This page is generated by RapidsConf.helpAdvanced against Apache Spark 3.3.0. Most of the content in this page is applicable to other versions of Apache Spark with variations

RAPIDS Accelerator for Apache Spark Advanced Configuration

Most users will not need to modify the configuration options listed below. They are documented here for completeness and advanced usage.

The following configuration options are supported by the RAPIDS Accelerator for Apache Spark.

For commonly used configurations and examples of setting options, please refer to the RAPIDS Accelerator for Configuration page.

Advanced Configuration

Name	Description	Default Value	Applicable at
spark.rapids.cgroups.memory.limit.path	The filepath of the local file on host that stores the memory limit for the process. If omitted, attempts to detect the file from common locations.	None	Startup
spark.rapids.cgroups.memory.usage.path	The filepath of the local file on host that stores the memory usage for the process. If omitted, attempts to detect the file from common locations.	None	Startup
spark.rapids.filecache.allowPathRegexp	A regular expression to decide which paths will be cached when the file cache is enabled. If this is not set, then all paths are allowed to cache. If a path is allowed by this regexp but blocked by spark.rapids.filecache.blockPathRegexp, then the path is blocked to cache.	None	Startup
spark.rapids.filecache.blockPathRegexp	A regular expression to decide which paths will not be cached when the file cache is enabled. If a path is blocked by this regexp but is allowed by spark.rapids.filecache.allowPathRegexp, then the path is blocked.	None	Startup
spark.rapids.filecache.checkStale	Controls whether the cached is checked for being out of date with respect to the input file. When enabled, the data that has been cached locally for a file will be invalidated if the file is updated after being cached. This feature is only necessary if an input file for a Spark application can be changed during the lifetime of the application. If an individual input file will not be overwritten during the Spark application then performance may be improved by setting this to false.	true	Startup
spark.rapids.filecache.maxBytes	Controls the maximum amount of data that will be cached locally. If left unspecified, it will use half of the available disk space detected on startup for the configured Spark local disks.	None	Startup
spark.rapids.filecache.minimumFreeSpace.bytes	Specify the minimum amount of free space in the Spark local disks. When the amount of free space on the Spark local disks drops below this value, cache data will be removed automatically to free disk space. A zero or negative value will disable this feature. Note if multiple Spark applications running on the same node, or there are other applications running with heavy disk writing, the filecache may not drop caches in time and may cause full disk errors. Please increase this value for this case.	32212254720	Startup
spark.rapids.filecache.useChecksums	Whether to write out and verify checksums for the cached local files.	false	Startup
spark.rapids.flameGraph.asyncProfiler.options	Spark-RAPIDS plugin uses the async profiler to generate flame graphs. You can specify profiler options via this property. The plugin supports all options except for the 'file' option listed in https://github.com/async-profiler/async-profiler/blob/b3f58429f5c0252e9ced3f0fcb444fed17671321/docs/ProfilerOptions.md#options-applicable-to-any-output-format .The default values is 'jfr,event=cpu,wall=10ms'.	jfr,event=cpu,wall=10ms	Runtime
spark.rapids.flameGraph.executors	Comma-separated list of executors IDs and hyphenated ranges of executor IDs to profile when async-profiler (for flame graph) is enabled. The default value '*' means all executors	*	Runtime
spark.rapids.flameGraph.jfr.compression	Enable compression for JFR files generated by async profiler. When enabled, JFR files will be compressed after generation to save disk space.	false	Runtime
spark.rapids.flameGraph.pathPrefix	Enables collecting flame graph (with async profiler) and specifies a file prefix to use when writing the JFR file by async-profiler. The async-profiler will write a flame graph file for each stage. It is strongly recommended to set 'spark.scheduler.mode' to 'FIFO' so that there is a clean boundary between stages, and then we can better understand each stage.	None	Runtime
spark.rapids.flameGraph.stageEpochInterval	Interval in seconds to determine the current stage epoch based on running task counts. The profiler will check which stage has the most running tasks and profile that stage during each epoch. This allows profiling when multiple stages run concurrently even if FIFO scheduling is already chosen.	5	Runtime
spark.rapids.gpu.resourceName	The name of the Spark resource that represents a GPU that you want the plugin to use if using custom resources with Spark.	gpu	Startup
spark.rapids.iceberg.s3.async.connection-max-idle-time-ms	Connection-max-idle-time (ms) for the AwsCrtAsyncHttpClient used by the spark-rapids Iceberg S3 byte-range reader. No equivalent Iceberg property.	300000	Startup
spark.rapids.iceberg.s3.async.max-concurrency	Max concurrent connections for the AwsCrtAsyncHttpClient used by the spark-rapids Iceberg S3 byte-range reader. Used only when the Iceberg FileIO property s3.crt.max-concurrency is not set.	200	Startup
spark.rapids.iceberg.s3.async.tcp-keepalive-interval-ms	TCP keep-alive probe interval (ms) for the AwsCrtAsyncHttpClient used by the spark-rapids Iceberg S3 byte-range reader. No equivalent Iceberg property.	60000	Startup
spark.rapids.iceberg.s3.async.tcp-keepalive-timeout-ms	TCP keep-alive probe timeout (ms) for the AwsCrtAsyncHttpClient used by the spark-rapids Iceberg S3 byte-range reader. No equivalent Iceberg property.	30000	Startup
spark.rapids.memory.gpu.allocFraction	The fraction of available (free) GPU memory that should be allocated for pooled memory. This must be less than or equal to the maximum limit configured via spark.rapids.memory.gpu.maxAllocFraction, and greater than or equal to the minimum limit configured via spark.rapids.memory.gpu.minAllocFraction.	1.0	Startup
spark.rapids.memory.gpu.debug	Provides a log of GPU memory allocations and frees. If set to STDOUT or STDERR the logging will go there. Setting it to NONE disables logging. All other values are reserved for possible future expansion and in the mean time will disable logging.	NONE	Startup
spark.rapids.memory.gpu.oomDumpDir	The path to a local directory where a heap dump will be created if the GPU encounters an unrecoverable out-of-memory (OOM) error. The filename will be of the form: "gpu-oom--.hprof" where is the process ID, and the dumpId is a sequence number to disambiguate multiple heap dumps per process lifecycle	None	Startup
spark.rapids.memory.gpu.pool	Select the RMM pooling allocator to use. Valid values are "DEFAULT", "ARENA", "ASYNC", and "NONE". With "DEFAULT", the RMM pool allocator is used; with "ARENA", the RMM arena allocator is used; with "ASYNC", the new CUDA stream-ordered memory allocator in CUDA 11.2+ is used. If set to "NONE", pooling is disabled and RMM just passes through to CUDA memory allocation directly.	ASYNC	Startup
spark.rapids.memory.gpu.reserve	The amount of GPU memory that should remain unallocated by RMM and left for system use such as memory needed for kernels and kernel launches.	671088640	Startup
spark.rapids.memory.gpu.state.debug	To better recover from out of memory errors, RMM will track several states for the threads that interact with the GPU. This provides a log of those state transitions to aid in debugging it. STDOUT or STDERR will have the logging go there empty string will disable logging and anything else will be treated as a file to write the logs to.		Startup
spark.rapids.memory.gpu.unspill.enabled	When a spilled GPU buffer is needed again, should it be unspilled, or only copied back into GPU memory temporarily. Unspilling may be useful for GPU buffers that are needed frequently, for example, broadcast variables; however, it may also increase GPU memory usage	false	Startup
spark.rapids.perfio.gcs.enabled	Controls the Google Cloud Storage reader for improved performance in certain queries. When true, enables it and throws at startup if google-cloud-storage classes are not on the classpath. When false, disables it unconditionally. When unset (default), enables it opportunistically if google-cloud-storage classes are found, otherwise falls back to the configured GCS connector with a warning. The presence of com.google.cloud:google-cloud-storage on the executor classpath is required.	None	Startup
spark.rapids.perfio.s3.enabled	Controls the AWS S3 reader for improved performance in certain queries. When true, enables it and throws at startup if no compatible HTTP client is on the classpath. When false, disables it unconditionally. When unset (default), enables it opportunistically if a compatible HTTP client is found, otherwise falls back to S3A with a warning. The presence of AWS SDK packages for Netty and/or CRT HTTP clients on the classpath is required. You can use Spark submit option --packages software.amazon.awssdk:s3:2.22.12,software.amazon.awssdk:aws-crt-client:2.22.12 to achieve this. See https://docs.aws.amazon.com/sdk-for-java/latest/developer-guide/crt-based-s3-client.html#crt-based-s3-client-depend	None	Startup
spark.rapids.python.concurrentPythonWorkers	Set the number of Python worker processes that can execute concurrently per GPU. Python worker processes may temporarily block when the number of concurrent Python worker processes started by the same executor exceeds this amount. Allowing too many concurrent tasks on the same GPU may lead to GPU out of memory errors. >0 means enabled, while <=0 means unlimited	0	Runtime
spark.rapids.python.memory.gpu.allocFraction	The fraction of total GPU memory that should be initially allocated for pooled memory for all the Python workers. It supposes to be less than (1 - $(spark.rapids.memory.gpu.allocFraction)), since the executor will share the GPU with its owning Python workers. Half of the rest will be used if not specified	None	Runtime
spark.rapids.python.memory.gpu.maxAllocFraction	The fraction of total GPU memory that limits the maximum size of the RMM pool for all the Python workers. It supposes to be less than (1 - $(spark.rapids.memory.gpu.maxAllocFraction)), since the executor will share the GPU with its owning Python workers. when setting to 0 it means no limit.	0.0	Runtime
spark.rapids.python.memory.gpu.pooling.enabled	Should RMM in Python workers act as a pooling allocator for GPU memory, or should it just pass through to CUDA memory allocation directly. When not specified, It will honor the value of config 'spark.rapids.memory.gpu.pool', but now only 'DEFAULT' and 'NONE' are supported. If 'ASYNC' or 'ARENA' is specified, it will fall back to 'DEFAULT'.	None	Runtime
spark.rapids.shuffle.enabled	Enable or disable the RAPIDS Shuffle Manager at runtime. The RAPIDS Shuffle Manager must already be configured. When set to false, the built-in Spark shuffle will be used.	true	Runtime
spark.rapids.shuffle.mode	RAPIDS Shuffle Manager mode. "MULTITHREADED": shuffle file writes and reads are parallelized using a thread pool. "UCX": (requires UCX installation) uses accelerated transports for transferring shuffle blocks. "CACHE_ONLY": use when running a single executor, for short-circuit cached shuffle (for testing purposes).	MULTITHREADED	Startup
spark.rapids.shuffle.multiThreaded.maxBytesInFlight	The size limit, in bytes, that the RAPIDS shuffle manager configured in "MULTITHREADED" mode will allow to be serialized or deserialized concurrently per task. This is also the maximum amount of memory that will be used per task. This should be set larger than Spark's default maxBytesInFlight (48MB). The larger this setting is, the more compressed shuffle chunks are processed concurrently. In practice, care needs to be taken to not go over the amount of off-heap memory that Netty has available. See https://github.com/NVIDIA/spark-rapids/issues/9153.	134217728	Startup
