ClickHouse version 25.3 contains 18 new features 🌱 13 performance optimizations 🐣 48 bug fixes 🌦️
This release brings query support for the AWS Glue and Unity catalogs, the new query condition cache, automatic parallelization when querying S3, and new array functions! ## New Contributors A special welcome to all the new contributors in 25.3! The growth of ClickHouse's community is humbling, and we are always grateful for the contributions that have made ClickHouse so popular. Below are the names of the new contributors: *Andrey Nehaychik, Arnaud Briche, Cheryl Tuquib, Didier Franc, Filipp Abapolov, Ilya Kataev, Jason Wong, Jimmy Aguilar Mena, Mark Roberts, Onkar Deshpande, Shankar Iyer, Tariq Almawash, Vico.Wu, f.abapolov, flyaways, otlxm, pheepa, rienath, talmawash* Hint: if you’re curious how we generate this list… [here](https://gist.github.com/gingerwizard/5a9a87a39ba93b422d8640d811e269e9). You can also [view the slides from the presentation](https://presentations.clickhouse.com/2025-release-25.3/). ## AWS Glue and Unity catalogs ### Contributed by Alexander Sapin This release adds support for more Lakehouse catalogs - AWS Glue and Unity. You can query Apache Iceberg tables via AWS Glue by first creating a database engine:
CREATE DATABASE demo_catalog
ENGINE = DataLakeCatalog
SETTINGS catalog_type = 'glue', region = 'us-west-2',
aws_access_key_id = 'AKIA...', aws_secret_access_key = '...';
SHOW TABLES
FROM demo_catalog;
SELECT *
FROM "demo_catalog"."db.table";
CREATE DATABASE unity_demo
ENGINE = DataLakeCatalog(
'https://endpoint.cloud.databricks.com/api/2.1/unity-catalog')
SETTINGS catalog_type = 'unity',
warehouse = 'workspace', catalog_credential = '...'
SHOW TABLES
FROM unity_demo;
SELECT *
FROM "unity_demo"."db.table";
SELECT count()
FROM bluesky
WHERE (data.kind = 'commit')
AND (data.commit.operation = 'create')
AND (data.commit.collection = 'app.bsky.feed.post')
AND (data.commit.record.text LIKE '%🥨%');
This query returns the top languages for pretzel emoji posts:
SELECT
arrayJoin(CAST(data.commit.record.langs, 'Array(String)')) AS language,
count() AS count
FROM bluesky
WHERE (data.kind = 'commit')
AND (data.commit.operation = 'create')
AND (data.commit.collection = 'app.bsky.feed.post')
AND (data.commit.record.text LIKE '%🥨%')
GROUP BY language
ORDER BY count DESC;
Both queries share the same predicate:
WHERE (data.kind = 'commit')
AND (data.commit.operation = 'create')
AND (data.commit.collection = 'app.bsky.feed.post')
AND (data.commit.record.text LIKE '%🥨%')
With the query condition cache, the scan result from the first query is cached and reused by the second—resulting in a significant speedup.
You can find results for the queries above, performance metrics, and a deep dive into how the query condition cache works in our [dedicated blog post](https://clickhouse.com/blog/introducing-the-clickhouse-query-condition-cache).
## Automatic parallelization for external data
### Contributed by Konstantin Bogdanov
In the previous section, we saw how to count the number of pretzel emoji mentions when querying the BlueSky dataset loaded into a `MergeTree` table. Let’s now see how long it takes to query that data directly on S3:
SELECT count()
FROM s3('https://clickhouse-public-datasets.s3.amazonaws.com/bluesky/file_{0001..0100}.json.gz', 'JSONAsObject')
WHERE (json.kind = 'commit')
AND (json.commit.operation = 'create')
AND (json.commit.collection = 'app.bsky.feed.post')
AND (json.commit.record.text LIKE '%🥨%')
SETTINGS
input_format_allow_errors_num = 100,
input_format_allow_errors_ratio = 1;
┌─count()─┐
│ 69 │
└─────────┘
1 row in set. Elapsed: 64.902 sec. Processed 100.00 million rows, 13.35 GB (1.54 million rows/s., 205.75 MB/s.)
Peak memory usage: 2.68 GiB.
SELECT count()
FROM s3Cluster(default, 'https://clickhouse-public-datasets.s3.amazonaws.com/bluesky/file_{0001..0100}.json.gz', 'JSONAsObject')
WHERE (json.kind = 'commit')
AND (json.commit.operation = 'create')
AND (json.commit.collection = 'app.bsky.feed.post')
AND (json.commit.record.text LIKE '%🥨%')
SETTINGS
input_format_allow_errors_num = 100,
input_format_allow_errors_ratio = 1;
┌─count()─┐
│ 69 │
└─────────┘
1 row in set. Elapsed: 16.689 sec. Processed 100.00 million rows, 13.38 GB (5.99 million rows/s., 801.86 MB/s.)
Peak memory usage: 2.06 GiB.
