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new 0129c36e57 Publish built docs triggered by
9fde5c4282fd9f0e3332fb40998bf1562c17fcda
0129c36e57 is described below
commit 0129c36e5706e0212175522f066933ac1c05b780
Author: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
AuthorDate: Sat Oct 21 20:24:31 2023 +0000
Publish built docs triggered by 9fde5c4282fd9f0e3332fb40998bf1562c17fcda
---
_sources/user-guide/configs.md.txt | 1 +
searchindex.js | 2 +-
user-guide/configs.html | 52 ++++++++++++++++++++------------------
3 files changed, 30 insertions(+), 25 deletions(-)
diff --git a/_sources/user-guide/configs.md.txt
b/_sources/user-guide/configs.md.txt
index f331b2de44..3476118ca6 100644
--- a/_sources/user-guide/configs.md.txt
+++ b/_sources/user-guide/configs.md.txt
@@ -77,6 +77,7 @@ Environment variables are read during `SessionConfig`
initialisation so they mus
| 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 `DiskManag [...]
| 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.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
[...]
diff --git a/searchindex.js b/searchindex.js
index b121738f0d..72704921b7 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["contributor-guide/architecture",
"contributor-guide/communication", "contributor-guide/index",
"contributor-guide/quarterly_roadmap", "contributor-guide/roadmap",
"contributor-guide/specification/index",
"contributor-guide/specification/invariants",
"contributor-guide/specification/output-field-name-semantic", "index",
"library-user-guide/adding-udfs", "library-user-guide/building-logical-plans",
"library-user-guide/catalogs", "library-user-guide/custom-tab [...]
\ No newline at end of file
+Search.setIndex({"docnames": ["contributor-guide/architecture",
"contributor-guide/communication", "contributor-guide/index",
"contributor-guide/quarterly_roadmap", "contributor-guide/roadmap",
"contributor-guide/specification/index",
"contributor-guide/specification/invariants",
"contributor-guide/specification/output-field-name-semantic", "index",
"library-user-guide/adding-udfs", "library-user-guide/building-logical-plans",
"library-user-guide/catalogs", "library-user-guide/custom-tab [...]
\ No newline at end of file
diff --git a/user-guide/configs.html b/user-guide/configs.html
index fd1855990e..ef6f6b42f0 100644
--- a/user-guide/configs.html
+++ b/user-guide/configs.html
@@ -570,99 +570,103 @@ Environment variables are read during <code
class="docutils literal notranslate"
<td><p>32</p></td>
<td><p>Number of files to read in parallel when inferring schema and
statistics</p></td>
</tr>
-<tr
class="row-even"><td><p>datafusion.execution.soft_max_rows_per_output_file</p></td>
+<tr
class="row-even"><td><p>datafusion.execution.minimum_parallel_output_files</p></td>
+<td><p>4</p></td>
+<td><p>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.</p></td>
+</tr>
+<tr
class="row-odd"><td><p>datafusion.execution.soft_max_rows_per_output_file</p></td>
<td><p>50000000</p></td>
<td><p>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</p></td>
</tr>
-<tr
class="row-odd"><td><p>datafusion.execution.max_buffered_batches_per_output_file</p></td>
+<tr
class="row-even"><td><p>datafusion.execution.max_buffered_batches_per_output_file</p></td>
<td><p>2</p></td>
<td><p>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</p></td>
</tr>
-<tr
class="row-even"><td><p>datafusion.optimizer.enable_round_robin_repartition</p></td>
+<tr
class="row-odd"><td><p>datafusion.optimizer.enable_round_robin_repartition</p></td>
<td><p>true</p></td>
<td><p>When set to true, the physical plan optimizer will try to add round
robin repartitioning to increase parallelism to leverage more CPU cores</p></td>
</tr>
-<tr
class="row-odd"><td><p>datafusion.optimizer.enable_topk_aggregation</p></td>
+<tr
class="row-even"><td><p>datafusion.optimizer.enable_topk_aggregation</p></td>
<td><p>true</p></td>
<td><p>When set to true, the optimizer will attempt to perform limit
operations during aggregations, if possible</p></td>