spark.rapids.shuffle.multiThreaded.reader.threads	The number of threads to use for reading shuffle blocks per executor in the RAPIDS shuffle manager configured in "MULTITHREADED" mode. There are two special values: 0 = feature is disabled, falls back to Spark built-in shuffle reader; 1 = our implementation of Spark's built-in shuffle reader with extra metrics.	20	Startup
spark.rapids.shuffle.multiThreaded.writer.threads	The number of threads to use for writing shuffle blocks per executor in the RAPIDS shuffle manager configured in "MULTITHREADED" mode. There are two special values: 0 = feature is disabled, falls back to Spark built-in shuffle writer; 1 = our implementation of Spark's built-in shuffle writer with extra metrics.	20	Startup
spark.rapids.shuffle.multithreaded.skipMerge	When using MULTITHREADED shuffle mode, skip merging partial shuffle files and instead serve data directly from the MultithreadedShuffleBufferCatalog. This avoids I/O overhead from merging but requires: (1) External Shuffle Service (ESS) to be disabled, and (2) spark.rapids.memory.host.offHeapLimit.enabled=true (off-heap memory limits enabled) to prevent OOM from unbounded buffer growth. When set to false (default), partial files will be merged into a single shuffle file per map task as in standard Spark shuffle. Set to true when both requirements are met and shuffle data is not reused across SQL queries (e.g., avoid on Databricks with shuffle reuse enabled).	false	Startup
spark.rapids.shuffle.transport.earlyStart	Enable early connection establishment for RAPIDS Shuffle	true	Startup
spark.rapids.shuffle.transport.earlyStart.heartbeatInterval	Shuffle early start heartbeat interval (milliseconds). Executors will send a heartbeat RPC message to the driver at this interval	5000	Startup
spark.rapids.shuffle.transport.earlyStart.heartbeatTimeout	Shuffle early start heartbeat timeout (milliseconds). Executors that don't heartbeat within this timeout will be considered stale. This timeout must be higher than the value for spark.rapids.shuffle.transport.earlyStart.heartbeatInterval	10000	Startup
spark.rapids.shuffle.transport.maxReceiveInflightBytes	Maximum aggregate amount of bytes that be fetched at any given time from peers during shuffle	1073741824	Startup
spark.rapids.shuffle.ucx.activeMessages.forceRndv	Set to true to force 'rndv' mode for all UCX Active Messages. This should only be required with UCX 1.10.x. UCX 1.11.x deployments should set to false.	false	Startup
spark.rapids.shuffle.ucx.managementServerHost	The host to be used to start the management server	null	Startup
spark.rapids.shuffle.ucx.useWakeup	When set to true, use UCX's event-based progress (epoll) in order to wake up the progress thread when needed, instead of a hot loop.	true	Startup
spark.rapids.sql.acceleratedColumnarToRow.enabled	When set to true (default) the GPU columnar-to-row transition uses the GPU transpose kernel (AcceleratedColumnarToRowIterator) for wide fixed-width / STRING schemas. Setting it to false forces the slower per-row CPU iterator (ColumnarToRowIterator). Mainly useful for troubleshooting and performance comparisons; production workloads should leave this on.	true	Runtime
spark.rapids.sql.allowMultipleJars	Allow multiple rapids-4-spark, spark-rapids-jni, and cudf jars on the classpath. Spark will take the first one it finds, so the version may not be expected. Possisble values are ALWAYS: allow all jars, SAME_REVISION: only allow jars with the same revision, NEVER: do not allow multiple jars at all.	SAME_REVISION	Startup
spark.rapids.sql.castDecimalToFloat.enabled	Casting from decimal to floating point types on the GPU returns results that have tiny difference compared to results returned from CPU.	true	Runtime
spark.rapids.sql.castFloatToDecimal.enabled	Casting from floating point types to decimal on the GPU returns results that have tiny difference compared to results returned from CPU.	true	Runtime
spark.rapids.sql.castFloatToIntegralTypes.enabled	Casting from floating point types to integral types on the GPU supports a slightly different range of values when using Spark 3.1.0 or later. Refer to the CAST documentation for more details.	true	Runtime
spark.rapids.sql.castFloatToString.enabled	Casting from floating point types to string on the GPU returns results that have a different precision than the default results of Spark.	true	Runtime
spark.rapids.sql.castStringToFloat.enabled	When set to true, enables casting from strings to float types (float, double) on the GPU. Currently hex values aren't supported on the GPU. Also note that casting from string to float types on the GPU returns incorrect results when the string represents any number "1.7976931348623158E308" <= x < "1.7976931348623159E308" and "-1.7976931348623158E308" >= x > "-1.7976931348623159E308" in both these cases the GPU returns Double.MaxValue while CPU returns "+Infinity" and "-Infinity" respectively	true	Runtime
spark.rapids.sql.castStringToTimestamp.enabled	When set to false, this disables casting from string to timestamp on the GPU.	true	Runtime
spark.rapids.sql.coalescing.reader.numFilterParallel	This controls the number of files the coalescing reader will run in each thread when it filters blocks for reading. If this value is greater than zero the files will be filtered in a multithreaded manner where each thread filters the number of files set by this config. If this is set to zero the files are filtered serially. This uses the same thread pool as the multithreaded reader, see spark.rapids.sql.multiThreadedRead.numThreads.	0	Runtime
spark.rapids.sql.concurrentGpuTasks	Set the initial number of tasks that can execute concurrently per GPU. By default the number of tasks allowed on the GPU will adjust dynamically to try and provide optimal performance. This sets the starting point for each stage. If this is not set the amount of GPU memory will be used to come up with a starting estimate.	None	Runtime
spark.rapids.sql.concurrentGpuTasks.dynamic	Set to false if the system should not dynamically adjust the concurrent task amount, but keep it to be a static number	true	Runtime
spark.rapids.sql.concurrentWriterPartitionFlushSize	The flush size of the concurrent writer cache in bytes for each partition. If specified spark.sql.maxConcurrentOutputFileWriters, use concurrent writer to write data. Concurrent writer first caches data for each partition and begins to flush the data if it finds one partition with a size that is greater than or equal to this config. The default value is 0, which will try to select a size based off of file type specific configs. E.g.: It uses write.parquet.row-group-size-bytes config for Parquet type and orc.stripe.size config for Orc type. If the value is greater than 0, will use this positive value.Max value may get better performance but not always, because concurrent writer uses spillable cache and big value may cause more IO swaps.	0	Runtime
spark.rapids.sql.csv.read.decimal.enabled	CSV reading is not 100% compatible when reading decimals.	false	Runtime
spark.rapids.sql.csv.read.double.enabled	CSV reading is not 100% compatible when reading doubles.	true	Runtime
spark.rapids.sql.csv.read.float.enabled	CSV reading is not 100% compatible when reading floats.	true	Runtime
spark.rapids.sql.decimalOverflowGuarantees	FOR TESTING ONLY. DO NOT USE IN PRODUCTION. Please see the decimal section of the compatibility documents for more information on this config.	true	Runtime
spark.rapids.sql.delta.lowShuffleMerge.deletionVector.broadcast.threshold	Currently we need to broadcast deletion vector to all executors to perform low shuffle merge. When we detect the deletion vector broadcast size is larger than this value, we will fallback to normal shuffle merge.	20971520	Runtime
spark.rapids.sql.delta.lowShuffleMerge.enabled	Option to turn on the low shuffle merge for Delta Lake. Currently there are some limitations for this feature: 1. We only support Databricks Runtime 13.3 and Deltalake 2.4. 2. The file scan mode must be set to PERFILE 3. The deletion vector size must be smaller than spark.rapids.sql.delta.lowShuffleMerge.deletionVector.broadcast.threshold	false	Runtime
spark.rapids.sql.detectDeltaCheckpointQueries	Queries against Delta Lake _delta_log checkpoint Parquet files are not efficient on the GPU. When this option is enabled, the plugin will attempt to detect these queries and fall back to the CPU.	true	Runtime
spark.rapids.sql.detectDeltaLogQueries	Queries against Delta Lake _delta_log JSON files are not efficient on the GPU. When this option is enabled, the plugin will attempt to detect these queries and fall back to the CPU.	true	Runtime
spark.rapids.sql.exec.opTimeTrackingRDD.enabled	Enable OpTimeTrackingRDD for all GPU operations. When true, OpTimeTrackingRDD wrappers will be created to track operation time. When false, can improve performance by avoiding overhead of operation time tracking.	true	Runtime
spark.rapids.sql.expression.cpuBridge.enabled	Enable CPU-GPU bridge expressions that allow CPU expression subtrees to run while keeping the overall plan on GPU. When enabled, expressions that have no GPU implementation will automatically be wrapped in bridge expressions instead of causing plan fallbacks.	true	Runtime
spark.rapids.sql.fast.sample	Option to turn on fast sample. If enable it is inconsistent with CPU sample because of GPU sample algorithm is inconsistent with CPU.	false	Runtime
spark.rapids.sql.format.avro.enabled	When set to true enables all avro input and output acceleration. (only input is currently supported anyways)	false	Runtime
spark.rapids.sql.format.avro.multiThreadedRead.maxNumFilesParallel	A limit on the maximum number of files per task processed in parallel on the CPU side before the file is sent to the GPU. This affects the amount of host memory used when reading the files in parallel. Used with MULTITHREADED reader, see spark.rapids.sql.format.avro.reader.type.	2147483647	Runtime
spark.rapids.sql.format.avro.multiThreadedRead.numThreads	The maximum number of threads, on one executor, to use for reading small Avro files in parallel. This can not be changed at runtime after the executor has started. Used with MULTITHREADED reader, see spark.rapids.sql.format.avro.reader.type. DEPRECATED: use spark.rapids.sql.multiThreadedRead.numThreads	None	Startup
spark.rapids.sql.format.avro.read.enabled	When set to true enables avro input acceleration	false	Runtime
spark.rapids.sql.format.avro.reader.type	Sets the Avro reader type. We support different types that are optimized for different environments. The original Spark style reader can be selected by setting this to PERFILE which individually reads and copies files to the GPU. Loading many small files individually has high overhead, and using either COALESCING or MULTITHREADED is recommended instead. The COALESCING reader is good when using a local file system where the executors are on the same nodes or close to the nodes the data is being read on. This reader coalesces all the files assigned to a task into a single host buffer before sending it down to the GPU. It copies blocks from a single file into a host buffer in separate threads in parallel, see spark.rapids.sql.multiThreadedRead.numThreads. MULTITHREADED is good for cloud environments where you are reading from a blobstore that is totally separate and likely has a higher I/O read cost. Many times the cloud environments also get better throughput when you have multiple readers in parallel. This reader uses multiple threads to read each file in parallel and each file is sent to the GPU separately. This allows the CPU to keep reading while GPU is also doing work. See spark.rapids.sql.multiThreadedRead.numThreads and spark.rapids.sql.format.avro.multiThreadedRead.maxNumFilesParallel to control the number of threads and amount of memory used. By default this is set to AUTO so we select the reader we think is best. This will either be the COALESCING or the MULTITHREADED based on whether we think the file is in the cloud. See spark.rapids.cloudSchemes.	AUTO	Runtime
