Move prepared_filter, prepared_restriction, prepared_rhs types and
prepare_filter() from vector_search/filter.{hh,cc} into new files
cql3/statements/external_search/filter.{hh,cc} under namespace
cql3::statements::external_search.
This eliminates a circular dependency between the cql3 and vector_search
modules: the filter code depends heavily on cql3 types (expressions,
query_options, statement_restrictions) and belongs in the cql3 layer.
This is a follow-up to VECTOR-250 which originally addressed the same
circular dependency but has since regressed.
Migrate rescoring_with_zerovector_query from rescoring_test.cc to pytest
as test_rescoring_with_zerovector_query. Tested with cosine similarity only
because zero vectors produce NaN only for cosine; other functions yield
valid scores.
The test is marked xfail: similarity_cosine now returns NaN for zero vectors
(SCYLLADB-456 fix) and rescoring should filter out NaN scores, yielding an
empty result set.
Semantic change: the test now asserts the desired empty-result behavior
instead of asserting that the query does not throw.
Delete rescoring_test.cc now that all tests have been migrated and remove
its entries from configure.py and test/vector_search/CMakeLists.txt.
Migrate no_nulls_in_rescored_results from rescoring_test.cc to pytest,
renamed to test_filters_invalid_similarity_scores_in_rescored_results.
The test now also inserts a zero-vector row (id=14) to cover the case
introduced when similarity_cosine was changed to return NaN for zero
vectors instead of throwing (SCYLLADB-456). The expected surviving set
of rows is refined per similarity function based on which inputs produce
valid (non-NaN, non-Infinity) similarity scores. Marked xfail because
rescoring does not yet filter rows with invalid scores.
Semantic change: the expected surviving row set is updated per the
behavior described above.
Migrate select_similarity_function_other_than_ann_ordering from
rescoring_test.cc to pytest. The test verifies that similarity scores in
SELECT are computed against the explicitly supplied argument vector rather
than the ANN ordering vector. No semantic change.
Migrate wildcard_select_is_correctly_rescored from rescoring_test.cc to
pytest. The test verifies that SELECT * with rescoring returns rows in
the correct similarity order with correct embedding values, covering a
slightly different processing path from the explicit-column SELECT test.
No semantic change.
Migrate similarity_function_returns_correctly_rescored_results from
rescoring_test.cc to pytest. The test verifies that similarity scores
in the SELECT clause are computed correctly after rescoring, for both
argument orderings of the similarity function. No semantic change.
Introduce shared test infrastructure in test_vector_search_rescoring.py:
EmbeddingRow dataclass, TEST_DATA dict keyed by similarity function name,
ANN_QUERY_VECTOR, reversed_ann_response() helper, and rescoring_test_table()
context manager.
Migrate result_returned_by_vector_store_is_rescored and
f32_quantization_disables_rescoring from rescoring_test.cc. No semantic change.
Migrate oversampling_multiplies_limit_for_vector_store_query and
oversampled_vector_store_results_are_limited_to_cql_limit from
rescoring_test.cc to test_vector_search_rescoring_with_mock.py.
No semantic change.
CREATE INDEX option tests for quantization, oversampling, and rescoring
are moved from rescoring_test.cc to test_vector_index.py alongside the
existing index option tests. These tests exercise only option parsing and
validation - no vector store mock needed. No semantic change.
value_to_json() converts CQL values to JSON for vector search filters.
For decimal and varint types, it used rjson::parse() on the JSON string,
which parses through a double and silently loses precision for values
exceeding ~15 significant digits — producing wrong filter results.
Additionally, for decimal type we need an exact string representation
that preserves the original (unscaled, scale) pair, because partition
keys use byte-level identity: different serialized representations of
the same numeric value are distinct rows, so the filter must reproduce
the exact representation stored in the key.
Add big_decimal::to_string_canonical() which follows the Java BigDecimal
toString() spec (JDK 8+), producing a bijective string representation
that uses exponential notation for extreme scales instead of expanding
trailing zeros (which could cause OOM). This could replace to_string(),
but doing so has wider consequences (e.g. hash/equality contract for
decimal_type) described in SCYLLADB-1574. Use it in value_to_json() for
decimal_type, and use rjson::from_string() for varint_type, both
bypassing the lossy double parse path.
Tests cover the new to_string_canonical() and the filter fix, as well as
existing decimal type behavior (key representation, clustering order,
toJson) that we rely on and must not break. The CQL decimal type tests
(test_type_decimal.py) also pass against Cassandra.
