Configuration Settings

The following configuration options can be passed to SessionConfig to control various aspects of query execution.

For applications which do not expose SessionConfig, like datafusion-cli, these options may also be set via environment variables. To construct a session with options from the environment, use SessionConfig::from_env. The name of the environment variable is the option’s key, transformed to uppercase and with periods replaced with underscores. For example, to configure datafusion.execution.batch_size you would set the DATAFUSION_EXECUTION_BATCH_SIZE environment variable. Values are parsed according to the same rules used in casts from Utf8. If the value in the environment variable cannot be cast to the type of the configuration option, the default value will be used instead and a warning emitted. Environment variables are read during SessionConfig initialisation so they must be set beforehand and will not affect running sessions.

key

default

description

datafusion.catalog.create_default_catalog_and_schema

true

Whether the default catalog and schema should be created automatically.

datafusion.catalog.default_catalog

datafusion

The default catalog name - this impacts what SQL queries use if not specified

datafusion.catalog.default_schema

public

The default schema name - this impacts what SQL queries use if not specified

datafusion.catalog.information_schema

false

Should DataFusion provide access to information_schema virtual tables for displaying schema information

datafusion.catalog.location

NULL

Location scanned to load tables for default schema

datafusion.catalog.format

NULL

Type of TableProvider to use when loading default schema

datafusion.catalog.has_header

false

Default value for format.has_header for CREATE EXTERNAL TABLE if not specified explicitly in the statement.

datafusion.execution.batch_size

8192

Default batch size while creating new batches, it’s especially useful for buffer-in-memory batches since creating tiny batches would result in too much metadata memory consumption

datafusion.execution.coalesce_batches

true

When set to true, record batches will be examined between each operator and small batches will be coalesced into larger batches. This is helpful when there are highly selective filters or joins that could produce tiny output batches. The target batch size is determined by the configuration setting

datafusion.execution.collect_statistics

false

Should DataFusion collect statistics after listing files

datafusion.execution.target_partitions

0

Number of partitions for query execution. Increasing partitions can increase concurrency. Defaults to the number of CPU cores on the system

datafusion.execution.time_zone

+00:00

The default time zone Some functions, e.g. EXTRACT(HOUR from SOME_TIME), shift the underlying datetime according to this time zone, and then extract the hour

datafusion.execution.parquet.enable_page_index

true

If true, reads the Parquet data page level metadata (the Page Index), if present, to reduce the I/O and number of rows decoded.

datafusion.execution.parquet.pruning

true

If true, the parquet reader attempts to skip entire row groups based on the predicate in the query and the metadata (min/max values) stored in the parquet file

datafusion.execution.parquet.skip_metadata

true

If true, the parquet reader skip the optional embedded metadata that may be in the file Schema. This setting can help avoid schema conflicts when querying multiple parquet files with schemas containing compatible types but different metadata

datafusion.execution.parquet.metadata_size_hint

NULL

If specified, the parquet reader will try and fetch the last size_hint bytes of the parquet file optimistically. If not specified, two reads are required: One read to fetch the 8-byte parquet footer and another to fetch the metadata length encoded in the footer

datafusion.execution.parquet.pushdown_filters

false

If true, filter expressions are be applied during the parquet decoding operation to reduce the number of rows decoded. This optimization is sometimes called “late materialization”.

datafusion.execution.parquet.reorder_filters

false

If true, filter expressions evaluated during the parquet decoding operation will be reordered heuristically to minimize the cost of evaluation. If false, the filters are applied in the same order as written in the query

datafusion.execution.parquet.data_pagesize_limit

1048576

Sets best effort maximum size of data page in bytes

datafusion.execution.parquet.write_batch_size

1024

Sets write_batch_size in bytes

datafusion.execution.parquet.writer_version

1.0

Sets parquet writer version valid values are “1.0” and “2.0”

datafusion.execution.parquet.compression

zstd(3)

Sets default parquet compression codec Valid values are: uncompressed, snappy, gzip(level), lzo, brotli(level), lz4, zstd(level), and lz4_raw. These values are not case sensitive. If NULL, uses default parquet writer setting

datafusion.execution.parquet.dictionary_enabled

NULL

Sets if dictionary encoding is enabled. If NULL, uses default parquet writer setting

datafusion.execution.parquet.dictionary_page_size_limit

1048576

Sets best effort maximum dictionary page size, in bytes

datafusion.execution.parquet.statistics_enabled

NULL

Sets if statistics are enabled for any column Valid values are: “none”, “chunk”, and “page” These values are not case sensitive. If NULL, uses default parquet writer setting

datafusion.execution.parquet.max_statistics_size

NULL

Sets max statistics size for any column. If NULL, uses default parquet writer setting

datafusion.execution.parquet.max_row_group_size

1048576

Target maximum number of rows in each row group (defaults to 1M rows). Writing larger row groups requires more memory to write, but can get better compression and be faster to read.