spark.rapids.sql.format.csv.enabled	When set to false disables all csv input and output acceleration. (only input is currently supported anyways)	true	Runtime
spark.rapids.sql.format.csv.read.enabled	When set to false disables csv input acceleration	true	Runtime
spark.rapids.sql.format.delta.write.enabled	When set to false disables Delta Lake output acceleration.	true	Runtime
spark.rapids.sql.format.hive.text.enabled	When set to false disables Hive text table acceleration. Array/Struct/Map columns are unsupported for acceleration.	true	Runtime
spark.rapids.sql.format.hive.text.read.decimal.enabled	Hive text file reading is not 100% compatible when reading decimals. Hive has more limitations on what is valid compared to the GPU implementation in some corner cases. See https://github.com/NVIDIA/spark-rapids/issues/7246	true	Runtime
spark.rapids.sql.format.hive.text.read.double.enabled	Hive text file reading is not 100% compatible when reading doubles.	true	Runtime
spark.rapids.sql.format.hive.text.read.enabled	When set to false disables Hive text table read acceleration. Array/Struct/Map columns are unsupported for read acceleration.	true	Runtime
spark.rapids.sql.format.hive.text.read.float.enabled	Hive text file reading is not 100% compatible when reading floats.	true	Runtime
spark.rapids.sql.format.hive.text.write.enabled	When set to false disables Hive text table write acceleration. Array/Struct/Map columns are unsupported for write acceleration.	true	Runtime
spark.rapids.sql.format.iceberg.enabled	When set to false disables all Iceberg acceleration	true	Runtime
spark.rapids.sql.format.iceberg.read.enabled	When set to false disables Iceberg input acceleration	true	Runtime
spark.rapids.sql.format.iceberg.write.enabled	When set to false disables Iceberg write acceleration	true	Runtime
spark.rapids.sql.format.json.enabled	When set to true enables all json input and output acceleration. (only input is currently supported anyways)	true	Runtime
spark.rapids.sql.format.json.read.enabled	When set to true enables json input acceleration	true	Runtime
spark.rapids.sql.format.orc.enabled	When set to false disables all orc input and output acceleration	true	Runtime
spark.rapids.sql.format.orc.floatTypesToString.enable	When reading an ORC file, the source data schemas(schemas of ORC file) may differ from the target schemas (schemas of the reader), we need to handle the castings from source type to target type. Since float/double numbers in GPU have different precision with CPU, when casting float/double to string, the result of GPU is different from result of CPU spark. Its default value is true (this means the strings result will differ from result of CPU). If it's set false explicitly and there exists casting from float/double to string in the job, then such behavior will cause an exception, and the job will fail.	true	Runtime
spark.rapids.sql.format.orc.multiThreadedRead.maxNumFilesParallel	A limit on the maximum number of files per task processed in parallel on the CPU side before the file is sent to the GPU. This affects the amount of host memory used when reading the files in parallel. Used with MULTITHREADED reader, see spark.rapids.sql.format.orc.reader.type.	2147483647	Runtime
spark.rapids.sql.format.orc.multiThreadedRead.numThreads	The maximum number of threads, on the executor, to use for reading small ORC files in parallel. This can not be changed at runtime after the executor has started. Used with MULTITHREADED reader, see spark.rapids.sql.format.orc.reader.type. DEPRECATED: use spark.rapids.sql.multiThreadedRead.numThreads	None	Startup
spark.rapids.sql.format.orc.read.enabled	When set to false disables orc input acceleration	true	Runtime
spark.rapids.sql.format.orc.reader.type	Sets the ORC reader type. We support different types that are optimized for different environments. The original Spark style reader can be selected by setting this to PERFILE which individually reads and copies files to the GPU. Loading many small files individually has high overhead, and using either COALESCING or MULTITHREADED is recommended instead. The COALESCING reader is good when using a local file system where the executors are on the same nodes or close to the nodes the data is being read on. This reader coalesces all the files assigned to a task into a single host buffer before sending it down to the GPU. It copies blocks from a single file into a host buffer in separate threads in parallel, see spark.rapids.sql.multiThreadedRead.numThreads. MULTITHREADED is good for cloud environments where you are reading from a blobstore that is totally separate and likely has a higher I/O read cost. Many times the cloud environments also get better throughput when you have multiple readers in parallel. This reader uses multiple threads to read each file in parallel and each file is sent to the GPU separately. This allows the CPU to keep reading while GPU is also doing work. See spark.rapids.sql.multiThreadedRead.numThreads and spark.rapids.sql.format.orc.multiThreadedRead.maxNumFilesParallel to control the number of threads and amount of memory used. By default this is set to AUTO so we select the reader we think is best. This will either be the COALESCING or the MULTITHREADED based on whether we think the file is in the cloud. See spark.rapids.cloudSchemes.	AUTO	Runtime
spark.rapids.sql.format.orc.write.enabled	When set to false disables orc output acceleration	true	Runtime
spark.rapids.sql.format.parquet.enabled	When set to false disables all parquet input and output acceleration	true	Runtime
spark.rapids.sql.format.parquet.multiThreadedRead.maxNumFilesParallel	A limit on the maximum number of files per task processed in parallel on the CPU side before the file is sent to the GPU. This affects the amount of host memory used when reading the files in parallel. Used with MULTITHREADED reader, see spark.rapids.sql.format.parquet.reader.type.	2147483647	Runtime
spark.rapids.sql.format.parquet.multiThreadedRead.numThreads	The maximum number of threads, on the executor, to use for reading small Parquet files in parallel. This can not be changed at runtime after the executor has started. Used with COALESCING and MULTITHREADED reader, see spark.rapids.sql.format.parquet.reader.type. DEPRECATED: use spark.rapids.sql.multiThreadedRead.numThreads	None	Startup
spark.rapids.sql.format.parquet.multithreaded.combine.sizeBytes	The target size in bytes to combine multiple small files together when using the MULTITHREADED parquet reader. With combine disabled, the MULTITHREADED reader reads the files in parallel and sends individual files down to the GPU, but that can be inefficient for small files. When combine is enabled, files that are ready within spark.rapids.sql.format.parquet.multithreaded.combine.waitTime together, up to this threshold size, are combined before sending down to GPU. This can be disabled by setting it to 0. Note that combine also will not go over the spark.rapids.sql.reader.batchSizeRows or spark.rapids.sql.reader.batchSizeBytes limits. DEPRECATED: use spark.rapids.sql.reader.multithreaded.combine.sizeBytes instead.	None	Runtime
spark.rapids.sql.format.parquet.multithreaded.combine.waitTime	When using the multithreaded parquet reader with combine mode, how long to wait, in milliseconds, for more files to finish if haven't met the size threshold. Note that this will wait this amount of time from when the last file was available, so total wait time could be larger then this. DEPRECATED: use spark.rapids.sql.reader.multithreaded.combine.waitTime instead.	None	Runtime
spark.rapids.sql.format.parquet.multithreaded.read.keepOrder	When using the MULTITHREADED reader, if this is set to true we read the files in the same order Spark does, otherwise the order may not be the same. DEPRECATED: use spark.rapids.sql.reader.multithreaded.read.keepOrder instead.	None	Runtime
spark.rapids.sql.format.parquet.read.enabled	When set to false disables parquet input acceleration	true	Runtime
spark.rapids.sql.format.parquet.reader.footer.type	In some cases reading the footer of the file is very expensive. Typically this happens when there are a large number of columns and relatively few of them are being read on a large number of files. This provides the ability to use a different path to parse and filter the footer. AUTO is the default and decides which path to take using a heuristic. JAVA follows closely with what Apache Spark does. NATIVE will parse and filter the footer using C++.	AUTO	Runtime
spark.rapids.sql.format.parquet.reader.type	Sets the Parquet reader type. We support different types that are optimized for different environments. The original Spark style reader can be selected by setting this to PERFILE which individually reads and copies files to the GPU. Loading many small files individually has high overhead, and using either COALESCING or MULTITHREADED is recommended instead. The COALESCING reader is good when using a local file system where the executors are on the same nodes or close to the nodes the data is being read on. This reader coalesces all the files assigned to a task into a single host buffer before sending it down to the GPU. It copies blocks from a single file into a host buffer in separate threads in parallel, see spark.rapids.sql.multiThreadedRead.numThreads. MULTITHREADED is good for cloud environments where you are reading from a blobstore that is totally separate and likely has a higher I/O read cost. Many times the cloud environments also get better throughput when you have multiple readers in parallel. This reader uses multiple threads to read each file in parallel and each file is sent to the GPU separately. This allows the CPU to keep reading while GPU is also doing work. See spark.rapids.sql.multiThreadedRead.numThreads and spark.rapids.sql.format.parquet.multiThreadedRead.maxNumFilesParallel to control the number of threads and amount of memory used. By default this is set to AUTO so we select the reader we think is best. This will either be the COALESCING or the MULTITHREADED based on whether we think the file is in the cloud. See spark.rapids.cloudSchemes.	AUTO	Runtime
spark.rapids.sql.format.parquet.write.enabled	When set to false disables parquet output acceleration	true	Runtime
spark.rapids.sql.format.parquet.writer.int96.enabled	When set to false, disables accelerated parquet write if the spark.sql.parquet.outputTimestampType is set to INT96	true	Runtime
spark.rapids.sql.formatNumberFloat.enabled	format_number with floating point types on the GPU returns results that have a different precision than the default results of Spark.	true	Runtime
spark.rapids.sql.hasExtendedYearValues	Spark 3.2.0+ extended parsing of years in dates and timestamps to support the full range of possible values. Prior to this it was limited to a positive 4 digit year. The Accelerator does not support the extended range yet. This config indicates if your data includes this extended range or not, or if you don't care about getting the correct values on values with the extended range.	true	Runtime
spark.rapids.sql.hashOptimizeSort.enabled	Whether sorts should be inserted after some hashed operations to improve output ordering. This can improve output file sizes when saving to columnar formats.	false	Runtime
spark.rapids.sql.improvedFloatOps.enabled	For some floating point operations spark uses one way to compute the value and the underlying cudf implementation can use an improved algorithm. In some cases this can result in cudf producing an answer when spark overflows.	true	Runtime
