Actual-time streaming knowledge processing is a strategic crucial that straight impacts enterprise competitiveness. Organizations face mounting stress to course of large knowledge streams instantaneously—from detecting fraudulent transactions and delivering personalised buyer experiences to optimizing advanced provide chains and responding to market dynamics milliseconds forward of opponents.
Apache Spark Structured Streaming addresses these vital enterprise challenges by way of its stateful processing capabilities, enabling functions to keep up and replace intermediate outcomes throughout a number of knowledge streams or time home windows. RocksDB was launched in Apache Spark 3.2, providing a extra environment friendly various to the default HDFS-based in-memory retailer. RocksDB excels in stateful streaming in situations that require dealing with giant portions of state knowledge. It delivers optimum efficiency advantages, significantly in lowering Java digital machine (JVM) reminiscence stress and rubbish assortment (GC) overhead.
This submit explores RocksDB’s key options and demonstrates its implementation utilizing Spark on Amazon EMR and AWS Glue, offering you with the data it’s good to scale your real-time knowledge processing capabilities.
RocksDB state retailer overview
Spark Structured Streaming processes fall into two classes:
Stateful: Requires monitoring intermediate outcomes throughout micro-batches (for instance, when operating aggregations and de-duplication).
Stateless: Processes every batch independently.
A state retailer is required by stateful functions that observe intermediate question outcomes. That is important for computations that rely upon steady occasions and alter outcomes primarily based on every batch of enter, or on mixture knowledge over time, together with late arriving knowledge. By default, Spark provides a state retailer that retains states in JVM reminiscence, which is performant and ample for many common streaming circumstances. Nevertheless, you probably have a lot of stateful operations in a streaming software—resembling, streaming aggregation, streaming dropDuplicates, stream-stream joins, and so forth—the default in-memory state retailer may face out-of-memory (OOM) points due to a big JVM reminiscence footprint or frequent GC pauses, leading to degraded efficiency.
Benefits of RocksDB over in-memory state retailer
RocksDB addresses the challenges of an in-memory state retailer by way of off-heap reminiscence administration and environment friendly checkpointing.
Off-heap reminiscence administration: RocksDB shops state knowledge in OS-managed off-heap reminiscence, lowering GC stress. Whereas off-heap reminiscence nonetheless consumes machine reminiscence, it doesn’t occupy house within the JVM. As an alternative, its core reminiscence buildings, resembling block cache or memTables, allocate straight from the working system, bypassing the JVM heap. This strategy makes RocksDB an optimum alternative for memory-intensive functions.
Environment friendly checkpointing: RocksDB robotically saves state adjustments to checkpoint places, resembling Amazon Easy Storage Service (Amazon S3) paths or native directories, serving to to make sure full fault tolerance. When interacting with S3, RocksDB is designed to enhance checkpointing effectivity; it does this by way of incremental updates and compaction to scale back the quantity of information transferred to S3 throughout checkpoints, and by persisting fewer giant state information in comparison with the various small information of the default state retailer, lowering S3 API calls and latency.
Implementation concerns
RocksDB operates as a local C++ library embedded throughout the Spark executor, utilizing off-heap reminiscence. Whereas it doesn’t fall below JVM GC management, it nonetheless impacts total executor reminiscence utilization from the YARN or OS perspective. RocksDB’s off-heap reminiscence utilization may exceed YARN container limits with out triggering container termination, doubtlessly resulting in OOM points. You must think about the next approaches to handle Spark’s reminiscence:
Alter the Spark executor reminiscence measurement
Improve spark.executor.memoryOverheadorspark.executor.memoryOverheadFactor to depart extra room for off-heap utilization. The next instance units half (4 GB) of spark.executor.reminiscence (8 GB) because the reminiscence overhead measurement.
# Whole executor reminiscence = 8GB (heap) + 4GB (overhead) = 12GB
spark-submit
. . . . . . . .
–conf spark.executor.reminiscence=8g # JVM Heap
–conf spark.executor.memoryOverhead=4g # Off-heap allocation (RocksDB + different native)
. . . . . . . .