Fixes: https://scylladb.atlassian.net/browse/SCYLLADB-1583
Refs: https://scylladb.atlassian.net/browse/SCYLLADB-1574Closesscylladb/scylladb#29505
The vector store returns for every ANN search, in addition to the keys
of the matching items, two additional vectors - "distances" and
"similarity_cores". The "distances" are raw distance metrics - lower
scores are better matches, while "similarity_scores" are modified
such that higher scores are better matches.
Traditionally, search scores in systems like Cassandra and Open Search
use the "similarity scores" approach (higher is better, results are
returned in decreasing similarity order), so this is the more interesting
vector of the two.
But before this patch, our vector_store_client::ann() inspected
only "distances". But... then, it didn't return even that to the
caller :-)
So in this patch, we:
1. Ignore "distances" and instead look at "similarity scores",
which is what users really want based on their experience with
other vector and non-vector search engines.
2. Return the similarity score of each match together with the match.
We already have this score (the vector store returns it) and we
can add it to the existing primary_key structure of each result.
So each result is a "struct primary_key" which has fields partition,
clustering, and after this patch - similarity.
Existing callers in CQL and Alternator vector search will ignore this
"similarity" field in each result, and not notice it was added.
But in the next patch, we'll allow Alternator's vector search to
return this similarity in each result.
The existing unit tests for vector_store_client.cc mocked vector-store
responses with "distances", without "similarity_scores", so no longer
represent what we actually expect the vector store to do. So this patch
also contains modifications for these tests, to mock and to test
"similarity_scores" - not "distances". The more interesting tests, in
the next patch, use the real vector store and check that we really do
get a "similarity_scores" response from it.
This patch also handles a small corner case for DOT_PRODUCT, which is
the only unbounded similarity function. If the similarity overflows
the 32-bit float, the vector store returns a JSON "null" instead of
a JSON number (since JSON doesn't support infinite numbers). Our
existing vector-store client code errored out when it saw this "null",
which is wrong - the request should be allowed to proceed. So in this
patch when we see a "null" JSON for similarity, we return +Inf.
This is usually correct because the top results really have +Inf, not
-Inf, but if we ask for all items we can reach those with similarity
-Inf and incorrectly assign +Inf to them (we have a test for this case
in the next patch). But this problenm won't happen when Limit is low,
and in any case it's better than aborting the request after it had
already succeeded.
Signed-off-by: Nadav Har'El <nyh@scylladb.com>
Migrate vector search (ANN ordered select query) CQL tests from C++/Boost suite to pytest.
This migration includes:
- New pytest tests in `test/cqlpy/test_vector_search_with_vector_store_mock.py`
- VectorStoreMock server as pytest fixture to simulate vector store responses
The benefits of this migration are:
- Extended test coverage to verify CQL protocol serialization and driver
- Reduced overall test time (no compilation required for pytest)
Fixes SCYLLADB-695
No backport needed as this is a refactoring.
Closesscylladb/scylladb#29593
* github.com:scylladb/scylladb:
vector_search: test: migrate paging warnings tests to Python
vector_search: test: migrate local_vector_index to Python
vector_search: test: migrate vector_index_with_additional_filtering_column to Python
vector_search: test: migrate cql_error_contains_http_error_description to Python
vector_search: test: migrate pk in restriction test to Python
Move the local vector index test from C++ vector_store_client_test to
Python test_vector_search_with_vector_store_mock.
The test creates a local vector index on ((pk1, pk2), embedding) and
verifies that SELECT with partition key restriction and ANN ordering
works correctly.
Move the SCYLLADB-635 regression test from C++ vector_store_client_test
to Python test_vector_search_with_vector_store_mock.
The test creates a vector index on (embedding, ck1) and verifies that
SELECT with ANN ordering works correctly when additional filtering
columns are included in the index definition.
Move the test that verifies HTTP error descriptions from the vector
store are propagated through CQL InvalidRequest messages from the C++
vector_store_client_test to the Python test_vector_search_with_vector_store_mock.
The test configures the mock to return HTTP 404 with 'index does not
exist' and asserts the CQL SELECT raises InvalidRequest containing '404'.
Move vector search (ANN ordered select query) with IN restrictions on
partition key from C++/Boost test suite to pytest (cqlpy).
Add VectorStoreMock server as pytest fixture to simulate vector store
responses.
Replace explicit 1-second timeouts in repeat_until() with the default
STANDARD_WAIT (10s). The 1-second timeout could be too aggressive for
loaded CI environments where lowres_clock granularity (~10ms) combined
with OS scheduling delays and resource contention (-c2 -m2G) could cause
the loop to expire before the DNS refresh task completes its cycle.