datafusion.execution.parquet.created_by

datafusion version 38.0.0

Sets “created by” property

datafusion.execution.parquet.column_index_truncate_length

NULL

Sets column index truncate length

datafusion.execution.parquet.data_page_row_count_limit

18446744073709551615

Sets best effort maximum number of rows in data page

datafusion.execution.parquet.encoding

NULL

Sets default encoding for any column Valid values are: plain, plain_dictionary, rle, bit_packed, delta_binary_packed, delta_length_byte_array, delta_byte_array, rle_dictionary, and byte_stream_split. These values are not case sensitive. If NULL, uses default parquet writer setting

datafusion.execution.parquet.bloom_filter_on_read

true

Use any available bloom filters when reading parquet files

datafusion.execution.parquet.bloom_filter_on_write

false

Write bloom filters for all columns when creating parquet files

datafusion.execution.parquet.bloom_filter_fpp

NULL

Sets bloom filter false positive probability. If NULL, uses default parquet writer setting

datafusion.execution.parquet.bloom_filter_ndv

NULL

Sets bloom filter number of distinct values. If NULL, uses default parquet writer setting

datafusion.execution.parquet.allow_single_file_parallelism

true

Controls whether DataFusion will attempt to speed up writing parquet files by serializing them in parallel. Each column in each row group in each output file are serialized in parallel leveraging a maximum possible core count of n_filesn_row_groupsn_columns.

datafusion.execution.parquet.maximum_parallel_row_group_writers

1

By default parallel parquet writer is tuned for minimum memory usage in a streaming execution plan. You may see a performance benefit when writing large parquet files by increasing maximum_parallel_row_group_writers and maximum_buffered_record_batches_per_stream if your system has idle cores and can tolerate additional memory usage. Boosting these values is likely worthwhile when writing out already in-memory data, such as from a cached data frame.

datafusion.execution.parquet.maximum_buffered_record_batches_per_stream

2

By default parallel parquet writer is tuned for minimum memory usage in a streaming execution plan. You may see a performance benefit when writing large parquet files by increasing maximum_parallel_row_group_writers and maximum_buffered_record_batches_per_stream if your system has idle cores and can tolerate additional memory usage. Boosting these values is likely worthwhile when writing out already in-memory data, such as from a cached data frame.

datafusion.execution.aggregate.scalar_update_factor

10

Specifies the threshold for using ScalarValues to update accumulators during high-cardinality aggregations for each input batch. The aggregation is considered high-cardinality if the number of affected groups is greater than or equal to batch_size / scalar_update_factor. In such cases, ScalarValues are utilized for updating accumulators, rather than the default batch-slice approach. This can lead to performance improvements. By adjusting the scalar_update_factor, you can balance the trade-off between more efficient accumulator updates and the number of groups affected.

datafusion.execution.planning_concurrency

0

Fan-out during initial physical planning. This is mostly use to plan UNION children in parallel. Defaults to the number of CPU cores on the system

datafusion.execution.sort_spill_reservation_bytes

10485760

Specifies the reserved memory for each spillable sort operation to facilitate an in-memory merge. When a sort operation spills to disk, the in-memory data must be sorted and merged before being written to a file. This setting reserves a specific amount of memory for that in-memory sort/merge process. Note: This setting is irrelevant if the sort operation cannot spill (i.e., if there’s no DiskManager configured).

datafusion.execution.sort_in_place_threshold_bytes

1048576

When sorting, below what size should data be concatenated and sorted in a single RecordBatch rather than sorted in batches and merged.

datafusion.execution.meta_fetch_concurrency

32

Number of files to read in parallel when inferring schema and statistics

datafusion.execution.minimum_parallel_output_files

4

Guarantees a minimum level of output files running in parallel. RecordBatches will be distributed in round robin fashion to each parallel writer. Each writer is closed and a new file opened once soft_max_rows_per_output_file is reached.

datafusion.execution.soft_max_rows_per_output_file

50000000

Target number of rows in output files when writing multiple. This is a soft max, so it can be exceeded slightly. There also will be one file smaller than the limit if the total number of rows written is not roughly divisible by the soft max

datafusion.execution.max_buffered_batches_per_output_file

2

This is the maximum number of RecordBatches buffered for each output file being worked. Higher values can potentially give faster write performance at the cost of higher peak memory consumption

datafusion.execution.listing_table_ignore_subdirectory

true

Should sub directories be ignored when scanning directories for data files. Defaults to true (ignores subdirectories), consistent with Hive. Note that this setting does not affect reading partitioned tables (e.g. /table/year=2021/month=01/data.parquet).

datafusion.execution.enable_recursive_ctes

true

Should DataFusion support recursive CTEs

datafusion.execution.split_file_groups_by_statistics

false

Attempt to eliminate sorts by packing & sorting files with non-overlapping statistics into the same file groups. Currently experimental

datafusion.optimizer.enable_distinct_aggregation_soft_limit

true

When set to true, the optimizer will push a limit operation into grouped aggregations which have no aggregate expressions, as a soft limit, emitting groups once the limit is reached, before all rows in the group are read.