For Amazon EMR on Amazon Elastic Compute Cloud (Amazon EC2), enabling YARN reminiscence management with the next strict container reminiscence enforcement by way of polling technique preempts containers to keep away from node-wide OOM failures:
yarn.nodemanager.useful resource.reminiscence.enforced = false
yarn.nodemanager.elastic-memory-control.enabled = false
yarn.nodemanager.pmem-check-enabled = true
or
yarn.nodemanager.vmem-check-enabled = true
Off-heap reminiscence management
Use RocksDB-specific settings to configure reminiscence utilization. Extra particulars might be discovered within the Finest practices and concerns part.
Get began with RocksDB on Amazon EMR and AWS Glue
To activate the state retailer RocksDB in Spark, configure your software with the next setting:
spark.sql.streaming.stateStore.providerClass=org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider
Within the following sections, we discover making a pattern Spark Structured Streaming job with RocksDB enabled operating on Amazon EMR and AWS Glue respectively.
RocksDB on Amazon EMR
Amazon EMR variations 6.6.0 and later assist RocksDB, together with Amazon EMR on EC2, Amazon EMR serverless and Amazon EMR on Amazon Elastic Kubernetes Service (Amazon EKS). On this case, we use Amazon EMR on EC2 for example.
Use the next steps to run a pattern streaming job with RocksDB enabled.
Add the next pattern script to s3:///script/sample_script.py
from pyspark.sql import SparkSession
from pyspark.sql.features import explode, break up, col, expr
import random
# Record of phrases
phrases = (“apple”, “banana”, “orange”, “grape”, “melon”,
“peach”, “berry”, “mango”, “kiwi”, “lemon”)
# Create random strings from phrases
def generate_random_string():
return ” “.be a part of(random.selections(phrases, ok=5))
# Create Spark Session
spark = SparkSession
.builder
.appName(“StreamingWordCount”)
.config(“spark.sql.streaming.stateStore.providerClass”,”org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider”)
.getOrCreate()
# Register UDF
spark.udf.register(“random_string”, generate_random_string)
# Create streaming knowledge
raw_stream = spark.readStream
.format(“charge”)
.possibility(“rowsPerSecond”, 1)
.load()
.withColumn(“phrases”, expr(“random_string()”))
# Execute phrase counts
wordCounts = raw_stream.choose(explode(break up(raw_stream.phrases, ” “)).alias(“phrase”)).groupby(“phrase”).rely()
# Output the outcomes
question = wordCounts
.writeStream
.outputMode(“full”)
.format(“console”)
.begin()
question.awaitTermination()
On the AWS Administration Console for Amazon EMR, select Create Cluster
For Title and functions – required, choose the newest Amazon EMR launch.
For Steps, select Add. For Sort, choose Spark software.
For Title, enter GettingStartedWithRocksDB and s3:///script/sample_script.py because the Utility location.
Select Save step.
For different settings, select the suitable settings primarily based in your use case.
Select Create cluster to begin the streaming software by way of Amazon EMR step.
RocksDB on AWS Glue
AWS Glue 4.0 and later variations assist RocksDB. Use the next steps to run the pattern job with RocksDB enabled on AWS Glue.
On the AWS Glue console, within the navigation pane, select ETL jobs.
Select Script editor and Create script.
For the job title, enter GettingStartedWithRocksDB.
Copy the script from the earlier instance and paste it on the Script tab.
On Job particulars tab, for Sort, choose Spark Streaming.
Select Save, after which select Run to begin the streaming job on AWS Glue.
Walkthrough particulars
Let’s dive deep into the script to grasp the best way to run a easy stateful Spark software with RocksDB utilizing the next instance pySpark code.
First, arrange RocksDB as your state retailer by configuring the supplier class:
spark = SparkSession
.builder
.appName(“StreamingWordCount”)
.config(“spark.sql.streaming.stateStore.providerClass”,”org.apache.spark.sql.execution.streaming.state.RocksDBStateStoreProvider”)
.getOrCreate()
To simulate streaming knowledge, create a knowledge stream utilizing the speed supply kind. It generates one document per second, containing 5 random fruit names from a pre-defined record.