This also unifies test timeouts across test cases.
Move trigger_dns_resolver() inside the repeat_until loop instead of
calling it once before the loop.
The test was intermittently timing out on CI. The exact root cause is not
fully understood, but the hypothesis is that a single trigger signal can
be lost somewhere (not exactly known where). This is not an issue for the
production code because refresh trigger will be called multiple times -
in every query where all configured nodes will be unreachable.
By triggering inside the loop, we ensure the signal is re-sent on
each iteration until the resolver actually performs the refresh and
picks up the new (failing) DNS resolution. This makes the test
resilient to timing-dependent signal loss without changing production
code.
Fixes: SCYLLADB-1794
Prevent copying/moving, that can change the address, and instead enforce
using shared_ptr. Most of the code is already using shared_ptr, so the
changes aren't very large.
To forbid non-shared_ptr construction, the constructors are annotated
with a private_tag tag class.
Decrease the default connection timeout to 3s to better align with the
default CQL query timeout of 10s.
The previous timeout allowed only one failover request in high availability
scenario before hitting the CQL query timeout.
By decreasing the timeout to 3s, we can perform up to three failover requests
within the CQL query timeout, which significantly improves the chances of
successfully completing the query in high availability scenarios.
Fixes: SCYLLADB-95
Add option `vector_store_unreachable_node_detection_time_in_ms` to
control parameters related to detecting unreachable vector store nodes.
This parameter is used to set the TCP connect timeout, keepalive
parameters, and TCP_USER_TIMEOUT. By configuring these parameters,
we can detect unreachable vector store nodes faster and trigger
failover mechanisms in a timely manner.
Include non-primary key restrictions (e.g. regular column filters) in
the filter JSON sent to the Vector Store service. Previously only
partition key and clustering column restrictions were forwarded, so
filtering on regular columns was silently ignored.
Add get_nonprimary_key_restrictions() getter to statement_restrictions.
Add unit tests for non-primary key equality, range, and bind marker
restrictions in filter_test.
Fixes: SCYLLADB-970
Closesscylladb/scylladb#29019
Queries against local vector indexes were failing with the error:
```ANN ordering by vector requires the column to be indexed using 'vector_index'```
This was a regression introduced by 15788c3734, which incorrectly
assumed the first column in the targets list is always the vector column.
For local vector indexes, the first column is the partition key, causing
the failure.
Previously, serialization logic for the target index option was shared
between vector and secondary indexes. This is no longer viable due to
the introduction of local vector indexes and vector indexes with filtering
columns, which have different target format.
This commit introduces a dedicated JSON-based serialization format for
vector index targets, identifying the target column (tc), filtering
columns (fc), and partition key columns (pk). This ensures unambiguous
serialization and deserialization for all vector index types.
This change is backward compatible for regular vector indexes. However,
it breaks compatibility for local vector indexes and vector indexes with
filtering columns created in version 2026.1.0. To mitigate this, usage
of these specific index types will be blocked in the 2026.1.0 release
by failing ANN queries against them in vector-store service.
Fixes: SCYLLADB-895
Backport to 2026.1 is required as this issue occurs also on this branch.
Closesscylladb/scylladb#28862
* github.com:scylladb/scylladb:
index: fix DESC INDEX for vector index
vector_search: test: refactor boilerplate setup
vector_search: fix SELECT on local vector index
index: test: vector index target option serialization test
index: test: secondary index target option serialization test
Clang 22 verifies [[nodiscard]] for co_await,
causing compilation failures where return values of expected<> were
silently discarded.
These call sites were discarding the return value of client::request()
and vector_store_client::ann(), both of which return expected<> types
marked [[nodiscard]]. Rather than suppressing the warning with (void)
casts, properly check the return values using the established test
patterns: BOOST_CHECK(result) where the call is expected to succeed,
and BOOST_CHECK(!result) where the call is expected to fail.
Closesscylladb/scylladb#29297
Queries against local vector indexes were failing with the error:
"ANN ordering by vector requires the column to be indexed using 'vector_index'"
This was a regression introduced by 15788c3734, which incorrectly
assumed the first column in the targets list is always the vector column.
For local vector indexes, the first column is the partition key, causing
the failure.
Previously, serialization logic for the target index option was shared
between vector and secondary indexes. This is no longer viable due to
the introduction of local vector indexes and vector indexes with filtering
columns, which have different target format.