datafusion.optimizer.enable_round_robin_repartition

true

When set to true, the physical plan optimizer will try to add round robin repartitioning to increase parallelism to leverage more CPU cores

datafusion.optimizer.enable_topk_aggregation

true

When set to true, the optimizer will attempt to perform limit operations during aggregations, if possible

datafusion.optimizer.filter_null_join_keys

false

When set to true, the optimizer will insert filters before a join between a nullable and non-nullable column to filter out nulls on the nullable side. This filter can add additional overhead when the file format does not fully support predicate push down.

datafusion.optimizer.repartition_aggregations

true

Should DataFusion repartition data using the aggregate keys to execute aggregates in parallel using the provided target_partitions level

datafusion.optimizer.repartition_file_min_size

10485760

Minimum total files size in bytes to perform file scan repartitioning.

datafusion.optimizer.repartition_joins

true

Should DataFusion repartition data using the join keys to execute joins in parallel using the provided target_partitions level

datafusion.optimizer.allow_symmetric_joins_without_pruning

true

Should DataFusion allow symmetric hash joins for unbounded data sources even when its inputs do not have any ordering or filtering If the flag is not enabled, the SymmetricHashJoin operator will be unable to prune its internal buffers, resulting in certain join types - such as Full, Left, LeftAnti, LeftSemi, Right, RightAnti, and RightSemi - being produced only at the end of the execution. This is not typical in stream processing. Additionally, without proper design for long runner execution, all types of joins may encounter out-of-memory errors.

datafusion.optimizer.repartition_file_scans

true

When set to true, file groups will be repartitioned to achieve maximum parallelism. Currently Parquet and CSV formats are supported. If set to true, all files will be repartitioned evenly (i.e., a single large file might be partitioned into smaller chunks) for parallel scanning. If set to false, different files will be read in parallel, but repartitioning won’t happen within a single file.

datafusion.optimizer.repartition_windows

true

Should DataFusion repartition data using the partitions keys to execute window functions in parallel using the provided target_partitions level

datafusion.optimizer.repartition_sorts

true

Should DataFusion execute sorts in a per-partition fashion and merge afterwards instead of coalescing first and sorting globally. With this flag is enabled, plans in the form below text "SortExec: [a@0 ASC]", " CoalescePartitionsExec", " RepartitionExec: partitioning=RoundRobinBatch(8), input_partitions=1", would turn into the plan below which performs better in multithreaded environments text "SortPreservingMergeExec: [a@0 ASC]", " SortExec: [a@0 ASC]", " RepartitionExec: partitioning=RoundRobinBatch(8), input_partitions=1",

datafusion.optimizer.prefer_existing_sort

false

When true, DataFusion will opportunistically remove sorts when the data is already sorted, (i.e. setting preserve_order to true on RepartitionExec and using SortPreservingMergeExec) When false, DataFusion will maximize plan parallelism using RepartitionExec even if this requires subsequently resorting data using a SortExec.

datafusion.optimizer.skip_failed_rules

false

When set to true, the logical plan optimizer will produce warning messages if any optimization rules produce errors and then proceed to the next rule. When set to false, any rules that produce errors will cause the query to fail

datafusion.optimizer.max_passes

3

Number of times that the optimizer will attempt to optimize the plan

datafusion.optimizer.top_down_join_key_reordering

true

When set to true, the physical plan optimizer will run a top down process to reorder the join keys

datafusion.optimizer.prefer_hash_join

true

When set to true, the physical plan optimizer will prefer HashJoin over SortMergeJoin. HashJoin can work more efficiently than SortMergeJoin but consumes more memory

datafusion.optimizer.hash_join_single_partition_threshold

1048576

The maximum estimated size in bytes for one input side of a HashJoin will be collected into a single partition

datafusion.optimizer.hash_join_single_partition_threshold_rows

131072

The maximum estimated size in rows for one input side of a HashJoin will be collected into a single partition

datafusion.optimizer.default_filter_selectivity

20

The default filter selectivity used by Filter Statistics when an exact selectivity cannot be determined. Valid values are between 0 (no selectivity) and 100 (all rows are selected).

datafusion.optimizer.prefer_existing_union

false

When set to true, the optimizer will not attempt to convert Union to Interleave

datafusion.explain.logical_plan_only

false

When set to true, the explain statement will only print logical plans

datafusion.explain.physical_plan_only

false

When set to true, the explain statement will only print physical plans

datafusion.explain.show_statistics

false

When set to true, the explain statement will print operator statistics for physical plans

datafusion.explain.show_sizes

true

When set to true, the explain statement will print the partition sizes

datafusion.sql_parser.parse_float_as_decimal

false

When set to true, SQL parser will parse float as decimal type

datafusion.sql_parser.enable_ident_normalization

true

When set to true, SQL parser will normalize ident (convert ident to lowercase when not quoted)

datafusion.sql_parser.dialect

generic

Configure the SQL dialect used by DataFusion’s parser; supported values include: Generic, MySQL, PostgreSQL, Hive, SQLite, Snowflake, Redshift, MsSQL, ClickHouse, BigQuery, and Ansi.