SET parallel_replicas_for_cluster_engines = 0;
WITH
[1, 2, 3] AS a,
[2, 3, 4] AS b
SELECT
arrayUnion(a, b) AS union,
arrayIntersect(a, b) AS intersect,
arrayFilter(x -> (NOT has(intersect, x)), union) AS unionButNotIntersect;
WITH
[1, 2, 3] AS a,
[2, 3, 4] AS b
SELECT
arrayUnion(a, b) AS union,
arrayIntersect(a, b) AS intersect,
arrayFilter(x -> (NOT has(intersect, x)), union) AS unionNotIntersect,
arraySymmetricDifference(a, b) AS symmetricDifference;
┌─union─────┬─intersect─┬─unionNotIntersect─┬─symmetricDifference─┐
│ [3,2,1,4] │ [2,3] │ [1,4] │ [1,4] │
└───────────┴───────────┴───────────────────┴─────────────────────┘
SELECT round(estimateCompressionRatio('NONE')(CounterID)) AS none,
round(estimateCompressionRatio('LZ4')(CounterID)) AS lz4,
round(estimateCompressionRatio('ZSTD')(CounterID)) AS zstd,
round(estimateCompressionRatio('ZSTD(3)')(CounterID)) AS zstd3,
round(estimateCompressionRatio('GCD')(CounterID)) AS gcd,
round(estimateCompressionRatio('Gorilla')(CounterID)) AS gorilla,
round(estimateCompressionRatio('Gorilla, ZSTD')(CounterID)) AS mix
FROM hits
FORMAT PrettyMonoBlock;
┏━━━━━━┳━━━━━┳━━━━━━┳━━━━━━━┳━━━━━┳━━━━━━━━━┳━━━━━━┓
┃ none ┃ lz4 ┃ zstd ┃ zstd3 ┃ gcd ┃ gorilla ┃ mix ┃
┡━━━━━━╇━━━━━╇━━━━━━╇━━━━━━━╇━━━━━╇━━━━━━━━━╇━━━━━━┩
│ 1 │ 248 │ 4974 │ 5110 │ 1 │ 32 │ 6682 │
└──────┴─────┴──────┴───────┴─────┴─────────┴──────┘
DESCRIBE s3(
'https://datasets-documentation.s3.eu-west-3.amazonaws.com' ||
'/amazon_reviews/amazon_reviews_2015.snappy.parquet'
)
SETTINGS describe_compact_output=1;
┌─name──────────────┬─type─────────────┐
│ review_date │ Nullable(UInt16) │
│ marketplace │ Nullable(String) │
│ customer_id │ Nullable(UInt64) │
│ review_id │ Nullable(String) │
│ product_id │ Nullable(String) │
│ product_parent │ Nullable(UInt64) │
│ product_title │ Nullable(String) │
│ product_category │ Nullable(String) │
│ star_rating │ Nullable(UInt8) │
│ helpful_votes │ Nullable(UInt32) │
│ total_votes │ Nullable(UInt32) │
│ vine │ Nullable(Bool) │
│ verified_purchase │ Nullable(Bool) │
│ review_headline │ Nullable(String) │
│ review_body │ Nullable(String) │
└───────────────────┴──────────────────┘
SELECT round(estimateCompressionRatio(‘NONE’)(product_category)) AS none,
round(estimateCompressionRatio(‘LZ4’)(product_category)) AS lz4,
round(estimateCompressionRatio(‘ZSTD’)(product_category)) AS zstd
FROM
s3(
'https://datasets-documentation.s3.eu-west-3.amazonaws.com' ||
'/amazon_reviews/amazon_reviews_2015.snappy.parquet'
);
┌─none─┬─lz4─┬─zstd─┐
│ 1 │ 227 │ 1750 │
└──────┴─────┴──────┘
SELECT round(estimateCompressionRatio(‘NONE’)(product_category)) AS none,
round(estimateCompressionRatio(‘LZ4’)(product_category)) AS lz4,
round(estimateCompressionRatio(‘ZSTD’)(product_category)) AS zstd
FROM
s3(
'https://datasets-documentation.s3.eu-west-3.amazonaws.com' ||
'/amazon_reviews/amazon_reviews_2015.snappy.parquet',
‘Parquet’,
‘product_category LowCardinality(String)’
);
┌─none─┬─lz4─┬─zstd─┐
│ 1 │ 226 │ 1691 │
└──────┴─────┴──────┘
SELECT round(estimateCompressionRatio(‘NONE’)(product_category)) AS none,
round(estimateCompressionRatio(‘LZ4’)(product_category)) AS lz4,
round(estimateCompressionRatio(‘ZSTD’)(product_category)) AS zstd
FROM (
SELECT *
FROM
s3(
'https://datasets-documentation.s3.eu-west-3.amazonaws.com' ||
'/amazon_reviews/amazon_reviews_2015.snappy.parquet',
‘Parquet’,
‘product_category LowCardinality(String)’
)
ORDER BY product_category
);
┌─none─┬─lz4─┬─zstd─┐
│ 1 │ 252 │ 7097 │
└──────┴─────┴──────┘