</tr>
-<tr class="row-even"><td><p>datafusion.optimizer.filter_null_join_keys</p></td>
+<tr class="row-odd"><td><p>datafusion.optimizer.filter_null_join_keys</p></td>
<td><p>false</p></td>
<td><p>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.</p></td>
</tr>
-<tr
class="row-odd"><td><p>datafusion.optimizer.repartition_aggregations</p></td>
+<tr
class="row-even"><td><p>datafusion.optimizer.repartition_aggregations</p></td>
<td><p>true</p></td>
<td><p>Should DataFusion repartition data using the aggregate keys to execute
aggregates in parallel using the provided <code class="docutils literal
notranslate"><span class="pre">target_partitions</span></code> level</p></td>
</tr>
-<tr
class="row-even"><td><p>datafusion.optimizer.repartition_file_min_size</p></td>
+<tr
class="row-odd"><td><p>datafusion.optimizer.repartition_file_min_size</p></td>
<td><p>10485760</p></td>
<td><p>Minimum total files size in bytes to perform file scan
repartitioning.</p></td>
</tr>
-<tr class="row-odd"><td><p>datafusion.optimizer.repartition_joins</p></td>
+<tr class="row-even"><td><p>datafusion.optimizer.repartition_joins</p></td>
<td><p>true</p></td>
<td><p>Should DataFusion repartition data using the join keys to execute joins
in parallel using the provided <code class="docutils literal notranslate"><span
class="pre">target_partitions</span></code> level</p></td>
</tr>
-<tr
class="row-even"><td><p>datafusion.optimizer.allow_symmetric_joins_without_pruning</p></td>
+<tr
class="row-odd"><td><p>datafusion.optimizer.allow_symmetric_joins_without_pruning</p></td>
<td><p>true</p></td>
<td><p>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 runne [...]
</tr>
-<tr class="row-odd"><td><p>datafusion.optimizer.repartition_file_scans</p></td>
+<tr
class="row-even"><td><p>datafusion.optimizer.repartition_file_scans</p></td>
<td><p>true</p></td>
<td><p>When set to <code class="docutils literal notranslate"><span
class="pre">true</span></code>, file groups will be repartitioned to achieve
maximum parallelism. Currently Parquet and CSV formats are supported. If set to
<code class="docutils literal notranslate"><span
class="pre">true</span></code>, all files will be repartitioned evenly (i.e., a
single large file might be partitioned into smaller chunks) for parallel
scanning. If set to <code class="docutils literal notranslate"><s [...]
</tr>
-<tr class="row-even"><td><p>datafusion.optimizer.repartition_windows</p></td>
+<tr class="row-odd"><td><p>datafusion.optimizer.repartition_windows</p></td>
<td><p>true</p></td>
<td><p>Should DataFusion repartition data using the partitions keys to execute
window functions in parallel using the provided <code class="docutils literal
notranslate"><span class="pre">target_partitions</span></code> level</p></td>
</tr>
-<tr class="row-odd"><td><p>datafusion.optimizer.repartition_sorts</p></td>
+<tr class="row-even"><td><p>datafusion.optimizer.repartition_sorts</p></td>
<td><p>true</p></td>
<td><p>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 <code class="docutils literal
notranslate"><span class="pre">text</span> <span
class="pre">"SortExec:</span> <span class="pre">[a@0</span> <span
class="pre">ASC]",</span> <span class="pre">"</span> <span
class="pre">CoalescePartitionsExec",</span> <span
class="pre">"</span> [...]
</tr>
-<tr class="row-even"><td><p>datafusion.optimizer.prefer_existing_sort</p></td>
+<tr class="row-odd"><td><p>datafusion.optimizer.prefer_existing_sort</p></td>
<td><p>false</p></td>
<td><p>When true, DataFusion will opportunistically remove sorts when the data
is already sorted, (i.e. setting <code class="docutils literal
notranslate"><span class="pre">preserve_order</span></code> to true on <code
class="docutils literal notranslate"><span
class="pre">RepartitionExec</span></code> and using <code class="docutils
literal notranslate"><span class="pre">SortPreservingMergeExec</span></code>)
When false, DataFusion will maximize plan parallelism using <code class="docut
[...]