# Record of phrases
phrases = (“apple”, “banana”, “orange”, “grape”, “melon”,
“peach”, “berry”, “mango”, “kiwi”, “lemon”)
# Create random strings from phrases
def generate_random_string():
return ” “.be a part of(random.selections(phrases, ok=5))
# Register UDF
spark.udf.register(“random_string”, generate_random_string)
# Create streaming knowledge
raw_stream = spark.readStream
.format(“charge”)
.possibility(“rowsPerSecond”, 1)
.load()
.withColumn(“phrases”, expr(“random_string()”))
Create a phrase counting operation on the incoming stream. It is a stateful operation as a result of it maintains operating counts between processing intervals, that’s, earlier counts should be saved to calculate the subsequent new totals.
# Break up raw_stream into phrases and counts them
wordCounts = raw_stream.choose(explode(break up(raw_stream.phrases, ” “)).alias(“phrase”)).groupby(“phrase”).rely()
Lastly, output the phrase rely totals to the console:
# Output the outcomes
question = wordCounts
.writeStream
.outputMode(“full”)
.format(“console”)
.begin()
Enter knowledge
In the identical pattern code, check knowledge (raw_stream) is generated at a charge of one-row-per-second, as proven within the following instance:
+———————–+—–+——————————–+
|timestamp |worth|phrases |
+———————–+—–+——————————–+
|2025-04-18 07:05:57.204|125 |berry peach orange banana banana|
+———————–+—–+——————————–+
Output outcome
The streaming job produces the next ends in the output logs. It demonstrates how Spark Structured Streaming maintains and updates the state throughout a number of micro-batches:
Batch 0: Begins with an empty state
Batch 1: Processes a number of enter information, leading to preliminary counts for each one of many 10 fruits (for instance, banana seems 8 occasions)
Batch 2: Operating totals primarily based on new occurrences from the subsequent set of information are added to the counts (for instance, banana will increase from 8 to fifteen, indicating 7 new occurrences).
——————————————-
Batch: 0
——————————————-
+—-+—–+
|phrase|rely|
+—-+—–+
+—-+—–+
——————————————-
Batch: 1
——————————————-
+——+—–+
| phrase|rely|
+——+—–+
|banana| 8|
|orange| 4|
| apple| 3|
| berry| 5|
| lemon| 7|
| kiwi| 6|
| melon| 8|
| peach| 8|
| mango| 7|
| grape| 9|
+——+—–+
——————————————-
Batch: 2
——————————————-
+——+—–+
| phrase|rely|
+——+—–+
|banana| 15|
|orange| 8|
| apple| 7|
| berry| 11|
| lemon| 12|
| kiwi| 11|
| melon| 16|
| peach| 15|
| mango| 12|
| grape| 13|
+——+—–+
State retailer logs
RocksDB generates detailed logs through the job run, like the next:
INFO 2025-04-18T07:52:28,378 83933 org.apache.spark.sql.execution.streaming.MicroBatchExecution (stream execution thread for (id = xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx, runId = xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx)) 60 Streaming question made progress: {
“id”: “xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx”,
“runId”: “xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx”,
“title”: null,
“timestamp”: “2025-04-18T07:52:27.642Z”,
“batchId”: 39,
“numInputRows”: 1,
“inputRowsPerSecond”: 100.0,
“processedRowsPerSecond”: 1.3623978201634879,
“durationMs”: {
“addBatch”: 648,
“commitOffsets”: 39,
“getBatch”: 0,
“latestOffset”: 0,
“queryPlanning”: 10,
“triggerExecution”: 734,
“walCommit”: 35
},
“stateOperators”: (
{
“operatorName”: “stateStoreSave”,
“numRowsTotal”: 10,
“numRowsUpdated”: 4,
“allUpdatesTimeMs”: 18,
“numRowsRemoved”: 0,
“allRemovalsTimeMs”: 0,
“commitTimeMs”: 3629,
“memoryUsedBytes”: 174179,
“numRowsDroppedByWatermark”: 0,
“numShufflePartitions”: 36,
“numStateStoreInstances”: 36,
“customMetrics”: {
“rocksdbBytesCopied”: 5009,
“rocksdbCommitCheckpointLatency”: 533,
