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Caching data in memory in Spark SQL can be done using the spark.catalog.createTable method.
Caching data in memory in Spark SQL can be done using the spark.catalog.createTable method.
False
Spark SQL will automatically compress data in memory to minimize memory usage and GC pressure when caching data.
Spark SQL will automatically compress data in memory to minimize memory usage and GC pressure when caching data.
True
The uncacheTable method is used to add a table to memory in Spark SQL.
The uncacheTable method is used to add a table to memory in Spark SQL.
False
The join method in Spark SQL can be used to specify a join strategy hint.
The join method in Spark SQL can be used to specify a join strategy hint.
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The setConf method on SparkSession can be used to configure in-memory caching in Spark SQL.
The setConf method on SparkSession can be used to configure in-memory caching in Spark SQL.
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Spark SQL can cache data in memory using a row-based format.
Spark SQL can cache data in memory using a row-based format.
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The SHUFFLE_HASH join strategy hint is used to instruct Spark to use a broadcast join strategy.
The SHUFFLE_HASH join strategy hint is used to instruct Spark to use a broadcast join strategy.
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Experimental options can be turned on to improve performance in Spark SQL for certain workloads.
Experimental options can be turned on to improve performance in Spark SQL for certain workloads.
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The MERGE join strategy hint is used to instruct Spark to use a shuffle replicate NL join strategy.
The MERGE join strategy hint is used to instruct Spark to use a shuffle replicate NL join strategy.
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In-memory caching in Spark SQL can be configured using SQL commands.
In-memory caching in Spark SQL can be configured using SQL commands.
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When the BROADCAST hint is used on table 't1', Spark will always choose the broadcast join strategy regardless of the size of table 't1'.
When the BROADCAST hint is used on table 't1', Spark will always choose the broadcast join strategy regardless of the size of table 't1'.
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The SHUFFLE_REPLICATE_NL hint has a higher priority than the MERGE hint in Spark.
The SHUFFLE_REPLICATE_NL hint has a higher priority than the MERGE hint in Spark.
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The 'COALESCE' hint in Spark SQL requires both a partition number and column names as parameters.
The 'COALESCE' hint in Spark SQL requires both a partition number and column names as parameters.
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Adaptive Query Execution (AQE) in Spark SQL is disabled by default since Apache Spark 3.2.0.
Adaptive Query Execution (AQE) in Spark SQL is disabled by default since Apache Spark 3.2.0.
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The coalescing post-shuffle partitions feature in AQE is enabled by default in Spark SQL.
The coalescing post-shuffle partitions feature in AQE is enabled by default in Spark SQL.
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AQE can convert sort-merge join to shuffled hash join when the runtime statistics of any join side is smaller than the adaptive broadcast hash join threshold.
AQE can convert sort-merge join to shuffled hash join when the runtime statistics of any join side is smaller than the adaptive broadcast hash join threshold.
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The spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold configuration determines the threshold for converting sort-merge join to broadcast hash join.
The spark.sql.adaptive.maxShuffledHashJoinLocalMapThreshold configuration determines the threshold for converting sort-merge join to broadcast hash join.
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The skew join optimization feature in AQE can only split skewed tasks into roughly evenly sized tasks.
The skew join optimization feature in AQE can only split skewed tasks into roughly evenly sized tasks.
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The REPARTITION_BY_RANGE hint in Spark SQL must have a partition number as a parameter.
The REPARTITION_BY_RANGE hint in Spark SQL must have a partition number as a parameter.
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The REBALANCE hint in Spark SQL can only have an initial partition number as a parameter.
The REBALANCE hint in Spark SQL can only have an initial partition number as a parameter.
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Study Notes
In-Memory Caching in Spark SQL
- Data caching can be accomplished using
spark.catalog.createTablemethod, optimizing storage and retrieval. - Spark SQL automatically compresses in-memory data to reduce memory usage and garbage collection (GC) pressure.
- The
uncacheTablemethod facilitates removal of a table from cache.
Join Strategies in Spark SQL
- The
joinmethod allows the specification of join strategy hints to improve query performance. -
SHUFFLE_HASHhint directs Spark to utilize a broadcast join strategy for efficient data processing. -
MERGEhint indicates that Spark should perform a shuffle replicate nested loop (NL) join strategy. - The
BROADCASThint enforces the broadcast join strategy for the specified table regardless of its size. - The
SHUFFLE_REPLICATE_NLhint carries higher priority than theMERGEhint, influencing the choice of join strategy.
Configuration and Performance Tuning
-
setConfmethod on SparkSession configures in-memory caching options for enhanced performance. - Caching can be implemented using SQL commands, providing flexibility for users.
Adaptive Query Execution (AQE)
- AQE is disabled by default since Apache Spark 3.2.0, requiring manual activation.
- Coalescing post-shuffle partitions is enabled by default, optimizing query execution.
- AQE can convert sort-merge joins into shuffled hash joins when one join side's runtime statistics are lower than the adaptive broadcast hash join threshold.
- The
spark.sql.adaptive.maxShuffledHashJoinLocalMapThresholdconfiguration sets the threshold for converting sort-merge joins to broadcast hash joins. - Skew join optimization in AQE allows splitting skewed tasks into evenly sized tasks for balanced execution.
Partitioning Hints in Spark SQL
- The
COALESCEhint requires both partition number and column names as input parameters. - The
REPARTITION_BY_RANGEhint mandates a partition number as a parameter for partitioning data. - The
REBALANCEhint also necessitates an initial partition number as a parameter, focusing on redistributing data across partitions.
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Description
Learn how to improve performance in Spark SQL by caching data in memory and using experimental options. This quiz covers the basics of caching tables and tuning compression to minimize memory usage and GC pressure.