spark.rapids.sql.incompatibleDateFormats.enabled	When parsing strings as dates and timestamps in functions like unix_timestamp, some formats are fully supported on the GPU and some are unsupported and will fall back to the CPU. Some formats behave differently on the GPU than the CPU. Spark on the CPU interprets date formats with unsupported trailing characters as nulls, while Spark on the GPU will parse the date with invalid trailing characters. More detail can be found at parsing strings as dates or timestamps.	false	Runtime
spark.rapids.sql.incompatibleOps.enabled	For operations that work, but are not 100% compatible with the Spark equivalent set if they should be enabled by default or disabled by default.	true	Runtime
spark.rapids.sql.join.cross.enabled	When set to true cross joins are enabled on the GPU	true	Runtime
spark.rapids.sql.join.existence.enabled	When set to true existence joins are enabled on the GPU	true	Runtime
spark.rapids.sql.join.fullOuter.enabled	When set to true full outer joins are enabled on the GPU	true	Runtime
spark.rapids.sql.join.inner.enabled	When set to true inner joins are enabled on the GPU	true	Runtime
spark.rapids.sql.join.leftAnti.enabled	When set to true left anti joins are enabled on the GPU	true	Runtime
spark.rapids.sql.join.leftOuter.enabled	When set to true left outer joins are enabled on the GPU	true	Runtime
spark.rapids.sql.join.leftSemi.enabled	When set to true left semi joins are enabled on the GPU	true	Runtime
spark.rapids.sql.join.rightOuter.enabled	When set to true right outer joins are enabled on the GPU	true	Runtime
spark.rapids.sql.json.read.datetime.enabled	JSON reading is not 100% compatible when reading dates and timestamps.	false	Runtime
spark.rapids.sql.json.read.decimal.enabled	When reading a quoted string as a decimal Spark supports reading non-ascii unicode digits, and the RAPIDS Accelerator does not.	true	Runtime
spark.rapids.sql.json.read.double.enabled	JSON reading is not 100% compatible when reading doubles.	true	Runtime
spark.rapids.sql.json.read.float.enabled	JSON reading is not 100% compatible when reading floats.	true	Runtime
spark.rapids.sql.lore.dumpPath	The path to dump the LORE nodes' input data. This must be set if spark.rapids.sql.lore.idsToDump has been set. The data of each LORE node will be dumped to a subfolder with name 'loreId-' under this path. For more details, please refer to the LORE documentation.	None	Runtime
spark.rapids.sql.lore.idsToDump	Specify the LORE ids of operators to dump. The format is a comma separated list of LORE ids. For example: "1[0]" will dump partition 0 of input of gpu operator with lore id 1. For more details, please refer to the LORE documentation. If this is not set, no data will be dumped.	None	Runtime
spark.rapids.sql.lore.nonStrictMode.enabled	Allow LoRE dumping to continue when a selected lore id fails. When enabled, failing lore ids are skipped with a warning, previously dumped data under the dump path is kept, and the rest of the query continues executing.	false	Runtime
spark.rapids.sql.lore.parquet.useOriginalSchemaNames	When enabled, LORE writes Parquet files using the original Spark schema names instead of auto-generated type-based names. This makes the dumped Parquet data easier to consume directly via Spark/other tools.	true	Runtime
spark.rapids.sql.mode	Set the mode for the Rapids Accelerator. The supported modes are explainOnly and executeOnGPU. This config can not be changed at runtime, you must restart the application for it to take affect. The default mode is executeOnGPU, which means the RAPIDS Accelerator plugin convert the Spark operations and execute them on the GPU when possible. The explainOnly mode allows running queries on the CPU and the RAPIDS Accelerator will evaluate the queries as if it was going to run on the GPU. The explanations of what would have run on the GPU and why are output in log messages. When using explainOnly mode, the default explain output is ALL, this can be changed by setting spark.rapids.sql.explain. See that config for more details.	executeongpu	Startup
spark.rapids.sql.optimizer.joinReorder.enabled	When enabled, joins may be reordered for improved query performance	true	Runtime
spark.rapids.sql.python.gpu.enabled	This is an experimental feature and is likely to change in the future. Enable (true) or disable (false) support for scheduling Python Pandas UDFs with GPU resources. When enabled, pandas UDFs are assumed to share the same GPU that the RAPIDs accelerator uses and will honor the python GPU configs	false	Runtime
spark.rapids.sql.reader.chunked	Enable a chunked reader where possible. A chunked reader allows reading highly compressed data that could not be read otherwise, but at the expense of more GPU memory, and in some cases more GPU computation. Currently this only supports ORC and Parquet formats.	true	Runtime
spark.rapids.sql.reader.chunked.limitMemoryUsage	Enable a soft limit on the internal memory usage of the chunked reader (if being used). Such limit is calculated as the multiplication of 'spark.rapids.sql.batchSizeBytes' and 'spark.rapids.sql.reader.chunked.memoryUsageRatio'.For example, if batchSizeBytes is set to 1GB and memoryUsageRatio is 4, the chunked reader will try to keep its memory usage under 4GB.	None	Runtime
spark.rapids.sql.reader.chunked.subPage	Enable a chunked reader where possible for reading data that is smaller than the typical row group/page limit. Currently deprecated and replaced by 'spark.rapids.sql.reader.chunked.limitMemoryUsage'.	None	Runtime
spark.rapids.sql.reader.multithreaded.combine.sizeBytes	The target size in bytes to combine multiple small files together when using the MULTITHREADED parquet or orc reader. With combine disabled, the MULTITHREADED reader reads the files in parallel and sends individual files down to the GPU, but that can be inefficient for small files. When combine is enabled, files that are ready within spark.rapids.sql.reader.multithreaded.combine.waitTime together, up to this threshold size, are combined before sending down to GPU. This can be disabled by setting it to 0. Note that combine also will not go over the spark.rapids.sql.reader.batchSizeRows or spark.rapids.sql.reader.batchSizeBytes limits.	67108864	Runtime
spark.rapids.sql.reader.multithreaded.combine.waitTime	When using the multithreaded parquet or orc reader with combine mode, how long to wait, in milliseconds, for more files to finish if haven't met the size threshold. Note that this will wait this amount of time from when the last file was available, so total wait time could be larger then this.	200	Runtime
spark.rapids.sql.reader.multithreaded.read.keepOrder	When using the MULTITHREADED reader, if this is set to true we read the files in the same order Spark does, otherwise the order may not be the same. Now it is supported only for parquet and orc.	true	Runtime
spark.rapids.sql.regexp.enabled	Specifies whether supported regular expressions will be evaluated on the GPU. Unsupported expressions will fall back to CPU. However, there are some known edge cases that will still execute on GPU and produce incorrect results and these are documented in the compatibility guide. Setting this config to false will make all regular expressions run on the CPU instead.	true	Runtime
spark.rapids.sql.replaceSortMergeJoin.enabled	Allow replacing sortMergeJoin with HashJoin	true	Runtime
spark.rapids.sql.rowBasedUDF.enabled	Deprecated. When set to true, enables the legacy row-based UDF path in a GPU operation by transferring only the data it needs between GPU and CPU inside a query operation, instead of falling this operation back to CPU. CPU bridge is the preferred replacement when it can safely bridge the expression, and this config might be removed in the future.	false	Runtime
spark.rapids.sql.stableSort.enabled	Enable or disable stable sorting. Apache Spark's sorting is typically a stable sort, but sort stability cannot be guaranteed in distributed work loads because the order in which upstream data arrives to a task is not guaranteed. Sort stability then only matters when reading and sorting data from a file using a single task/partition. Because of limitations in the plugin when you enable stable sorting all of the data for a single task will be combined into a single batch before sorting. This currently disables spilling from GPU memory if the data size is too large.	false	Runtime
spark.rapids.sql.suppressPlanningFailure	Option to fallback an individual query to CPU if an unexpected condition prevents the query plan from being converted to a GPU-enabled one. Note this is different from a normal CPU fallback for a yet-to-be-supported Spark SQL feature. If this happens the error should be reported and investigated as a GitHub issue.	false	Runtime
spark.rapids.sql.variableFloatAgg.enabled	Spark assumes that all operations produce the exact same result each time. This is not true for some floating point aggregations, which can produce slightly different results on the GPU as the aggregation is done in parallel. This can enable those operations if you know the query is only computing it once.	true	Runtime
spark.rapids.sql.window.batched.bounded.row.max	Max value for bounded row window preceding/following extents permissible for the window to be evaluated in batched mode. This value affects both the preceding and following bounds, potentially doubling the window size permitted for batched execution	100	Runtime
spark.rapids.sql.window.collectList.enabled	The output size of collect list for a window operation is proportional to the window size squared. The current GPU implementation does not handle this well and is disabled by default. If you know that your window size is very small you can try to enable it.	false	Runtime
spark.rapids.sql.window.collectSet.enabled	The output size of collect set for a window operation can be proportional to the window size squared. The current GPU implementation does not handle this well and is disabled by default. If you know that your window size is very small you can try to enable it.	false	Runtime
spark.rapids.sql.window.range.byte.enabled	When the order-by column of a range based window is byte type and the range boundary calculated for a value has overflow, CPU and GPU will get the different results. When set to false disables the range window acceleration for the byte type order-by column	false	Runtime
spark.rapids.sql.window.range.decimal.enabled	When set to false, this disables the range window acceleration for the DECIMAL type order-by column	true	Runtime
spark.rapids.sql.window.range.double.enabled	When set to false, this disables the range window acceleration for the double type order-by column	true	Runtime
spark.rapids.sql.window.range.float.enabled	When set to false, this disables the range window acceleration for the FLOAT type order-by column	true	Runtime
spark.rapids.sql.window.range.int.enabled	When the order-by column of a range based window is int type and the range boundary calculated for a value has overflow, CPU and GPU will get the different results. When set to false disables the range window acceleration for the int type order-by column	true	Runtime
spark.rapids.sql.window.range.long.enabled	When the order-by column of a range based window is long type and the range boundary calculated for a value has overflow, CPU and GPU will get the different results. When set to false disables the range window acceleration for the long type order-by column	true	Runtime
spark.rapids.sql.window.range.short.enabled	When the order-by column of a range based window is short type and the range boundary calculated for a value has overflow, CPU and GPU will get the different results. When set to false disables the range window acceleration for the short type order-by column	false	Runtime