This commit introduces a dedicated JSON-based serialization format for
vector index targets, identifying the target column (tc), filtering
columns (fc), and partition key columns (pk). This ensures unambiguous
serialization and deserialization for all vector index types.
This change is backward compatible for regular vector indexes. However,
it breaks compatibility for local vector indexes and vector indexes with
filtering columns created in version 2026.1.0. To mitigate this, usage
of these specific index types will be blocked in the 2026.1.0 release
by failing ANN queries against them in vector-store service.
Fixes: SCYLLADB-895
The test assumes that the sleep duration will be at least the value of
the sleep parameter. However, the actual sleep time can be slightly less
than requested (e.g., a 100ms sleep request might result in a 99ms
sleep).
This commit adjusts the test's time comparison to be more lenient,
preventing test flakiness.
When a `with_connect` operation timed out, the underlying connection
attempt continued to run in the reactor. This could lead to a crash
if the connection was established/rejected after the client object had
already been destroyed. This issue was observed during the teardown
phase of a upcoming high-availability test case.
This commit fixes the race condition by ensuring the connection attempt
is properly canceled on timeout.
Additionally, the explicit TLS handshake previously forced during the
connection is now deferred to the first I/O operation, which is the
default and preferred behavior.
Fixes: SCYLLADB-832
This simple patch adds support for storing the HTTP error description
that Vector Store client receives from vector store. Until now it was
just printed to the log but it was not returned. For this reason it
was not forwarded to the drivers which forced users to access ScyllaDB
server logs to understand what is wrong with Vector Store.
This patch also updates formatter to print the message next to the
error code.
Fixes: VECTOR-189
SNI works only with DNS hostnames. Adding an IP address causes warnings
on the server side.
This change adds SNI only if it is not an IP address.
This change has no unit tests, as this behavior is not critical,
since it causes a warning on the server side.
The critical part, that the server name is verified, is already covered.
This PR also adds warning logs to improve future troubleshooting of connections to the vector-store nodes.
Fixes: VECTOR-528
Backports to 2025.04 and 2026.01 are required, as these branches are also affected.
Closesscylladb/scylladb#28637
* github.com:scylladb/scylladb:
vector_search: fix TLS server name with IP
vector_search: add warn log for failed ann requests
vector_search: small improvements
This PR addresses several minor code quality issues and style inconsistencies within the vector_search module.
No backport is needed as these improvements are not visible to the end user.
Closesscylladb/scylladb#28718
* github.com:scylladb/scylladb:
vector_search: fix names of private members
vector_search: remove unused global variable
When the test fails, the assertion message does not include
the error from the ANN request.
This change enhances the assertion to include the specific ANN error,
making it easier to diagnose test failures.
The default 100ms timeout for client readiness in tests is too
aggressive. In some test environments, this is not enough time for
client creation, which involves address resolution and TLS certificate
reading, leading to flaky tests.
This commit increases the default client creation timeout to 10 seconds.
This makes the tests more robust, especially in slower execution
environments, and prevents similar flakiness in other test cases.
Fixes: VECTOR-547, SCYLLADB-802
The secondary index mechanism is currently used to determine the target column.
This mechanism works incorrectly for vector indexes with filtering because
it returns the last specified column as the target (vectors) column.
However, the syntax for a vector index requires the first column to be the target:
```
CREATE CUSTOM INDEX ON t(vectors, users) USING 'vector_index';
```
This discrepancy eventually leads to the following exception when performing an
ANN search on a vector index with filtering columns:
````
ANN ordering by vector requires the column to be indexed using 'vector_index'
````
This commit fixes the issue by introducing dedicated logic for vector indexes
to correctly identify the target(vectors) column.
Fixes: SCYLLADB-635
Closesscylladb/scylladb#28740
The ANN vector queries with all-zero vectors are allowed even on vector indexes with similarity function set to cosine.
When enabling the rescoring option, those queries would fail as the rescoring calls `similarity_cosine` function underneath, causing an `InvalidRequest` exception as all-zero vectors were not allowed matching Cassandra's behaviour.
To eliminate the discrepancy we want the all-zero vector `similarity_cosine` calls to pass, but return the NaN as the cosine similarity for zero vectors is mathematically incorrect. We decided not to use arbitrary values contrary to USearch, for which the distance (not to be confused with similarity) is defined as cos(0, 0) = 0, cos(0, x) = 1 while supporting the range of values [0, 2].
