Based on this comment:
https://github.com/lancedb/lancedb/issues/2228#issuecomment-2730463075
and https://github.com/lancedb/lance/pull/2357
Here is my attempt at implementing bindings for returning merge stats
from a `merge_insert.execute` call for lancedb.
Note: I have almost no idea what I am doing in Rust but tried to follow
existing code patterns and pay attention to compiler hints.
- The change in nodejs binding appeared to be necessary to get
compilation to work, presumably this could actual work properly by
returning some kind of NAPI JS object of the stats data?
- I am unsure of what to do with the remote/table.rs changes -
necessarily for compilation to work; I assume this is related to LanceDB
cloud, but unsure the best way to handle that at this point.
Proof of function:
```python
import pandas as pd
import lancedb
db = lancedb.connect("/tmp/test.db")
test_data = pd.DataFrame(
{
"title": ["Hello", "Test Document", "Example", "Data Sample", "Last One"],
"id": [1, 2, 3, 4, 5],
"content": [
"World",
"This is a test",
"Another example",
"More test data",
"Final entry",
],
}
)
table = db.create_table("documents", data=test_data, exist_ok=True, mode="overwrite")
update_data = pd.DataFrame(
{
"title": [
"Hello, World",
"Test Document, it's good",
"Example",
"Data Sample",
"Last One",
"New One",
],
"id": [1, 2, 3, 4, 5, 6],
"content": [
"World",
"This is a test",
"Another example",
"More test data",
"Final entry",
"New content",
],
}
)
stats = (
table.merge_insert(on="id")
.when_matched_update_all()
.when_not_matched_insert_all()
.execute(update_data)
)
print(stats)
```
returns
```
{'num_inserted_rows': 1, 'num_updated_rows': 5, 'num_deleted_rows': 0}
```
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Summary by CodeRabbit
- **New Features**
- Merge-insert operations now return detailed statistics, including
counts of inserted, updated, and deleted rows.
- **Bug Fixes**
- Tests updated to validate returned merge-insert statistics for
accuracy.
- **Documentation**
- Method documentation improved to reflect new return values and clarify
merge operation results.
- Added documentation for the new `MergeStats` interface detailing
operation statistics.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
* Add a new "table stats" API to expose basic table and fragment
statistics with local and remote table implementations
### Questions
* This is using `calculate_data_stats` to determine total bytes in the
table. This seems like a potentially expensive operation - are there any
concerns about performance for large datasets?
### Notes
* bytes_on_disk seems to be stored at the column level but there does
not seem to be a way to easily calculate total bytes per fragment. This
may need to be added in lance before we can support fragment size
(bytes) statistics.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Added a method to retrieve comprehensive table statistics, including
total rows, index counts, storage size, and detailed fragment size
metrics such as minimum, maximum, mean, and percentiles.
- Enabled fetching of table statistics from remote sources through
asynchronous requests.
- Extended table interfaces across Python, Rust, and Node.js to support
synchronous and asynchronous retrieval of table statistics.
- **Tests**
- Introduced tests to verify the accuracy of the new table statistics
feature for both populated and empty tables.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
add the tag related API to list existing tags, attach tag to a version,
update the tag version, delete tag, get the version of the tag, and
checkout the version that the tag bounded to.
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Introduced table version tagging, allowing users to create, update,
delete, and list human-readable tags for specific table versions.
- Enabled checking out a table by either version number or tag name.
- Added new interfaces for tag management in both Python and Node.js
APIs, supporting synchronous and asynchronous workflows.
- **Bug Fixes**
- None.
- **Documentation**
- Updated documentation to describe the new tagging features, including
usage examples.
- **Tests**
- Added comprehensive tests for tag creation, updating, deletion,
listing, and version checkout by tag in both Python and Node.js
environments.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
* Add new wait_for_index() table operation that polls until indices are
created/fully indexed
* Add an optional wait timeout parameter to all create_index operations
* Python and NodeJS interfaces
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
## Summary by CodeRabbit
- **New Features**
- Added optional waiting for index creation completion with configurable
timeout.
- Introduced methods to poll and wait for indices to be fully built
across sync and async tables.
- Extended index creation APIs to accept a wait timeout parameter.
