docs/content/primary-key-table/query-performance.md

---
title: "Query Performance"
weight: 8
type: docs
aliases:
- /primary-key-table/query-performance.html
---
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# Query Performance

## Table Mode

The table schema has the greatest impact on query performance. See [Table Mode]({{< ref "primary-key-table/table-mode" >}}).

For Merge On Read table, the most important thing you should pay attention to is the number of buckets, which will limit
the concurrency of reading data.

For MOW (Deletion Vectors) or COW table or [Read Optimized]({{< ref "concepts/system-tables#read-optimized-table" >}}) table,
there is no limit to the concurrency of reading data, and they can also utilize some filtering conditions for non-primary-key columns.

## Aggregate push down

Table with Deletion Vectors Enabled supports aggregate push down:

```sql
SELECT COUNT(*) FROM TABLE WHERE DT = '20230101';
```

This query can be accelerated during compilation and returns very quickly.

For Spark SQL, table with default `metadata.stats-mode` can be accelerated:

```sql
SELECT MIN(a), MAX(b) FROM TABLE WHERE DT = '20230101';
```

Min max query can be also accelerated during compilation and returns very quickly.

## Data Skipping By Primary Key Filter

For a regular bucketed table (For example, bucket = 5), the filtering conditions of the primary key will greatly
accelerate queries and reduce the reading of a large number of files.

## Data Skipping By File Index

For full-compacted file, or for primary-key table with `'deletion-vectors.enabled'`, you can use file index, it filters
files by indexing on the reading side.

Define `file-index.bitmap.columns`, Data file index is an external index file and Paimon will create its
corresponding index file for each file. If the index file is too small, it will be stored directly in the manifest,
otherwise in the directory of the data file. Each data file corresponds to an index file, which has a separate file
definition and can contain different types of indexes with multiple columns.

Different file indexes may be efficient in different scenarios. For example bloom filter may speed up query in point lookup
scenario. Using a bitmap may consume more space but can result in greater accuracy.

* [BloomFilter]({{< ref "concepts/spec/fileindex#index-bloomfilter" >}}): `file-index.bloom-filter.columns`.
* [Bitmap]({{< ref "concepts/spec/fileindex#index-bitmap" >}}): `file-index.bitmap.columns`.
* [Range Bitmap]({{< ref "concepts/spec/fileindex#index-range-bitmap" >}}): `file-index.range-bitmap.columns`.

If you want to add file index to existing table, without any rewrite, you can use `rewrite_file_index` procedure. Before
we use the procedure, you should config appropriate configurations in target table. You can use ALTER clause to config
`file-index.<filter-type>.columns` to the table.

How to invoke: see [flink procedures]({{< ref "flink/procedures#procedures" >}})

## Bucketed Join

Fixed Bucketed table (e.g. bucket = 10) can be used to avoid shuffle if necessary in batch query, for example, you can
use the following Spark SQL to read a Paimon table:

```sql
SET spark.sql.sources.v2.bucketing.enabled = true;

CREATE TABLE FACT_TABLE (order_id INT, f1 STRING) TBLPROPERTIES ('bucket'='10', 'primary-key' = 'order_id');

CREATE TABLE DIM_TABLE (order_id INT, f2 STRING) TBLPROPERTIES ('bucket'='10', 'primary-key' = 'order_id');

SELECT * FROM FACT_TABLE JOIN DIM_TABLE on t1.order_id = t4.order_id;
```

The `spark.sql.sources.v2.bucketing.enabled` config is used to enable bucketing for V2 data sources. When turned on,
Spark will recognize the specific distribution reported by a V2 data source through SupportsReportPartitioning, and
will try to avoid shuffle if necessary.

The costly join shuffle will be avoided if two tables have the same bucketing strategy and same number of buckets.