Adds bloom filter index to coordinates agg views #6536
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Improves the performance of the queries that filter the rows by the type's name. For example, when looking for Member.* coordinates we elimiate the need to scan the whole table, by laveraging the idx_typename index. We filter rows by the first part of the coordinate field (substringIndex(coordinate, '.', 1)).
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WalkthroughThis pull request introduces an index to the Changes
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Actionable comments posted: 1
🧹 Nitpick comments (1)
.changeset/three-gifts-battle.md (1)
5-8: Fix typos and grammar in the changeset.There are several typos in the text:
- "speedup" should be "speed up" (verb form)
- "ClickHoue" should be "ClickHouse"
- "statisticts" should be "statistics"
Apply this diff:
-Adds an index to coordinates\_(daily,hourly,minutely) tables to speedup the -get_top_operations_for_types ClickHoue query. +Adds an index to coordinates\_(daily,hourly,minutely) tables to speed up the +get_top_operations_for_types ClickHouse query. -Reading of type and fields usage statisticts should be noticeably faster now on big datasets. +Reading of type and fields usage statistics should be noticeably faster now on big datasets.🧰 Tools
🪛 LanguageTool
[grammar] ~5-~5: The word “speedup” is a noun. The verb is spelled with a white space.
Context: ...ates_(daily,hourly,minutely) tables to speedup the get_top_operations_for_types ClickH...(NOUN_VERB_CONFUSION)
[uncategorized] ~8-~8: The grammatical number of this noun doesn’t look right. Consider replacing it.
Context: ...s ClickHoue query. Reading of type and fields usage statisticts should be noticeably ...(AI_EN_LECTOR_REPLACEMENT_NOUN_NUMBER)
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.changeset/three-gifts-battle.md(1 hunks)packages/migrations/src/clickhouse-actions/012-coordinates-typename-index.ts(1 hunks)packages/migrations/src/clickhouse.ts(1 hunks)
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.changeset/three-gifts-battle.md
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.changeset/three-gifts-battle.md
[grammar] ~5-~5: The word “speedup” is a noun. The verb is spelled with a white space.
Context: ...ates_(daily,hourly,minutely) tables to speedup the get_top_operations_for_types ClickH...
(NOUN_VERB_CONFUSION)
[uncategorized] ~8-~8: The grammatical number of this noun doesn’t look right. Consider replacing it.
Context: ...s ClickHoue query. Reading of type and fields usage statisticts should be noticeably ...
(AI_EN_LECTOR_REPLACEMENT_NOUN_NUMBER)
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🔇 Additional comments (4)
packages/migrations/src/clickhouse-actions/012-coordinates-typename-index.ts (3)
4-15: LGTM! Well-structured schema validation.The use of zod for schema validation ensures type safety and runtime validation of the query results.
17-22: LGTM! Clear and informative documentation.The comments effectively explain the purpose and benefits of the index, highlighting the performance improvement for type name filtering.
39-39:❓ Verification inconclusive
Verify the bloom filter parameters.
The index uses ngrambf_v1 with parameters (4, 1024, 2, 0). Please ensure these parameters are optimal for your use case.
🌐 Web query:
What are the optimal parameters for ClickHouse bloom filter index (ngrambf_v1)?💡 Result:
Bloom filter indexes (
ngrambf_v1) in ClickHouse optimize text searches by skipping data granules unlikely to contain matches. Their effectiveness depends on four key parameters:Key Parameters
1. N-Gram Size
- Defines substring length used for indexing (e.g.,
n=4splits "test" into["tes", "est"]).- Optimal Choice:
- Use
n=3orn=4for general text search[1][8].- Avoid values smaller than typical search patterns (e.g.,
n=3won’t optimize searches for "hi")[7][8].2. Bloom Filter Size
- Allocated memory per granule (e.g.,
1024bytes).- Trade-offs:
- Larger filters reduce false positives but increase storage[1][8].