- **Bug Fixes**
- Added a new timeout error variant for improved error reporting on
index operations.
- **Tests**
- Added tests covering successful index readiness waiting, timeout
scenarios, and missing index cases.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Added the ability to prewarm (load into memory) table indexes via new
methods in Python, Node.js, and Rust APIs, potentially reducing
cold-start query latency.
- **Bug Fixes**
- Ensured prewarming an index does not interfere with subsequent search
operations.
- **Tests**
- Introduced new test cases to verify full-text search index creation,
prewarming, and search functionalities in both Python and Node.js.
- **Chores**
- Updated dependencies for improved compatibility and performance.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: Lu Qiu <luqiujob@gmail.com>
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Enhanced full-text search capabilities with support for phrase
queries, fuzzy matching, boosting, and multi-column matching.
- Search methods now accept full-text query objects directly, improving
query flexibility and precision.
- Python and JavaScript SDKs updated to handle full-text queries
seamlessly, including async search support.
- **Tests**
- Added comprehensive tests covering fuzzy search, phrase search, and
boosted queries to ensure robust full-text search functionality.
- **Documentation**
- Updated query class documentation to reflect new constructor options
and removal of deprecated methods for clarity and simplicity.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
This reverts commit a547c523c2 or #2281
The current implementation can cause panics and performance degradation.
I will bring this back with more testing in
https://github.com/lancedb/lancedb/pull/2311
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **Documentation**
- Enhanced clarity on read consistency settings with updated
descriptions and default behavior.
- Removed outdated warnings about eventual consistency from the
troubleshooting guide.
- **Refactor**
- Streamlined the handling of the read consistency interval across
integrations, now defaulting to "None" for improved performance.
- Simplified internal logic to offer a more consistent experience.
- **Tests**
- Updated test expectations to reflect the new default representation
for the read consistency interval.
- Removed redundant tests related to "no consistency" settings for
streamlined testing.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **Chores**
- Updated internal library dependencies to the latest beta version for
improved system stability.
- **Tests**
- Added automated tests to validate full-text search functionality on
list-based text fields.
- **Refactor**
- Enhanced the search processing logic to provide robust support for
list-type text data, ensuring more reliable results.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
Closes#2287
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
- **New Features**
- Added configurable timeout support for query executions. Users can now
specify maximum wait times for queries, enhancing control over
long-running operations across various integrations.
- **Tests**
- Expanded test coverage to validate timeout behavior in both
synchronous and asynchronous query flows, ensuring timely error
responses when query execution exceeds the specified limit.
- Introduced a new test suite to verify query operations when a timeout
is reached, checking for appropriate error handling.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
add analyze plan api to allow executing the queries and see runtime
metrics.
Which help identify the query IO overhead and help identify query
slowness
Previously, when we loaded the next version of the table, we would block
all reads with a write lock. Now, we only do that if
`read_consistency_interval=0`. Otherwise, we load the next version
asynchronously in the background. This should mean that
`read_consistency_interval > 0` won't have a meaningful impact on
latency.
Along with this change, I felt it was safe to change the default
consistency interval to 5 seconds. The current default is `None`, which
means we will **never** check for a new version by default. I think that
default is contrary to most users expectations.
- adds `loss` into the index stats for vector index
- now `optimize` can retrain the vector index
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
Previously, users could only specify new data types in `alterColumns` as
strings:
```ts
await tbl.alterColumns([
path: "price",
dataType: "float"
]);
```
But this has some problems:
1. It wasn't clear what were valid types
2. It was impossible to specify nested types, like lists and vector
columns.
This PR changes it to take an Arrow data type, similar to how the Python
API works. This allows casting vector types:
```ts
await tbl.alterColumns([
{
path: "vector",
dataType: new arrow.FixedSizeList(
2,
new arrow.Field("item", new arrow.Float16(), false),
),
},
]);
```
Closes#2185
In earlier PRs (#1886, #1191) we made the default limit 10 regardless of
the query type. This was confusing for users and in many cases a
breaking change. Users would have queries that used to return all
results, but instead only returned the first 10, causing silent bugs.