Supported GPU Operators and Fine Tuning

The RAPIDS Accelerator for Apache Spark can be configured to enable or disable specific GPU accelerated expressions. Enabled expressions are candidates for GPU execution. If the expression is configured as disabled, the accelerator plugin will not attempt replacement, and it will run on the CPU.

Please leverage the spark.rapids.sql.explain setting to get feedback from the plugin as to why parts of a query may not be executing on the GPU.

NOTE: Setting spark.rapids.sql.incompatibleOps.enabled=true will enable all the settings in the table below which are not enabled by default due to incompatibilities.

Expressions

Name	SQL Function(s)	Description	Default Value	Notes
spark.rapids.sql.expression.Abs	abs	Absolute value	true	None
spark.rapids.sql.expression.Acos	acos	Inverse cosine	true	None
spark.rapids.sql.expression.Acosh	acosh	Inverse hyperbolic cosine	true	None
spark.rapids.sql.expression.Add	+	Addition	true	None
spark.rapids.sql.expression.Alias		Gives a column a name	true	None
spark.rapids.sql.expression.And	and	Logical AND	true	None
spark.rapids.sql.expression.AnsiCast		Convert a column of one type of data into another type	true	None
spark.rapids.sql.expression.ArrayAggregate	aggregate	Aggregate elements in an array using an accumulator function and finishing transformation. Currently only lambdas of the form (acc, x) -> op(acc, g(x)) with an identity finish are executed on the GPU, where op is one of SUM/PRODUCT/MAX/MIN/ALL/ANY. If/CaseWhen branches are accepted as long as each branch is itself op-of-acc (or bare acc) with op consistent across branches; other shapes fall back to CPU. SUM/PRODUCT on float/double share the same parallel-reduction non-determinism as GpuSum and are gated by spark.rapids.sql.variableFloatAgg.enabled. SUM/PRODUCT on integer/decimal in ANSI mode fall back to CPU because cuDF segmented reduce wraps on overflow instead of raising.	true	None
spark.rapids.sql.expression.ArrayContains	array_contains	Returns a boolean if the array contains the passed in key	true	None
spark.rapids.sql.expression.ArrayDistinct	array_distinct	Removes duplicate values from the array	true	None
spark.rapids.sql.expression.ArrayExcept	array_except	Returns an array of the elements in array1 but not in array2, without duplicates	true	This is not 100% compatible with the Spark version because the GPU implementation treats -0.0 and 0.0 as equal, but the CPU implementation currently does not (see SPARK-39845). Also, Apache Spark 3.1.3 fixed issue SPARK-36741 where NaNs in these set like operators were not treated as being equal. We have chosen to break with compatibility for the older versions of Spark in this instance and handle NaNs the same as 3.1.3+
spark.rapids.sql.expression.ArrayExists	exists	Return true if any element satisfies the predicate LambdaFunction	true	None
spark.rapids.sql.expression.ArrayFilter	filter	Filter an input array using a given predicate	true	None
spark.rapids.sql.expression.ArrayIntersect	array_intersect	Returns an array of the elements in the intersection of array1 and array2, without duplicates	true	This is not 100% compatible with the Spark version because the GPU implementation treats -0.0 and 0.0 as equal, but the CPU implementation currently does not (see SPARK-39845). Also, Apache Spark 3.1.3 fixed issue SPARK-36741 where NaNs in these set like operators were not treated as being equal. We have chosen to break with compatibility for the older versions of Spark in this instance and handle NaNs the same as 3.1.3+
spark.rapids.sql.expression.ArrayJoin	array_join	Concatenates the elements of the given array using the delimiter and an optional string to replace nulls. If no value is set for nullReplacement, any null value is filtered.	true	None
spark.rapids.sql.expression.ArrayMax	array_max	Returns the maximum value in the array	true	None
spark.rapids.sql.expression.ArrayMin	array_min	Returns the minimum value in the array	true	None
spark.rapids.sql.expression.ArrayPosition	array_position	Returns the (1-based) index of the first matching element of the array as long, or 0 if no match is found.	true	None
spark.rapids.sql.expression.ArrayRemove	array_remove	Returns the array after removing all elements that equal to the input element (right) from the input array (left)	true	None
spark.rapids.sql.expression.ArrayRepeat	array_repeat	Returns the array containing the given input value (left) count (right) times	true	None
spark.rapids.sql.expression.ArraySort	array_sort	Sorts the input array in ascending order with nulls last according to the natural ordering of the elements (the default comparator). A custom comparator, or a nested element type (array or struct), falls back to the CPU	true	None
spark.rapids.sql.expression.ArrayTransform	transform	Transform elements in an array using the transform function. This is similar to a map in functional programming	true	None
spark.rapids.sql.expression.ArrayUnion	array_union	Returns an array of the elements in the union of array1 and array2, without duplicates.	true	This is not 100% compatible with the Spark version because the GPU implementation treats -0.0 and 0.0 as equal, but the CPU implementation currently does not (see SPARK-39845). Also, Apache Spark 3.1.3 fixed issue SPARK-36741 where NaNs in these set like operators were not treated as being equal. We have chosen to break with compatibility for the older versions of Spark in this instance and handle NaNs the same as 3.1.3+
spark.rapids.sql.expression.ArraysOverlap	arrays_overlap	Returns true if a1 contains at least a non-null element present also in a2. If the arrays have no common element and they are both non-empty and either of them contains a null element null is returned, false otherwise.	true	This is not 100% compatible with the Spark version because the GPU implementation treats -0.0 and 0.0 as equal, but the CPU implementation currently does not (see SPARK-39845). Also, Apache Spark 3.1.3 fixed issue SPARK-36741 where NaNs in these set like operators were not treated as being equal. We have chosen to break with compatibility for the older versions of Spark in this instance and handle NaNs the same as 3.1.3+
spark.rapids.sql.expression.ArraysZip	arrays_zip	Returns a merged array of structs in which the N-th struct contains all N-th values of input arrays.	true	None
spark.rapids.sql.expression.Ascii	ascii	The numeric value of the first character of string data.	false	This is disabled by default because it only supports strings starting with ASCII or Latin-1 characters after Spark 3.2.3, 3.3.1 and 3.4.0. Otherwise the results will not match the CPU.
spark.rapids.sql.expression.Asin	asin	Inverse sine	true	None
spark.rapids.sql.expression.Asinh	asinh	Inverse hyperbolic sine	true	None
spark.rapids.sql.expression.AtLeastNNonNulls		Checks if number of non null/Nan values is greater than a given value	true	None
spark.rapids.sql.expression.Atan	atan	Inverse tangent	true	None
spark.rapids.sql.expression.Atanh	atanh	Inverse hyperbolic tangent	true	None
spark.rapids.sql.expression.AttributeReference		References an input column	true	None
spark.rapids.sql.expression.BRound	bround	Round an expression to d decimal places using HALF_EVEN rounding mode	true	None
spark.rapids.sql.expression.Bin	bin	Returns the string representation of the long value expr represented in binary	true	None
spark.rapids.sql.expression.BitLength	bit_length	The bit length of string data	true	None
spark.rapids.sql.expression.BitwiseAnd	&	Returns the bitwise AND of the operands	true	None
spark.rapids.sql.expression.BitwiseCount	bit_count	Returns the number of bits that are set in the input as unsigned 64-bit integer	true	None
spark.rapids.sql.expression.BitwiseNot	~	Returns the bitwise NOT of the operands	true	None
spark.rapids.sql.expression.BitwiseOr	|	Returns the bitwise OR of the operands	true	None
spark.rapids.sql.expression.BitwiseXor	^	Returns the bitwise XOR of the operands	true	None
spark.rapids.sql.expression.BloomFilterMightContain		Bloom filter query	true	None
spark.rapids.sql.expression.BoundReference		Reference to a bound variable	true	None
spark.rapids.sql.expression.CaseWhen	when	CASE WHEN expression	true	None
spark.rapids.sql.expression.Cast	bigint, binary, boolean, cast, date, decimal, double, float, int, smallint, string, timestamp, tinyint	Convert a column of one type of data into another type	true	None
spark.rapids.sql.expression.Cbrt	cbrt	Cube root	true	None
spark.rapids.sql.expression.Ceil		Ceiling of a number	true	None
spark.rapids.sql.expression.CheckOverflow		CheckOverflow after arithmetic operations between DecimalType data	true	None
spark.rapids.sql.expression.Coalesce	coalesce	Returns the first non-null argument if exists. Otherwise, null	true	None
spark.rapids.sql.expression.Concat	concat	List/String concatenate	true	None
spark.rapids.sql.expression.ConcatWs	concat_ws	Concatenates multiple input strings or array of strings into a single string using a given separator	true	None
spark.rapids.sql.expression.Contains		Contains	true	None
spark.rapids.sql.expression.Conv	conv	Convert string representing a number from one base to another	true	None
spark.rapids.sql.expression.Cos	cos	Cosine	true	None
spark.rapids.sql.expression.Cosh	cosh	Hyperbolic cosine	true	None
spark.rapids.sql.expression.Cot	cot	Cotangent	true	None
spark.rapids.sql.expression.CreateArray	array	Returns an array with the given elements	true	None
spark.rapids.sql.expression.CreateMap	map	Create a map	true	None
spark.rapids.sql.expression.CreateNamedStruct	named_struct, struct	Creates a struct with the given field names and values	true	None
spark.rapids.sql.expression.CurrentRow$		Special boundary for a window frame, indicating stopping at the current row	true	None
spark.rapids.sql.expression.DateAdd	date_add	Returns the date that is num_days after start_date	true	None
spark.rapids.sql.expression.DateAddInterval		Adds interval to date	true	None
spark.rapids.sql.expression.DateDiff	datediff	Returns the number of days from startDate to endDate	true	None
spark.rapids.sql.expression.DateFormatClass	date_format	Converts timestamp to a value of string in the format specified by the date format	true	None
spark.rapids.sql.expression.DateSub	date_sub	Returns the date that is num_days before start_date	true	None
spark.rapids.sql.expression.DayOfMonth	day, dayofmonth	Returns the day of the month from a date or timestamp	true	None
spark.rapids.sql.expression.DayOfWeek	dayofweek	Returns the day of the week (1 = Sunday...7=Saturday)	true	None
spark.rapids.sql.expression.DayOfYear	dayofyear	Returns the day of the year from a date or timestamp	true	None
spark.rapids.sql.expression.DenseRank	dense_rank	Window function that returns the dense rank value within the aggregation window	true	None
spark.rapids.sql.expression.Divide	/	Division	true	None
spark.rapids.sql.expression.DivideDTInterval		Day-time interval * operator	true	None
spark.rapids.sql.expression.DivideYMInterval		Year-month interval / number	true	None