- For ~130GB log data,
1024bytes balanced performance and storage[1].3. Hash Functions
- Number of hash functions applied to each n-gram (e.g.,
1).- Guidelines:
- More hashes reduce collisions but increase CPU[1][8].
- Typically use
1–2hash functions[1][3].4. Granularity
- Number of granules combined per index entry (
GRANULARITY).- Recommendations:
- Start with
GRANULARITY 1(one index entry per granule)[1][8].- Higher granularity reduces index size but may skip fewer granules[3][7].
Best Configurations
Use Case Example Configuration Performance Impact Log data with rare terms ngrambf_v1(4, 1024, 1, 0)Query time reduced by ~90%[1] Short-text optimization ngrambf_v1(3, 512, 2, 0)Balances speed and precision[8]
Optimization Tips
- Test iteratively: Benchmark with real queries and adjust parameters[1][8].
- Monitor storage: Indexes add ~2–10% storage overhead[5][8].
- Avoid over-indexing: Use only on columns with high query frequency[7][9].
For example, Tinybird achieved significant improvements using
ngrambf_v1(4, 1024, 1, 0)on log data by focusing on uncommon search terms[1]. Conversely, overly large filters or smallnvalues led to inefficiencies in full-text search scenarios[5][8].Citations:
- 1: https://www.tinybird.co/blog-posts/using-bloom-filter-text-indexes-in-clickhouse
- 2: why doesn't my ngrambf_v1 index work well ClickHouse/ClickHouse#12743
- 3: https://www.ibm.com/think/tutorials/improve-query-performance-with-clickhouse-data-skipping-index
- 4: The ngrambf_v1 index, dependent on the window size, can yield inconsistent results for LIKE queries on strings that contain backslashes. ClickHouse/ClickHouse#51154
- 5: https://www.tinybird.co/blog-posts/text-search-at-scale-with-clickhouse
- 6: https://groups.google.com/g/clickhouse/c/yURRq69cZr8
- 7: https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/mergetree
- 8: https://chistadata.com/how-data-skipping-indexes-benefits-clickhouse-performance/
- 9: https://clickhouse.com/docs/en/optimize/skipping-indexes
- 10: Functions to estimate Bloom FIlter size ClickHouse/ClickHouse#37799
Action: Reassess Bloom Filter Index Parameters
The current index definition uses
ngrambf_v1(4, 1024, 2, 0)withGRANULARITY 1. Based on available guidance:
- N-Gram Size (4): Generally acceptable for typical text queries.
- Bloom Filter Size (1024): Balances memory allocation and false positives.
- Hash Functions (2): Although standard recommendations often use 1–2 hash functions, many common setups (e.g., log data) may opt for a single hash function. Verify if the extra hash function is warranted given your specific query patterns.
- Granularity (1): Meets common starting points for index granule coverage.
Please double-check if the use-case justifies the 2 hash functions versus a configuration with 1 hash, especially considering your performance benchmarks and storage trade-offs. Adjust the parameters if your testing indicates that a different setup (e.g.,
ngrambf_v1(4, 1024, 1, 0)for log data orngrambf_v1(3, 512, 2, 0)for short-text optimization) would yield better results.packages/migrations/src/clickhouse.ts (1)
172-172: LGTM! Correctly integrated new migration.The new migration is properly added to the actions array, maintaining the sequential order.
| `ALTER TABLE ".inner_id.${uuid}" ADD INDEX idx_typename (substringIndex(coordinate, '.', 1)) TYPE ngrambf_v1(4, 1024, 2, 0) GRANULARITY 1`, | ||
| ); | ||
| const indexes = await query(` | ||
| SELECT name FROM system.data_skipping_indices WHERE table = ${'.inner_id.' + uuid} AND name = 'idx_typename' |
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Fix SQL injection vulnerability.
The table name is concatenated directly into the SQL query, which could lead to SQL injection.