“rocksdbCommitCompactLatency”: 0,
“rocksdbCommitFileSyncLatencyMs”: 2991,
“rocksdbCommitFlushLatency”: 44,
“rocksdbCommitPauseLatency”: 0,
“rocksdbCommitWriteBatchLatency”: 0,
“rocksdbFilesCopied”: 4,
“rocksdbFilesReused”: 24,
“rocksdbGetCount”: 8,
“rocksdbGetLatency”: 0,
“rocksdbPinnedBlocksMemoryUsage”: 3168,
“rocksdbPutCount”: 4,
“rocksdbPutLatency”: 0,
“rocksdbReadBlockCacheHitCount”: 8,
“rocksdbReadBlockCacheMissCount”: 0,
“rocksdbSstFileSize”: 35035,
“rocksdbTotalBytesRead”: 136,
“rocksdbTotalBytesReadByCompaction”: 0,
“rocksdbTotalBytesReadThroughIterator”: 0,
“rocksdbTotalBytesWritten”: 228,
“rocksdbTotalBytesWrittenByCompaction”: 0,
“rocksdbTotalBytesWrittenByFlush”: 5653,
“rocksdbTotalCompactionLatencyMs”: 0,
“rocksdbWriterStallLatencyMs”: 0,
“rocksdbZipFileBytesUncompressed”: 266452
}
}
),
“sources”: (
{
“description”: “RateStreamV2(rowsPerSecond=1, rampUpTimeSeconds=0, numPartitions=default”,
“startOffset”: 63,
“endOffset”: 64,
“latestOffset”: 64,
“numInputRows”: 1,
“inputRowsPerSecond”: 100.0,
“processedRowsPerSecond”: 1.3623978201634879
}
),
“sink”: {
“description”: “org.apache.spark.sql.execution.streaming.ConsoleTable$@2cf39784”,
“numOutputRows”: 10
}
}
In Amazon EMR on EC2, these logs can be found on the node the place the YARN ApplicationMaster container is operating. They are often discovered at/var/log/hadoop-yarn/containers///stderr.
As for AWS Glue, you’ll find the RocksDB metrics in Amazon CloudWatch, below the log group /aws-glue/jobs/error.
RocksDB metrics
The metrics from the previous logs present insights on RocksDB standing. The followings are some instance metrics you may discover helpful when investigating streaming job points:
rocksdbCommitCheckpointLatency: Time spent writing checkpoints to native storage
rocksdbCommitCompactLatency: Length of checkpoint compaction operations throughout checkpoint commits
rocksdbSstFileSize: Present measurement of SST information in RocksDB.
Deep dive into RocksDB key ideas
To raised perceive the state metrics proven within the logs, we deep dive into RocksDB’s key ideas: MemTable, sorted string desk (SST) file, and checkpoints. Moreover, we offer some suggestions for greatest practices and fine-tuning.
Excessive degree structure
RocksDB is a neighborhood, non-distributed persistent key-value retailer embedded in Spark executors. It permits scalable state administration for streaming workloads, backed by Spark’s checkpointing for fault tolerance. As proven within the previous determine, RocksDB shops knowledge in reminiscence and likewise on disk. RocksDB’s capacity to spill knowledge over to disk is what permits Spark Structured Streaming to deal with state knowledge that exceeds the out there reminiscence.
Reminiscence:
Write buffers (MemTables): Designated reminiscence to buffer writes earlier than flushing onto disk
Block cache (learn buffer): Reduces question time by caching outcomes from disk
Disk:
SST information: Sorted String Desk saved as SST file format for quick entry
MemTable: Saved off-heap
MemTable, proven within the previous determine, is an in-memory retailer the place knowledge is first written off-heap, earlier than being flushed to disk as an SST file. RocksDB caches the newest two batches of information (sizzling knowledge) in MemTable to scale back streaming course of latency. By default, RocksDB solely has two MemTables—one is lively and the opposite is read-only. You probably have ample reminiscence, the configuration spark.sql.streaming.stateStore.rocksdb.maxWriteBufferNumber might be elevated to have greater than two MemTables. Amongst these MemTables, there’s all the time one lively desk, and the remaining are read-only MemTables used as write buffers.