If we wanted to convert that to similarity, that would mean sim_cos(0, x) = 0.5, which does not support mathematical reasoning why that would be more similar than for example vectors marking obtuse angles.
It's safe to assume that all-zero vectors for cosine similarity shouldn't make any impact, therefore we return NaN and eliminate them from best results.
Adjusted the tests accordingly to check both proper Cassandra and Scylla's behaviour.
Fixes: SCYLLADB-456
Backport to 2026.1 needed, as it fixes the bug for ANN vector queries using rescoring introduced there.
Closesscylladb/scylladb#28609
* github.com:scylladb/scylladb:
test/vector_search: add reproducer for rescoring with zero vectors
vector_search: return NaN for similarity_cosine with all-zero vectors
SNI works only with DNS hostnames. Adding an IP address causes warnings
on the server side.
This change adds SNI only if it is not an IP address.
This change has no unit tests, as this behavior is not critical,
since it causes a warning on the server side.
The critical part, that the server name is verified, is already covered.
Fixes: VECTOR-528
The test is flaky most likely because when TLS certificate rewrite
happens simultaneously with an ANN request, the handshake can hang for a
long time (~60s). This leads to a timeout in the test case.
This change introduces a checkpoint in the test so that it will
wait for the certificate rewrite to happen before sending an ANN request,
which should prevent the handshake from hanging and make the test more reliable.
Fixes: #28012
So far with oversampling the extended set of keys was returned from VS,
but query to the base table was still limited by the query `limit`.
Now for rescoring we want to fetch rows for all the keys returned from VS.
However later we need to restore the command limit, to trim result_set accordingly.
For non-rescoring scenarios we trim directly keys set returned from VS if it happens to exceed query limit.
With this change rescoring validation tests (except `no_nulls_in_rescored_results`) pass fully.
Fixes https://scylladb.atlassian.net/browse/SCYLLADB-83
Our plan for rescoring is to use the existing post-query ordering mechanism to sort (and trim) result_set by similarity column.
For general SELECT case this ordering is permitted only for queries with IN on the partition key and an ORDER BY, which is checked in `needs_post_query_ordering`.
Recently this check was overriden for ANN queries in https://github.com/scylladb/scylladb/pull/28109 to enable IN queries handled by VS without excessive post-processing.
In this patch we revert that change - ANN case will be handled by general check.
However we change the condition - we will enable post processing anytime `_ordering_comparator` is set.
In current implementation `_ordering_comparator` is created only in `select_statement::prepare` with `get_ordering_comparator`,
only for the same conditions as were checked in `needs_post_query_ordering`, so this change should be transparent for general SELECT.
For ANN query it is also not set (yet), so it will not influence ANN filtering, but we confirm that this functionality still works
by adding filtering test: `test/vector_search/filter_test.cc::vector_store_client_test_filtering_ann_cql`.
Rescoring ordering for ANN queries will be enabled when we add `_ordering_comparator` in following patch.
Adding tests for specific cases of rescoring processing:
- wildcard selection - "SELECT * ..." is a case with slightly different path of rescoring processing. We want to confirm that it is handled correctly.
- calculating similarity with other vectors in SELECT clause should not influence ANN ordering.
- NULL handling - results that for any reason have NULL in a score should be filtered out.
As rescoring is not implemented yet, the tests use boost::unit_test::expected_failures
to indicate that the test reports errors.
Verify that vector store results will be correctly rescored and reordered
according to the rescoring algorithm.
As rescoring is not implemented yet, the tests use `boost::unit_test::expected_failures`
to indicate that they report errors.
First test checks rescoring with a simple selection list.
Second makes sure that rescoring is not triggered for quantization=f32 - full representation of vectors.
Third repeats the first one, but adds to it returning of similarity score value.
Add `prepared_filter` class which handles the preparation, construction
and caching of Vector Search filtering compatible JSON object.
If no bind markers found in SELECT statement, the JSON object will be built
once at prepare time and cached for use during execution calls.
Adjust tests accordingly to use prepared filters.
Follow-up: #28109
Fixes: SCYLLADB-299
Move Vector Search filter functions from `cql3::restrictions` to
`vector_search` namespace as it's a better place according to
it's purpose.
The effective name has now changed from `cql3::restrictions::to_json`
to `vector_search::to_json` which clearly mentions that the JSON
object will be used for Vector Search.
Rename the auxilary functions to use `to_json` suffix instead of
variety of verbs as those functions logic focuses on building JSON
object from different structures. The late naming emphasized too
much distinction between those functions, while they do pretty much
the same thing.
Follow-up: #28109