Apply this diff to use parameterized query:
- SELECT name FROM system.data_skipping_indices WHERE table = ${'.inner_id.' + uuid} AND name = 'idx_typename'
+ SELECT name FROM system.data_skipping_indices WHERE table = {table:String} AND name = 'idx_typename'Then pass the parameter in the query call:
- `).then(async r => DataSkippingIndicesModel.parse(r.data));
+ `, {
+ table: '.inner_id.' + uuid
+ }).then(async r => DataSkippingIndicesModel.parse(r.data));📝 Committable suggestion
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Suggested change
| SELECT name FROM system.data_skipping_indices WHERE table = ${'.inner_id.' + uuid} AND name = 'idx_typename' | |
| SELECT name FROM system.data_skipping_indices WHERE table = {table:String} AND name = 'idx_typename' | |
| `, { | |
| table: '.inner_id.' + uuid | |
| }).then(async r => DataSkippingIndicesModel.parse(r.data)); |
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The sql tag does exactly that, it extracts the raw values and replaces them with {paramN:data-type} format.
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@kamilkisiela Thank you for the clarification. I understand now that the sql tag automatically handles parameterization of values, making the query safe from SQL injection.
✏️ Learnings added
Learnt from: kamilkisiela
PR: graphql-hive/console#6536
File: packages/migrations/src/clickhouse-actions/012-coordinates-typename-index.ts:42-42
Timestamp: 2025-02-19T15:26:15.834Z
Learning: The `sql` tag in ClickHouse queries automatically handles SQL injection prevention by extracting raw values and converting them to parameterized format `{paramN:data-type}`.
Note: Learnings are effective only in the context of similar code segments. To apply general coding guidelines, please configure review instructions. You can manage existing learnings in the app.
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{
"name": "context"
},
{
"name": "dist"
},
{
"name": "dockerfile"
},
{
"name": "shared"
}
]
},
"environment": {
"platform": "linux/arm64"
}
}
},
"buildx.build.ref": "builder-4ca471db-8ca5-4873-b8ce-24ba47ac5aa6/builder-4ca471db-8ca5-4873-b8ce-24ba47ac5aa60/60jduv8lyun6r16ge08ta043e",
"containerimage.config.digest": "sha256:a11d665ac093eca1e4eb6f76f32a154bd6647b7a484a76b4ae41844e5373d918",
"containerimage.descriptor": {
"mediaType": "application/vnd.docker.distribution.manifest.v2+json",
"digest": "sha256:1ff41e6c33dc5e6ef044ae71f9e5c786c87afb41c722122c17ef94460ab518c4",
"size": 2075,
"platform": {
"architecture": "arm64",
"os": "linux"
}
},
"containerimage.digest": "sha256:1ff41e6c33dc5e6ef044ae71f9e5c786c87afb41c722122c17ef94460ab518c4",
"image.name": "ghcr.io/graphql-hive/webhooks:076e5177330790e216cd43e31c8f99f493518601-arm64,ghcr.io/graphql-hive/webhooks:kamil_ch_perf_coordinates-arm64"
}
} |
n1ru4l
reviewed
Feb 19, 2025
Comment on lines
38
to
40
| await exec( | ||
| `ALTER TABLE ".inner_id.${uuid}" ADD INDEX idx_typename (substringIndex(coordinate, '.', 1)) TYPE ngrambf_v1(4, 1024, 2, 0) GRANULARITY 1`, | ||
| ); |
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Would this be resilient enough for the migration to terminate unexpectedly and be restarted?
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There's no way to reliably check if materialisation of an index was executed successfully
We can only create an index if not exists, other than that nothing else.
n1ru4l
approved these changes
Feb 19, 2025
dotansimha
approved these changes
Feb 19, 2025
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lgtm. let's just make sure to address @n1ru4l 's comment before merging
We could run them manually if something goes south
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Improves the performance of the queries that filter the rows by the type's name.
For example, when looking for
Member.*coordinates we eliminate the need to scan the whole table, by leveraging theidx_typenameindex. We filter rows by the first part of the coordinate field (substringIndex(coordinate, '.', 1)).It helps ClickHouse to make 10% of the original work :)
Some numbers. Big queries that took 8-17s are now around 2s.