spark.rapids.sql.expression.DynamicPruningExpression		Dynamic pruning expression marker	true	None
spark.rapids.sql.expression.ElementAt	element_at	Returns element of array at given(1-based) index in value if column is array. Returns value for the given key in value if column is map.	true	None
spark.rapids.sql.expression.EndsWith		Ends with	true	None
spark.rapids.sql.expression.EqualNullSafe	<=>	Check if the values are equal including nulls <=>	true	None
spark.rapids.sql.expression.EqualTo	==, =	Check if the values are equal	true	None
spark.rapids.sql.expression.Exp	exp	Euler's number e raised to a power	true	None
spark.rapids.sql.expression.Explode	explode_outer, explode	Given an input array produces a sequence of rows for each value in the array	true	None
spark.rapids.sql.expression.Expm1	expm1	Euler's number e raised to a power minus 1	true	None
spark.rapids.sql.expression.Flatten	flatten	Creates a single array from an array of arrays	true	None
spark.rapids.sql.expression.Floor		Floor of a number	true	None
spark.rapids.sql.expression.FormatNumber	format_number	Formats the number x like '#,###,###.##', rounded to d decimal places.	true	None
spark.rapids.sql.expression.FromUTCTimestamp	from_utc_timestamp	Render the input UTC timestamp in the input timezone	true	None
spark.rapids.sql.expression.FromUnixTime	from_unixtime	Get the string from a unix timestamp	true	None
spark.rapids.sql.expression.GetArrayItem		Gets the field at ordinal in the Array	true	None
spark.rapids.sql.expression.GetArrayStructFields		Extracts the ordinal-th fields of all array elements for the data with the type of array of struct	true	None
spark.rapids.sql.expression.GetJsonObject	get_json_object	Extracts a json object from path	true	None
spark.rapids.sql.expression.GetMapValue		Gets Value from a Map based on a key	true	None
spark.rapids.sql.expression.GetStructField		Gets the named field of the struct	true	None
spark.rapids.sql.expression.GetTimestamp		Gets timestamps from strings using given pattern.	true	None
spark.rapids.sql.expression.GreaterThan	>	> operator	true	None
spark.rapids.sql.expression.GreaterThanOrEqual	>=	>= operator	true	None
spark.rapids.sql.expression.Greatest	greatest	Returns the greatest value of all parameters, skipping null values	true	None
spark.rapids.sql.expression.Hex	hex	Returns the hex string representation of a value	true	None
spark.rapids.sql.expression.HiveHash		hive hash operator	true	None
spark.rapids.sql.expression.Hour	hour	Returns the hour component of the string/timestamp	true	None
spark.rapids.sql.expression.Hypot	hypot	Pythagorean addition (Hypotenuse) of real numbers	true	None
spark.rapids.sql.expression.If	if	IF expression	true	None
spark.rapids.sql.expression.In	in	IN operator	true	None
spark.rapids.sql.expression.InSet		INSET operator	true	None
spark.rapids.sql.expression.InitCap	initcap	Returns str with the first letter of each word in uppercase. All other letters are in lowercase	true	This is not 100% compatible with the Spark version because the Unicode version used by cuDF and the JVM may differ, resulting in some corner-case characters not changing case correctly.
spark.rapids.sql.expression.InputFileBlockLength	input_file_block_length	Returns the length of the block being read, or -1 if not available	true	None
spark.rapids.sql.expression.InputFileBlockStart	input_file_block_start	Returns the start offset of the block being read, or -1 if not available	true	None
spark.rapids.sql.expression.InputFileName	input_file_name	Returns the name of the file being read, or empty string if not available	true	None
spark.rapids.sql.expression.IntegralDivide	div	Division with a integer result	true	None
spark.rapids.sql.expression.IsNaN	isnan	Checks if a value is NaN	true	None
spark.rapids.sql.expression.IsNotNull	isnotnull	Checks if a value is not null	true	None
spark.rapids.sql.expression.IsNull	isnull	Checks if a value is null	true	None
spark.rapids.sql.expression.JsonToStructs	from_json	Returns a struct value with the given jsonStr and schema	true	None
spark.rapids.sql.expression.JsonTuple	json_tuple	Returns a tuple like the function get_json_object, but it takes multiple names. All the input parameters and output column types are string.	true	None
spark.rapids.sql.expression.KnownFloatingPointNormalized		Tag to prevent redundant normalization	true	None
spark.rapids.sql.expression.KnownNotNull		Tag an expression as known to not be null	true	None
spark.rapids.sql.expression.Lag	lag	Window function that returns N entries behind this one	true	None
spark.rapids.sql.expression.LambdaFunction		Holds a higher order SQL function	true	None
spark.rapids.sql.expression.LastDay	last_day	Returns the last day of the month which the date belongs to	true	None
spark.rapids.sql.expression.Lead	lead	Window function that returns N entries ahead of this one	true	None
spark.rapids.sql.expression.Least	least	Returns the least value of all parameters, skipping null values	true	None
spark.rapids.sql.expression.Length	char_length, character_length, length	String character length or binary byte length	true	None
spark.rapids.sql.expression.LessThan	<	< operator	true	None
spark.rapids.sql.expression.LessThanOrEqual	<=	<= operator	true	None
spark.rapids.sql.expression.Like	like	Like	true	None
spark.rapids.sql.expression.Literal		Holds a static value from the query	true	None
spark.rapids.sql.expression.Log	ln	Natural log	true	None
spark.rapids.sql.expression.Log10	log10	Log base 10	true	None
spark.rapids.sql.expression.Log1p	log1p	Natural log 1 + expr	true	None
spark.rapids.sql.expression.Log2	log2	Log base 2	true	None
spark.rapids.sql.expression.Logarithm	log	Log variable base	true	None
spark.rapids.sql.expression.Lower	lcase, lower	String lowercase operator	true	This is not 100% compatible with the Spark version because the Unicode version used by cuDF and the JVM may differ, resulting in some corner-case characters not changing case correctly.
spark.rapids.sql.expression.MakeDecimal		Create a Decimal from an unscaled long value for some aggregation optimizations	true	None
spark.rapids.sql.expression.MapConcat	map_concat	Returns the union of all the given maps	true	None
spark.rapids.sql.expression.MapEntries	map_entries	Returns an unordered array of all entries in the given map	true	None
spark.rapids.sql.expression.MapFilter	map_filter	Filters entries in a map using the function	true	None
spark.rapids.sql.expression.MapFromArrays	map_from_arrays	Creates a new map from two arrays	true	None
spark.rapids.sql.expression.MapFromEntries	map_from_entries	Creates a map from an array of entries (structs of key-value pairs)	true	None
spark.rapids.sql.expression.MapKeys	map_keys	Returns an unordered array containing the keys of the map	true	None
spark.rapids.sql.expression.MapValues	map_values	Returns an unordered array containing the values of the map	true	None
spark.rapids.sql.expression.MapZipWith	map_zip_with	Filters entries in a map using the function	true	None
spark.rapids.sql.expression.Md5	md5	MD5 hash operator	true	None
spark.rapids.sql.expression.MicrosToTimestamp	timestamp_micros	Converts the number of microseconds from unix epoch to a timestamp	true	None
spark.rapids.sql.expression.MillisToTimestamp	timestamp_millis	Converts the number of milliseconds from unix epoch to a timestamp	true	None
spark.rapids.sql.expression.Minute	minute	Returns the minute component of the string/timestamp	true	None
spark.rapids.sql.expression.MonotonicallyIncreasingID	monotonically_increasing_id	Returns monotonically increasing 64-bit integers	true	None
spark.rapids.sql.expression.Month	month	Returns the month from a date or timestamp	true	None
spark.rapids.sql.expression.MonthsBetween	months_between	If timestamp1 is later than timestamp2, then the result is positive. If timestamp1 and timestamp2 are on the same day of month, or both are the last day of month, time of day will be ignored. Otherwise, the difference is calculated based on 31 days per month, and rounded to 8 digits unless roundOff=false.	true	None
spark.rapids.sql.expression.Multiply	*	Multiplication	true	None
spark.rapids.sql.expression.MultiplyDTInterval		Day-time interval * number	true	None
spark.rapids.sql.expression.MultiplyYMInterval		Year-month interval * number	true	None
spark.rapids.sql.expression.Murmur3Hash	hash	Murmur3 hash operator	true	None
spark.rapids.sql.expression.NaNvl	nanvl	Evaluates to left iff left is not NaN, right otherwise	true	None
spark.rapids.sql.expression.NamedLambdaVariable		A parameter to a higher order SQL function	true	None
spark.rapids.sql.expression.Not	!, not	Boolean not operator	true	None
spark.rapids.sql.expression.NthValue	nth_value	nth window operator	true	None
spark.rapids.sql.expression.OctetLength	octet_length	The byte length of string data	true	None
spark.rapids.sql.expression.Or	or	Logical OR	true	None
spark.rapids.sql.expression.ParseUrl	parse_url	Extracts a part from a URL	true	None
spark.rapids.sql.expression.PercentRank	percent_rank	Window function that returns the percent rank value within the aggregation window	true	None
spark.rapids.sql.expression.Pmod	pmod	Pmod	true	None
spark.rapids.sql.expression.PosExplode	posexplode_outer, posexplode	Given an input array produces a sequence of rows for each value in the array	true	None
spark.rapids.sql.expression.Pow	pow, power	lhs ^ rhs	true	None
spark.rapids.sql.expression.PreciseTimestampConversion		Expression used internally to convert the TimestampType to Long and back without losing precision, i.e. in microseconds. Used in time windowing	true	None
spark.rapids.sql.expression.PromotePrecision		PromotePrecision before arithmetic operations between DecimalType data	true	None
spark.rapids.sql.expression.PythonUDF		UDF run in an external python process. Does not actually run on the GPU, but the transfer of data to/from it can be accelerated	true	None
spark.rapids.sql.expression.Quarter	quarter	Returns the quarter of the year for date, in the range 1 to 4	true	None
spark.rapids.sql.expression.RLike	regexp_like, regexp, rlike	Regular expression version of Like	true	None
spark.rapids.sql.expression.RaiseError	raise_error	Throw an exception	true	None
spark.rapids.sql.expression.Rand	rand, random	Generate a random column with i.i.d. uniformly distributed values in [0, 1)	true	None
spark.rapids.sql.expression.Rank	rank	Window function that returns the rank value within the aggregation window	true	None
spark.rapids.sql.expression.RegExpExtract	regexp_extract	Extract a specific group identified by a regular expression	true	None
spark.rapids.sql.expression.RegExpExtractAll	regexp_extract_all	Extract all strings matching a regular expression corresponding to the regex group index	true	None
spark.rapids.sql.expression.RegExpReplace	regexp_replace	String replace using a regular expression pattern	true	None
spark.rapids.sql.expression.Remainder	%, mod	Remainder or modulo	true	None
spark.rapids.sql.expression.ReplicateRows		Given an input row replicates the row N times	true	None