SST information: Saved on Spark executor’s native disk
SST information are block-based tables saved on the Spark executor’s native disk. When the in-memory state knowledge can not match right into a MemTable (outlined by a Spark configuration writeBufferSizeMB), the lively desk is marked as immutable, saving it because the SST file format, which switches it to a read-only MemTable whereas asynchronously flushing it to native disks. Whereas flushing, the immutable MemTable can nonetheless be learn, in order that the latest state knowledge is obtainable with minimal learn latency.
Studying from RocksDB follows the sequence demonstrated by the previous diagram:
Learn from the lively MemTable.
If not discovered, iterate by way of read-only MemTables within the order of latest to oldest.
If not discovered, learn from BlockCache (learn buffer).
If misses, load index (one index per SST) from disk into BlockCache. Lookup key from index and if hits, load knowledge block onto BlockCache and return outcome.
SST information are saved on executors’ native directories below the trail of spark.native.dir (default: /tmp) or yarn.nodemanager.local-dirs:
Amazon EMR on EC2 – ${yarn.nodemanager.local-dirs}/usercache/hadoop/appcache///
Amazon EMR Serverless, Amazon EMR on EKS, AWS Glue – ${spark.native.dir}//
Moreover, through the use of software logs, you may observe the MemTable flush and SST file add standing below the file path:
Amazon EMR on EC2 – /var/log/hadoop-yarn/containers///stderr
Amazon EMR on EKS –/var/log/spark/person/-/stderr
The next is an instance command to examine the SST file standing in an executor log from Amazon EMR on EKS:
cat /var/log/spark/person/-/stderr/present | grep previous
or
kubectl logs –namespace emr -c spark-kubernetes-executor | grep previous
The next screenshot is an instance of the output of both command.
You should use the next examples to examine if the MemTable information have been deleted and flushed out to SST:
cat /var/log/spark/person/-/stderr/present | grep deletes
or
kubectl logs –namespace emr -c spark-kubernetes-executor | grep deletes
The next screenshot is an instance of the output of both command.
Checkpoints: Saved on the executor’s native disk or in an S3 bucket
To deal with fault tolerance and fail over from the final dedicated level, RocksDB helps checkpoints. The checkpoint information are often saved on the executor’s disk or in an S3 bucket, together with snapshot and delta or changelog knowledge information.
Beginning with Amazon EMR 7.0 and AWS Glue5.0, RocksDB state retailer supplies a brand new characteristic referred to as changelog checkpointing to improve checkpoint efficiency. when the changelog is enabled (disabled by default) utilizing the setting spark.sql.streaming.stateStore.rocksdb.changelogCheckpointing.enabled, RocksDB writes smaller change logs to the storage location (the native disk by default) as a substitute of incessantly persisting giant snapshot knowledge. Observe that snapshots are nonetheless created however much less incessantly, as proven within the following screenshot.
Right here’s an instance of a checkpoint location path when overridden to an S3 bucket: s3:////state/0/spark_parition_ID/state_version_ID.zip
Finest practices and concerns
This part outlines key methods for fine-tuning RocksDB efficiency and avoiding frequent pitfalls.
1. Reminiscence administration for RocksDB
To forestall OOM errors on Spark executors, you may configure RocksDB’s reminiscence utilization at both the node degree or occasion degree:
Node degree (really useful): Implement a world off-heap reminiscence restrict per executor. On this context, every executor is handled as a RocksDB node. If an executor processes N partitions of a stateful operator, it can have N variety of RocksDB cases on a single executor.
Occasion-level: Wonderful-tune particular person RocksDB cases.
Node-level reminiscence management per executor
Beginning with Amazon EMR 7.0 and AWS Glue 5.0 (Spark 3.5), a vital Spark configuration, boundedMemoryUsage, was launched (by way of SPARK-43311) to implement a world reminiscence cap at a single executor degree that’s shared by a number of RocksDB cases. This prevents RocksDB from consuming unbounded off-heap reminiscence, which might result in OOM errors or executor termination by useful resource managers resembling YARN or Kubernetes.