</tr>
-<tr class="row-odd"><td><p>datafusion.optimizer.skip_failed_rules</p></td>
+<tr class="row-even"><td><p>datafusion.optimizer.skip_failed_rules</p></td>
<td><p>false</p></td>
<td><p>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</p></td>
</tr>
-<tr class="row-even"><td><p>datafusion.optimizer.max_passes</p></td>
+<tr class="row-odd"><td><p>datafusion.optimizer.max_passes</p></td>
<td><p>3</p></td>
<td><p>Number of times that the optimizer will attempt to optimize the
plan</p></td>
</tr>
-<tr
class="row-odd"><td><p>datafusion.optimizer.top_down_join_key_reordering</p></td>
+<tr
class="row-even"><td><p>datafusion.optimizer.top_down_join_key_reordering</p></td>
<td><p>true</p></td>
<td><p>When set to true, the physical plan optimizer will run a top down
process to reorder the join keys</p></td>
</tr>
-<tr class="row-even"><td><p>datafusion.optimizer.prefer_hash_join</p></td>
+<tr class="row-odd"><td><p>datafusion.optimizer.prefer_hash_join</p></td>
<td><p>true</p></td>
<td><p>When set to true, the physical plan optimizer will prefer HashJoin over
SortMergeJoin. HashJoin can work more efficiently than SortMergeJoin but
consumes more memory</p></td>
</tr>
-<tr
class="row-odd"><td><p>datafusion.optimizer.hash_join_single_partition_threshold</p></td>
+<tr
class="row-even"><td><p>datafusion.optimizer.hash_join_single_partition_threshold</p></td>
<td><p>1048576</p></td>
<td><p>The maximum estimated size in bytes for one input side of a HashJoin
will be collected into a single partition</p></td>
</tr>
-<tr class="row-even"><td><p>datafusion.explain.logical_plan_only</p></td>
+<tr class="row-odd"><td><p>datafusion.explain.logical_plan_only</p></td>
<td><p>false</p></td>
<td><p>When set to true, the explain statement will only print logical
plans</p></td>
</tr>
-<tr class="row-odd"><td><p>datafusion.explain.physical_plan_only</p></td>
+<tr class="row-even"><td><p>datafusion.explain.physical_plan_only</p></td>
<td><p>false</p></td>
<td><p>When set to true, the explain statement will only print physical
plans</p></td>
</tr>
-<tr class="row-even"><td><p>datafusion.explain.show_statistics</p></td>
+<tr class="row-odd"><td><p>datafusion.explain.show_statistics</p></td>
<td><p>false</p></td>
<td><p>When set to true, the explain statement will print operator statistics
for physical plans</p></td>
</tr>
-<tr
class="row-odd"><td><p>datafusion.sql_parser.parse_float_as_decimal</p></td>
+<tr
class="row-even"><td><p>datafusion.sql_parser.parse_float_as_decimal</p></td>
<td><p>false</p></td>
<td><p>When set to true, SQL parser will parse float as decimal type</p></td>
</tr>
-<tr
class="row-even"><td><p>datafusion.sql_parser.enable_ident_normalization</p></td>
+<tr
class="row-odd"><td><p>datafusion.sql_parser.enable_ident_normalization</p></td>
<td><p>true</p></td>
<td><p>When set to true, SQL parser will normalize ident (convert ident to
lowercase when not quoted)</p></td>
</tr>
-<tr class="row-odd"><td><p>datafusion.sql_parser.dialect</p></td>
+<tr class="row-even"><td><p>datafusion.sql_parser.dialect</p></td>
<td><p>generic</p></td>
<td><p>Configure the SQL dialect used by DataFusion’s parser; supported values
include: Generic, MySQL, PostgreSQL, Hive, SQLite, Snowflake, Redshift, MsSQL,
ClickHouse, BigQuery, and Ansi.</p></td>
</tr>