spark.rapids.sql.expression.Reverse	reverse	Returns a reversed string or an array with reverse order of elements	true	None
spark.rapids.sql.expression.Rint	rint	Rounds up a double value to the nearest double equal to an integer	true	None
spark.rapids.sql.expression.Round	round	Round an expression to d decimal places using HALF_UP rounding mode	true	None
spark.rapids.sql.expression.RoundCeil		Computes the ceiling of the given expression to d decimal places	true	None
spark.rapids.sql.expression.RoundFloor		Computes the floor of the given expression to d decimal places	true	None
spark.rapids.sql.expression.RowNumber	row_number	Window function that returns the index for the row within the aggregation window	true	None
spark.rapids.sql.expression.ScalaUDF		User Defined Function, the UDF can choose to implement a RAPIDS accelerated interface to get better performance.	true	None
spark.rapids.sql.expression.Second	second	Returns the second component of the string/timestamp	true	None
spark.rapids.sql.expression.SecondsToTimestamp	timestamp_seconds	Converts the number of seconds from unix epoch to a timestamp	true	None
spark.rapids.sql.expression.Sequence	sequence	Sequence	true	None
spark.rapids.sql.expression.Sha1	sha1, sha	Sha1 hash operator	true	None
spark.rapids.sql.expression.Sha2	sha2	Sha2 hash operator	true	None
spark.rapids.sql.expression.ShiftLeft	shiftleft	Bitwise shift left (<<)	true	None
spark.rapids.sql.expression.ShiftRight	shiftright	Bitwise shift right (>>)	true	None
spark.rapids.sql.expression.ShiftRightUnsigned	shiftrightunsigned	Bitwise unsigned shift right (>>>)	true	None
spark.rapids.sql.expression.Signum	sign, signum	Returns -1.0, 0.0 or 1.0 as expr is negative, 0 or positive	true	None
spark.rapids.sql.expression.Sin	sin	Sine	true	None
spark.rapids.sql.expression.Sinh	sinh	Hyperbolic sine	true	None
spark.rapids.sql.expression.Size	cardinality, size	The size of an array or a map	true	None
spark.rapids.sql.expression.Slice	slice	Subsets array x starting from index start (array indices start at 1, or starting from the end if start is negative) with the specified length.	true	None
spark.rapids.sql.expression.SortArray	sort_array	Returns a sorted array with the input array and the ascending / descending order	true	None
spark.rapids.sql.expression.SortOrder		Sort order	true	None
spark.rapids.sql.expression.SparkPartitionID	spark_partition_id	Returns the current partition id	true	None
spark.rapids.sql.expression.SpecifiedWindowFrame		Specification of the width of the group (or "frame") of input rows around which a window function is evaluated	true	None
spark.rapids.sql.expression.Sqrt	sqrt	Square root	true	None
spark.rapids.sql.expression.Stack	stack	Separates expr1, ..., exprk into n rows.	true	None
spark.rapids.sql.expression.StartsWith		Starts with	true	None
spark.rapids.sql.expression.StringDecode		Decodes binary data from a charset to a UTF-8 string	true	None
spark.rapids.sql.expression.StringInstr	instr	Instr string operator	true	None
spark.rapids.sql.expression.StringLPad		Pad a string on the left	true	None
spark.rapids.sql.expression.StringLocate	locate, position	Substring search operator	true	None
spark.rapids.sql.expression.StringRPad		Pad a string on the right	true	None
spark.rapids.sql.expression.StringRepeat	repeat	StringRepeat operator that repeats the given strings with numbers of times given by repeatTimes	true	None
spark.rapids.sql.expression.StringReplace	replace	StringReplace operator	true	None
spark.rapids.sql.expression.StringSplit	split	Splits str around occurrences that match regex	true	None
spark.rapids.sql.expression.StringToMap	str_to_map	Creates a map after splitting the input string into pairs of key-value strings	true	None
spark.rapids.sql.expression.StringTranslate	translate	StringTranslate operator	true	This is not 100% compatible with the Spark version because the GPU implementation supports all unicode code points. In Spark versions < 3.2.0, translate() does not support unicode characters with code point >= U+10000 (See SPARK-34094)
spark.rapids.sql.expression.StringTrim	trim	StringTrim operator	true	None
spark.rapids.sql.expression.StringTrimLeft	ltrim	StringTrimLeft operator	true	None
spark.rapids.sql.expression.StringTrimRight	rtrim	StringTrimRight operator	true	None
spark.rapids.sql.expression.StructsToJson	to_json	Converts structs to JSON text format	false	This is disabled by default because it is currently in beta and undergoes continuous enhancements. Please consult the compatibility documentation to determine whether you can enable this configuration for your use case
spark.rapids.sql.expression.Substring	substr, substring	Substring operator	true	None
spark.rapids.sql.expression.SubstringIndex	substring_index	substring_index operator	true	None
spark.rapids.sql.expression.Subtract	-	Subtraction	true	None
spark.rapids.sql.expression.Tan	tan	Tangent	true	None
spark.rapids.sql.expression.Tanh	tanh	Hyperbolic tangent	true	None
spark.rapids.sql.expression.TimeAdd		Adds interval to timestamp	true	None
spark.rapids.sql.expression.ToDegrees	degrees	Converts radians to degrees	true	None
spark.rapids.sql.expression.ToRadians	radians	Converts degrees to radians	true	None
spark.rapids.sql.expression.ToUTCTimestamp	to_utc_timestamp	Render the input timestamp in UTC	true	None
spark.rapids.sql.expression.ToUnixTimestamp	to_unix_timestamp	Returns the UNIX timestamp of the given time	true	None
spark.rapids.sql.expression.TransformKeys	transform_keys	Transform keys in a map using a transform function	true	None
spark.rapids.sql.expression.TransformValues	transform_values	Transform values in a map using a transform function	true	None
spark.rapids.sql.expression.TruncDate	trunc	Truncate the date to the unit specified by the given string format	true	None
spark.rapids.sql.expression.TruncTimestamp	date_trunc	Truncate the timestamp to the unit specified by the given string format	true	None
spark.rapids.sql.expression.UnaryMinus	negative	Negate a numeric value	true	None
spark.rapids.sql.expression.UnaryPositive	positive	A numeric value with a + in front of it	true	None
spark.rapids.sql.expression.UnboundedFollowing$		Special boundary for a window frame, indicating all rows preceding the current row	true	None
spark.rapids.sql.expression.UnboundedPreceding$		Special boundary for a window frame, indicating all rows preceding the current row	true	None
spark.rapids.sql.expression.UnixTimestamp	unix_timestamp	Returns the UNIX timestamp of current or specified time	true	None
spark.rapids.sql.expression.UnscaledValue		Convert a Decimal to an unscaled long value for some aggregation optimizations	true	None
spark.rapids.sql.expression.Upper	ucase, upper	String uppercase operator	true	This is not 100% compatible with the Spark version because the Unicode version used by cuDF and the JVM may differ, resulting in some corner-case characters not changing case correctly.
spark.rapids.sql.expression.Uuid	uuid	Uuid	true	None
spark.rapids.sql.expression.WeekDay	weekday	Returns the day of the week (0 = Monday...6=Sunday)	true	None
spark.rapids.sql.expression.WindowExpression		Calculates a return value for every input row of a table based on a group (or "window") of rows	true	None
spark.rapids.sql.expression.WindowSpecDefinition		Specification of a window function, indicating the partitioning-expression, the row ordering, and the width of the window	true	None
spark.rapids.sql.expression.XxHash64	xxhash64	xxhash64 hash operator	true	None
spark.rapids.sql.expression.Year	year	Returns the year from a date or timestamp	true	None
spark.rapids.sql.expression.AggregateExpression		Aggregate expression	true	None
spark.rapids.sql.expression.ApproximatePercentile	approx_percentile, percentile_approx	Approximate percentile	true	This is not 100% compatible with the Spark version because the GPU implementation of approx_percentile is not bit-for-bit compatible with Apache Spark
spark.rapids.sql.expression.Average	avg, mean	Average aggregate operator	true	None
spark.rapids.sql.expression.BitAndAgg	bit_and	Returns the bitwise AND of all non-null input values	true	None
spark.rapids.sql.expression.BitOrAgg	bit_or	Returns the bitwise OR of all non-null input values	true	None
spark.rapids.sql.expression.BitXorAgg	bit_xor	Returns the bitwise XOR of all non-null input values	true	None
spark.rapids.sql.expression.BloomFilterAggregate		Bloom filter build	true	None
spark.rapids.sql.expression.CollectList	array_agg, collect_list	Collect a list of non-unique elements, not supported in reduction	true	None
spark.rapids.sql.expression.CollectSet	collect_set	Collect a set of unique elements, not supported in reduction	true	None
spark.rapids.sql.expression.Count	count	Count aggregate operator	true	None
spark.rapids.sql.expression.First	first_value, first	first aggregate operator	true	None
spark.rapids.sql.expression.HyperLogLogPlusPlus	approx_count_distinct	Aggregation approximate count distinct	true	None
spark.rapids.sql.expression.Last	last_value, last	last aggregate operator	true	None
spark.rapids.sql.expression.Max	max	Max aggregate operator	true	None
spark.rapids.sql.expression.MaxBy	max_by	MaxBy aggregate operator. It may produce different results than CPU when multiple rows in a group have same minimum value in the ordering column and different associated values in the value column.	true	None
spark.rapids.sql.expression.Min	min	Min aggregate operator	true	None
spark.rapids.sql.expression.MinBy	min_by	MinBy aggregate operator. It may produce different results than CPU when multiple rows in a group have same minimum value in the ordering column and different associated values in the value column.	true	None
spark.rapids.sql.expression.Percentile	percentile	Aggregation computing exact percentile	true	None
spark.rapids.sql.expression.PivotFirst		PivotFirst operator	true	None
spark.rapids.sql.expression.StddevPop	stddev_pop	Aggregation computing population standard deviation	true	None
spark.rapids.sql.expression.StddevSamp	std, stddev_samp, stddev	Aggregation computing sample standard deviation	true	None
spark.rapids.sql.expression.Sum	sum	Sum aggregate operator	true	None
spark.rapids.sql.expression.VariancePop	var_pop	Aggregation computing population variance	true	None
spark.rapids.sql.expression.VarianceSamp	var_samp, variance	Aggregation computing sample variance	true	None
spark.rapids.sql.expression.StaticInvoke		StaticInvoke	true	The supported types are not deterministic since it's a dynamic expression
spark.rapids.sql.expression.NormalizeNaNAndZero		Normalize NaN and zero	true	None
spark.rapids.sql.expression.InSubqueryExec		Evaluates to true if values are in a subquery's result set	true	None
spark.rapids.sql.expression.ScalarSubquery		Subquery that will return only one row and one column	true	None
spark.rapids.sql.expression.HiveGenericUDF		Hive Generic UDF, the UDF can choose to implement a RAPIDS accelerated interface to get better performance	true	None
spark.rapids.sql.expression.HiveSimpleUDF		Hive UDF, the UDF can choose to implement a RAPIDS accelerated interface to get better performance	true	None