The next instance reveals the node-level configuration:
# Sure whole reminiscence utilization per executor
“spark.sql.streaming.stateStore.rocksdb.boundedMemoryUsage”: “true”
# Set a static whole reminiscence measurement per executor
“spark.sql.streaming.stateStore.rocksdb.maxMemoryUsageMB”: “500”
# For read-heavy workloads, break up reminiscence allocation between write buffers (30%) and block cache (70%)
“spark.sql.streaming.stateStore.rocksdb.writeBufferCacheRatio”: “0.3”
A single RocksDB occasion degree management
For granular reminiscence administration, you may configure particular person RocksDB cases utilizing the next settings:
# Management MemTable (write buffer) measurement and rely
“spark.sql.streaming.stateStore.rocksdb.writeBufferSizeMB”: “64”
“spark.sql.streaming.stateStore.rocksdb.maxWriteBufferNumber”: “4”
writeBufferSizeMB (default: 64, recommended: 64 – 128): Controls the most measurement of a single MemTable in RocksDB, affecting reminiscence utilization and write throughput. This setting is obtainable in Spark3.5 – (SPARK-42819) and later. It determines the dimensions of the reminiscence buffer earlier than state knowledge is flushed to disk. Bigger buffer sizes can enhance write efficiency by lowering SST flush frequency however will improve the executor’s reminiscence utilization. Adjusting this parameter is essential for optimizing reminiscence utilization and write throughput.
maxWriteBufferNumber (default: 2, recommended: 3 – 4): Units the whole variety of lively and immutable MemTables.
For read-heavy workloads, prioritize the next block cache tuning over write buffers to scale back disk I/O. You’ll be able to configure SST block measurement and caching as follows:
“spark.sql.streaming.stateStore.rocksdb.blockSizeKB”: “64”
“spark.sql.streaming.stateStore.rocksdb.blockCacheSizeMB”: “128”
blockSizeKB (default: 4, recommended: 64–128): When an lively MemTable is full, it turns into a read-only memTable. From there, new writes proceed to build up in a brand new desk. The read-only MemTable is flushed into SST information on the disk. The info in SST information is roughly chunked into fixed-sized blocks (default is 4 KB). Every block, in flip, retains a number of knowledge entries. When writing knowledge to SST information, you may compress or encode knowledge effectively inside a block, which frequently ends in a smaller knowledge measurement in contrast with its uncooked format.
For workloads with a small state measurement (resembling lower than 10 GB), the default block measurement is often ample. For a big state (resembling greater than 50 GB), growing the block measurement can enhance compression effectivity and sequential learn efficiency however improve CPU overhead.
blockCacheSizeMB (default: 8, recommended: 64–512, giant state: greater than 1024): When retrieving knowledge from SST information, RocksDB supplies a cache layer (block cache) to enhance the learn efficiency. It first locates the information block the place the goal document may reside, then caches the block to reminiscence, and at last searches that document throughout the cached block. To keep away from frequent reads of the identical block, the block cache can be utilized to maintain the loaded blocks in reminiscence.
2. Clear up state knowledge at checkpoint
To assist be sure that your state file sizes and storage prices stay below management when checkpoint efficiency turns into a priority, use the next Spark configurations to regulate cleanup frequency, retention limits, and checkpoint file varieties:
# clear up RocksDB state each 30 seconds
“spark.sql.streaming.stateStore.maintenanceInterval”:”30s”
# retain solely the final 50 state variations
“spark.sql.streaming.minBatchesToRetain”:”50″
# use changelog as a substitute of snapshots
“spark.sql.streaming.stateStore.rocksdb.changelogCheckpointing.enabled”:”true”
maintenanceInterval (default: 60 seconds): Retaining a state for an extended time frame might help cut back upkeep value and background IO. Nevertheless, longer intervals improve file itemizing time, as a result of state shops usually scan each retained file.
minBatchesToRetain (default: 100, recommended: 10–50): Limits the variety of state variations retained at checkpoint places. Decreasing this quantity ends in fewer information being endured and reduces storage utilization.
changelogCheckpointing (default: false, recommended: true): Historically, RocksDB snapshots and uploads incremental SST information to checkpoint. To keep away from this value, changelog checkpointing was launched in Amazon EMR7.0+ and AWS Glue 5.0, which write solely state adjustments for the reason that final checkpoint.