Part of the cause was consistency: the Python sync API seems to have
always had a limit of 10, while newer APIs (Python async and Nodejs)
didn't.
This PR sets the default limit only for searches (vector search, FTS),
while letting scans (even with filters) be unbounded. It does this
consistently for all SDKs.
Fixes#1983Fixes#1852Fixes#2141
BREAKING CHANGE: embedding function implementations in Node need to now
call `resolveVariables()` in their constructors and should **not**
implement `toJSON()`.
This tries to address the handling of secrets. In Node, they are
currently lost. In Python, they are currently leaked into the table
schema metadata.
This PR introduces an in-memory variable store on the function registry.
It also allows embedding function definitions to label certain config
values as "sensitive", and the preprocessing logic will raise an error
if users try to pass in hard-coded values.
Closes#2110Closes#521
---------
Co-authored-by: Weston Pace <weston.pace@gmail.com>
* Test that we can insert subschemas (omit nullable columns) in Python.
* More work is needed to support this in Node. See:
https://github.com/lancedb/lancedb/issues/1832
* Test that we can insert data with nullable schema but no nulls in
non-nullable schema.
* Add `"null"` option for `on_bad_vectors` where we fill with null if
the vector is bad.
* Make null values not considered bad if the field itself is nullable.
Allows users to pass multiple query vector as part of a single query
plan. This just runs the queries in parallel without any further
optimization. It's mostly a convenience.
Previously, I think this was only handled by the sync Python remote API.
This makes it common across all SDKs.
Closes https://github.com/lancedb/lancedb/issues/1803
```python
>>> import lancedb
>>> import asyncio
>>>
>>> async def main():
... db = await lancedb.connect_async("./demo")
... table = await db.create_table("demo", [{"id": 1, "vector": [1, 2, 3]}, {"id": 2, "vector": [4, 5, 6]}], mode="overwrite")
... return await table.query().nearest_to([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [4.0, 5.0, 6.0]]).limit(1).to_pandas()
...
>>> asyncio.run(main())
query_index id vector _distance
0 2 2 [4.0, 5.0, 6.0] 0.0
1 1 2 [4.0, 5.0, 6.0] 0.0
2 0 1 [1.0, 2.0, 3.0] 0.0
```
Sometimes it is acceptable to users to only search indexed data and skip
and new un-indexed data. For example, if un-indexed data will be shortly
indexed and they don't mind the delay. In these cases, we can save a lot
of CPU time in search, and provide better latency. Users can activate
this on queries using `fast_search()`.
BREAKING CHANGE: the return value of `index_stats` method has changed
and all `index_stats` APIs now take index name instead of UUID. Also
several deprecated index statistics methods were removed.
* Removes deprecated methods for individual index statistics
* Aligns public `IndexStatistics` struct with API response from LanceDB
Cloud.
* Implements `index_stats` for remote Rust SDK and Python async API.
Lance now supports FTS, so add it into lancedb Python, TypeScript and
Rust SDKs.
For Python, we still use tantivy based FTS by default because the lance
FTS index now misses some features of tantivy.
For Python:
- Support to create lance based FTS index
- Support to specify columns for full text search (only available for
lance based FTS index)
For TypeScript:
- Change the search method so that it can accept both string and vector
- Support full text search
For Rust
- Support full text search
The others:
- Update the FTS doc
BREAKING CHANGE:
- for Python, this renames the attached score column of FTS from "score"
to "_score", this could be a breaking change for users that rely the
scores
---------
Signed-off-by: BubbleCal <bubble-cal@outlook.com>
previously if you tried to install both vectordb and @lancedb/lancedb,
you would get a peer dependency issue due to `vectordb` requiring
`14.0.2` and `@lancedb/lancedb` requiring `15.0.0`. now
`@lancedb/lancedb` should just work with any arrow version 13-17
so this was annoying me when writing the docs.
for a `search` query, one needed to chain `async` calls.
```ts
const res = await (await tbl.search("greetings")).toArray()
```
now the promise will be deferred until the query is collected, leading
to a more functional API
```ts
const res = await tbl.search("greetings").toArray()
```
It's useful to see the underlying query plan for debugging purposes.
This exposes LanceScanner's `explain_plan` function. Addresses #1288
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