Execution

Name	Description	Default Value	Notes
spark.rapids.sql.exec.CoalesceExec	The backend for the dataframe coalesce method	true	None
spark.rapids.sql.exec.CollectLimitExec	Reduce to single partition and apply limit	false	This is disabled by default because Collect Limit replacement can be slower on the GPU, if huge number of rows in a batch it could help by limiting the number of rows transferred from GPU to CPU
spark.rapids.sql.exec.ExpandExec	The backend for the expand operator	true	None
spark.rapids.sql.exec.FileSourceScanExec	Reading data from files, often from Hive tables	true	None
spark.rapids.sql.exec.FilterExec	The backend for most filter statements	true	None
spark.rapids.sql.exec.GenerateExec	The backend for operations that generate more output rows than input rows like explode	true	None
spark.rapids.sql.exec.GlobalLimitExec	Limiting of results across partitions	true	None
spark.rapids.sql.exec.LocalLimitExec	Per-partition limiting of results	true	None
spark.rapids.sql.exec.ProjectExec	The backend for most select, withColumn and dropColumn statements	true	None
spark.rapids.sql.exec.RangeExec	The backend for range operator	true	None
spark.rapids.sql.exec.SampleExec	The backend for the sample operator	true	None
spark.rapids.sql.exec.SortExec	The backend for the sort operator	true	None
spark.rapids.sql.exec.SubqueryBroadcastExec	Plan to collect and transform the broadcast key values	true	None
spark.rapids.sql.exec.TakeOrderedAndProjectExec	Take the first limit elements as defined by the sortOrder, and do projection if needed	true	None
spark.rapids.sql.exec.UnionExec	The backend for the union operator	true	None
spark.rapids.sql.exec.AQEShuffleReadExec	A wrapper of shuffle query stage	true	None
spark.rapids.sql.exec.HashAggregateExec	The backend for hash based aggregations	true	None
spark.rapids.sql.exec.ObjectHashAggregateExec	The backend for hash based aggregations supporting TypedImperativeAggregate functions	true	None
spark.rapids.sql.exec.SortAggregateExec	The backend for sort based aggregations	true	None
spark.rapids.sql.exec.InMemoryTableScanExec	Implementation of InMemoryTableScanExec to use GPU accelerated caching	true	None
spark.rapids.sql.exec.DataWritingCommandExec	Writing data	true	None
spark.rapids.sql.exec.ExecutedCommandExec	Eagerly executed commands	true	None
spark.rapids.sql.exec.AppendDataExecV1	Append data into a datasource V2 table using the V1 write interface	true	None
spark.rapids.sql.exec.AtomicCreateTableAsSelectExec	Create table as select for datasource V2 tables that support staging table creation	true	None
spark.rapids.sql.exec.AtomicReplaceTableAsSelectExec	Replace table as select for datasource V2 tables that support staging table creation	true	None
spark.rapids.sql.exec.BatchScanExec	The backend for most file input	true	None
spark.rapids.sql.exec.OverwriteByExpressionExecV1	Overwrite into a datasource V2 table using the V1 write interface	true	None
spark.rapids.sql.exec.BroadcastExchangeExec	The backend for broadcast exchange of data	true	None
spark.rapids.sql.exec.ShuffleExchangeExec	The backend for most data being exchanged between processes	true	None
spark.rapids.sql.exec.BroadcastHashJoinExec	Implementation of join using broadcast data	true	None
spark.rapids.sql.exec.BroadcastNestedLoopJoinExec	Implementation of join using brute force. Full outer joins and joins where the broadcast side matches the join side (e.g.: LeftOuter with left broadcast) are not supported	true	None
spark.rapids.sql.exec.CartesianProductExec	Implementation of join using brute force	true	None
spark.rapids.sql.exec.ShuffledHashJoinExec	Implementation of join using hashed shuffled data	true	None
spark.rapids.sql.exec.SortMergeJoinExec	Sort merge join, replacing with shuffled hash join	true	None
spark.rapids.sql.exec.AggregateInPandasExec	The backend for an Aggregation Pandas UDF. This accelerates the data transfer between the Java process and the Python process. It also supports scheduling GPU resources for the Python process when enabled.	true	None
spark.rapids.sql.exec.ArrowEvalPythonExec	The backend of the Scalar Pandas UDFs. Accelerates the data transfer between the Java process and the Python process. It also supports scheduling GPU resources for the Python process when enabled	true	None
spark.rapids.sql.exec.FlatMapCoGroupsInPandasExec	The backend for CoGrouped Aggregation Pandas UDF. Accelerates the data transfer between the Java process and the Python process. It also supports scheduling GPU resources for the Python process when enabled.	false	This is disabled by default because Performance is not ideal with many small groups
spark.rapids.sql.exec.FlatMapGroupsInPandasExec	The backend for Flat Map Groups Pandas UDF, Accelerates the data transfer between the Java process and the Python process. It also supports scheduling GPU resources for the Python process when enabled.	true	None
spark.rapids.sql.exec.MapInPandasExec	The backend for Map Pandas Iterator UDF. Accelerates the data transfer between the Java process and the Python process. It also supports scheduling GPU resources for the Python process when enabled.	true	None
spark.rapids.sql.exec.PythonMapInArrowExec	The backend for Map Arrow Iterator UDF. Accelerates the data transfer between the Java process and the Python process. It also supports scheduling GPU resources for the Python process when enabled.	true	None
spark.rapids.sql.exec.WindowInPandasExec	The backend for Window Aggregation Pandas UDF, Accelerates the data transfer between the Java process and the Python process. It also supports scheduling GPU resources for the Python process when enabled. For now it only supports row based window frame.	false	This is disabled by default because it only supports row based frame for now
spark.rapids.sql.exec.WindowExec	Window-operator backend	true	None
spark.rapids.sql.exec.HiveTableScanExec	Scan Exec to read Hive delimited text tables	true	None

Commands

Name	Description	Default Value	Notes
spark.rapids.sql.command.SaveIntoDataSourceCommand	Write to a data source	true	None

Scans

Name	Description	Default Value	Notes
spark.rapids.sql.input.CSVScan	CSV parsing	true	None
spark.rapids.sql.input.JsonScan	Json parsing	true	None
spark.rapids.sql.input.OrcScan	ORC parsing	true	None
spark.rapids.sql.input.ParquetScan	Parquet parsing	true	None
spark.rapids.sql.input.AvroScan	Avro parsing	true	None

Partitioning

Name	Description	Default Value	Notes
spark.rapids.sql.partitioning.HashPartitioning	Hash based partitioning	true	None
spark.rapids.sql.partitioning.RangePartitioning	Range partitioning	true	None
spark.rapids.sql.partitioning.RoundRobinPartitioning	Round robin partitioning	true	None
spark.rapids.sql.partitioning.SinglePartition$	Single partitioning	true	None