To trace an SST file’s retention standing, you may search RocksDBFileManager entries within the executor logs. Contemplate the next logs in Amazon EMR on EKS for example. The output (proven within the screenshot) reveals that 4 SST information below model 102 have been uploaded to an S3 checkpoint location, and that an previous changelog state file with model 97 was cleaned up.
cat /var/log/spark/person/-/stderr/ present | grep RocksDBFileManager
or
kubectl logs -n emr -c spark-kubernetes-executor | grep RocksDBFileManager
3. Optimize native disk utilization
RocksDB consumes native disk house when producing SST information at every Spark executor. Whereas disk utilization doesn’t scale linearly, RocksDB can accumulate storage over time primarily based on state knowledge measurement. When operating streaming jobs, if native out there disk house will get inadequate, No house left on gadget errors can happen.
To optimize disk utilization by RocksDB, alter the next Spark configurations:
# compact state information throughout commit (default:false)
“spark.sql.streaming.stateStore.rocksdb.compactOnCommit”: “true”
# variety of delta SST information earlier than turns into a consolidated snapshot file(default:10)
“spark.sql.streaming.stateStore.minDeltasForSnapshot”: “5”
Infrastructure changes can additional mitigate the disk difficulty:
For Amazon EMR:
For AWS Glue:
Use AWS Glue G.2X or bigger employee varieties to keep away from the restricted disk capability of G.1X staff.
Schedule common upkeep home windows at optimum timing to release disk house primarily based on workload wants.
Conclusion
On this submit, we explored RocksDB because the new state retailer implementation in Apache Spark Structured Streaming, out there on Amazon EMR and AWS Glue. RocksDB provides benefits over the default HDFS-backed in-memory state retailer, significantly for functions coping with large-scale stateful operations. RocksDB helps forestall JVM reminiscence stress and rubbish assortment points frequent with the default state retailer.
The implementation is easy, requiring minimal configuration adjustments, although it is best to pay cautious consideration to reminiscence and disk house administration for optimum efficiency. Whereas RocksDB just isn’t assured to scale back job latency, it supplies a strong answer for dealing with large-scale stateful operations in Spark Structured Streaming functions.
We encourage you to judge RocksDB in your use circumstances, significantly in case you’re experiencing reminiscence stress points with the default state retailer or have to deal with giant quantities of state knowledge in your streaming functions.
In regards to the authors
Melody Yang is a Senior Large Knowledge Answer Architect for Amazon EMR at AWS. She is an skilled analytics chief working with AWS prospects to offer greatest apply steering and technical recommendation with a purpose to help their success in knowledge transformation. Her areas of pursuits are open-source frameworks and automation, knowledge engineering and DataOps.
Dai Ozaki is a Cloud Help Engineer on the AWS Large Knowledge Help crew. He’s keen about serving to prospects construct knowledge lakes utilizing ETL workloads. In his spare time, he enjoys taking part in desk tennis.
Noritaka Sekiyama is a Principal Large Knowledge Architect with Amazon Net Companies (AWS) Analytics companies. He’s chargeable for constructing software program artifacts to assist prospects. In his spare time, he enjoys biking on his street bike.
Amir Shenavandeh is a Sr Analytics Specialist Options Architect and Amazon EMR subject material knowledgeable at Amazon Net Companies. He helps prospects with architectural steering and optimisation. He leverages his expertise to assist individuals carry their concepts to life, specializing in distributed processing and large knowledge architectures.
Xi Yang is a Senior Hadoop System Engineer and Amazon EMR subject material knowledgeable at Amazon Net Companies. He’s keen about serving to prospects resolve difficult points within the Large Knowledge space.
