracing so that ai_completions operations are correctly parented under sql_query traces, improving observability for AI-powered queries.

Git Data Connector (Alpha)​

Version-Controlled Data Access: The new Git Data Connector (Alpha) enables querying datasets stored in Git repositories. This connector is ideal for use cases involving configuration files, documentation, or any data tracked in version control.

Example Spicepod.yml configuration:

datasets:
- from: git:https://github.com/myorg/myrepo
name: git_metrics
params:
file_format: csv

For more details, refer to the Git Data Connector Documentation.

Additional Improvements & Bug Fixes​

  • Reliability: Fixed refresh worker panics with recovery handling to prevent runtime crashes during acceleration refreshes.
  • Reliability: Improved error messages for missing or invalid spicepod.yaml files, providing actionable feedback for misconfiguration.
  • Reliability: Fixed DuckDB metadata pointer loading issues for snapshots.
  • Performance: Ensured ListingTable partitions are pruned correctly when filters are not used.
  • Reliability: Fixed vector dimension determination for partitioned indexes.
  • Search: Fixed casing issues in Reciprocal Rank Fusion (RRF) for hybrid search queries.
  • Search: Fixed search field handling as metadata for chunked search indexes.
  • Validation: Added timestamp support for partition expressions.
  • Validation: Fixed regexp_match function for DuckDB datasets.
  • Validation: Fixed partition name validation for improved reliability.

Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

No major cookbook updates.

The Spice Cookbook includes 81 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.9.0-rc.1, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.9.0-rc.1 image:

docker pull spiceai/spiceai:1.9.0-rc.1

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

πŸŽ‰ Spice is now available in the AWS Marketplace!

What's Changed​

Changelog​

Spice v1.8.3 (Oct 27, 2025)

Β· 5 min read
David Stancu
Member of Technical Staff at Spice AI

Announcing the release of Spice v1.8.3! ⚑

Spice v1.8.3 is a patch release focused on performance, reliability, and observability. This release delivers optimizations for DuckDB acceleration, parameterized queries, and query plans. A new opt-in dedicated thread pool for queries is now in preview.

What's New in v1.8.3​

DuckDB Data Accelerator Improvements​

  • Connection Pool Sizing: The DuckDB accelerator now supports a configurable connection_pool_size parameter, supporting fine-grained control over concurrent query execution. This enables tuning for high-concurrency workloads and improved resource utilization.

Example Spicepod.yaml snippet:

datasets:
- from: postgres:my_table
name: my_table
acceleration:
enabled: true
engine: duckdb
params:
connection_pool_size: 10
  • Automatic Statistics Recomputation: The new on_refresh_recompute_statistics parameter, on by default, triggers automatic ANALYZE execution after refreshes. This keeps DuckDB optimizer statistics up-to-date, ensuring efficient query plans and optimal performance.

Example Spicepod.yaml snippet:

datasets:
- from: postgres:my_table
name: my_table
acceleration:
enabled: true
engine: duckdb
params:
on_refresh_recompute_statistics: disabled # default enabled

Task History SQL Query Plan Capture & Configuration​

Spice now supports automated SQL query plan capture and store (via EXPLAIN or EXPLAIN ANALYZE) in the task history, enabling deeper analysis and debugging of query execution. This feature is configurable, supporting control of which queries are included based on duration thresholds and plan type.

  • New Configuration Options:
    • task_history.captured_plan: Controls which plan is captured (none, explain, or explain analyze). Default none.
    • task_history.min_sql_duration: Minimum query duration before a plan is captured.
    • task_history.min_plan_duration: Minimum plan execution duration before a plan is captured.

Example spicepod.yaml snippet:

runtime:
task_history:
captured_plan: explain analyze
min_sql_duration: 5s
min_plan_duration: 10s

Query plans are captured asynchronously to avoid blocking query execution. The result of the plan is stored in the standard sql_query output in the task history.

Learn more in the Task History Documentation.

Query Performance Optimizations​

  • Optimized Prepared Statements (Parameterized Queries): Prepared statement caching for parameterized SQL queries has been improved, reducing planning overhead for repeated queries with different parameters. This results in faster execution and lower latency for workloads that reuse query structures.

  • Limit Pushdown via BytesProcessedExec: Introduces the BytesProcessedExec physical operator, enabling limit pushdown for large datasets. This optimization reduces the amount of data processed and improves top-k query performance.

Dedicated Query Thread Pool (Opt-In)​

Spice now supports running query execution and accelerated refreshes on a dedicated thread pool, separate from the HTTP server. This prevents heavy query workloads from slowing down API responses, keeping health and readiness checks fast. Opt-In for v1.8.3: This feature is opt-in for this release and will become enabled by default (opt-out) in v1.9.

Example Spicepod.yaml snippet:

runtime:
params:
dedicated_thread_pool: sql_engine # Default: disabled

Validation & Reliability Improvements​

  • Selective Evaluation Scorer Loading: Evaluation scorers are now loaded only when evaluation is explicitly defined, reducing unnecessary initialization and improving startup performance.

  • Improved Error Reporting: Enhanced error messages for misconfigured full-text search (FTS) on datasets and views, providing actionable feedback for configuration issues.

REPL & Usability​

  • Execution Time Display: The Spice REPL now displays query execution time even when queries return no results, improving user feedback and diagnostics.

Contributors​

Breaking Changes​

No breaking changes.

Cookbook Updates​

No major cookbook updates.

The Spice Cookbook includes 81 recipes to help you get started with Spice quickly and easily.

Upgrading​

To upgrade to v1.8.3, use one of the following methods:

CLI:

spice upgrade

Homebrew:

brew upgrade spiceai/spiceai/spice

Docker:

Pull the spiceai/spiceai:1.8.3 image:

docker pull spiceai/spiceai:1.8.3

For available tags, see DockerHub.

Helm:

helm repo update
helm upgrade spiceai spiceai/spiceai

AWS Marketplace:

πŸŽ‰ Spice is now available in the AWS Marketplace!

What's Changed​

Changelog​