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"envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 6, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.viewcode": 1, "sphinx": 56}}) \ No newline at end of file diff --git a/datafusion/user-guide/configs.html b/datafusion/user-guide/configs.html index 9a36ed979fe5..dad50b571215 100644 --- a/datafusion/user-guide/configs.html +++ b/datafusion/user-guide/configs.html @@ -410,42 +410,62 @@
true
Should DataFusion repartition data using the aggregate keys to execute aggregates in parallel using the provided target_partitions level”
datafusion.optimizer.repartition_joins
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.repartition_file_scans
false
When set to true, file groups will be repartitioned to achieve maximum parallelism. Currently supported only for Parquet format in which case multiple row groups from the same file may be read concurrently. If false then each row group is read serially, though different files may be read in parallel.
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.skip_failed_rules
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 “SortExec: [a@0 ASC]”, ” CoalescePartitionsExec”, ” RepartitionExec: partitioning=RoundRobinBatch(8), input_partitions=1”, would turn into the plan below which performs better in multithreaded environments “SortPreservingMergeExec: [a@0 ASC]”, ” SortExec: [a@0 ASC]”, ” RepartitionExec: partitioning=RoundRobinBatch(8), input_partitions=1”,
datafusion.optimizer.skip_failed_rules
true
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
datafusion.optimizer.max_passes
3
Number of times that the optimizer will attempt to optimize the plan
datafusion.optimizer.top_down_join_key_reordering
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
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
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.explain.logical_plan_only
datafusion.explain.logical_plan_only
false
When set to true, the explain statement will only print logical plans
datafusion.explain.physical_plan_only
datafusion.explain.physical_plan_only
false
When set to true, the explain statement will only print physical plans
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)
join
Join this DataFrame with another DataFrame using the specified columns as join keys.
limit
join_on
Join this DataFrame with another DataFrame using arbitrary expressions.
limit
Limit the number of rows returned from this DataFrame.
repartition
repartition
Repartition a DataFrame based on a logical partitioning scheme.
sort
sort
Sort the DataFrame by the specified sorting expressions. Any expression can be turned into a sort expression by calling its sort method.
select
select
Create a projection based on arbitrary expressions. Example: df..select(vec![col("c1"), abs(col("c2"))])?
select_columns
select_columns
Create a projection based on column names. Example: df.select_columns(&["id", "name"])?.
union
union
Calculate the union of two DataFrames, preserving duplicate rows. The two DataFrames must have exactly the same schema.
union_distinct
union_distinct
Calculate the distinct union of two DataFrames. The two DataFrames must have exactly the same schema.
with_column
with_column
Add an additional column to the DataFrame.
with_column_renamed
with_column_renamed
Rename one column by applying a new projection.
filter accept one or more logical expressions and there are many functions
available for creating logical expressions. These are documented below.
Expressions can be chained together using a fluent-style API:
-// create the expression `(a > 5) AND (b < 7)`
-col("a").gt(lit(5)).and(col("b").lt(lit(7)))
+// create the expression `(a > 6) AND (b < 7)`
+col("a").gt(lit(6)).and(col("b").lt(lit(7)))