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Spark improve write performance?
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Spark improve write performance?
You are nowhere asking spark to reduce the existing partition count of the dataframe. I am trying to find the most efficient way to read them, uncompress and then write back in parquet format. For example, in Databricks Community Edition the sparkparallelism is only 8 ( Local Mode single machine. 4. Formats that are slow to serialize objects into, or consume a large number of bytes, will greatly slow down the computation. I am trying to find the most efficient way to read them, uncompress and then write back in parquet format. Books can spark a child’s imaginat. One often overlooked factor that can greatly. spark = SparkSession. code # Create a DataFrame with 6 partitions initial_df = df. Increase the shuffle buffer by increasing the memory in your executor processes ( sparkmemory) Increase the shuffle buffer by increasing the fraction of executor memory allocated to it ( sparkmemoryFraction) from the default of 0 Refresh the page, check Medium 's site status, or find something interesting to read. Auto compaction occurs after a write to a table has succeeded and runs synchronously on the cluster that has performed the write. A Spark DataFrame can be created from the SparkContext object as follows: from pyspark. However, it turns out be a very slow operation. Spark can handle tasks of 100ms+ and recommends at least 2-3 tasks per core for an executor. My code is extremely simple but it runs very very slow. stalenessLimit with a time string value such as 1h or 15m. Set refresh interval to -1 and replications to '0' and other basic configurations required for better writing. If the data size becomes larger than the storage size, accessing and managing the data efficiently become challenging. Adjust the number of buckets and the columns as needed for your specific use case. 4. Spark SQL can turn on and off AQE by sparkadaptive. You can increase the size of the write buffer to reduce the number of requests made to S3 and. Sparks dataframe. Using the AWS Glue console: To enable metrics on an existing job, do the following: Open the AWS Glue console. I need write about 1 million rows from Spark a DataFrame to MySQL but the insert is too slow. Formats that are slow to serialize objects into, or consume a large number of bytes, will greatly slow down the computation. For some workloads, it is possible to improve performance by either caching data in memory, or by turning on some experimental options. None of the resources seem to be the bottleneck: tserver cpu usage ~3-4 core, RAM 10G, no disk congestion. Performance reviews are an important part of any business. The above answer will improve the performance slightly but if I'm correct using a list will give you a O(n^2) time complexity. This section explains Apache Spark basic concepts and key topics for tuning AWS Glue for Apache Spark performance. Hudi provides best indexing performance when you model the recordKey to be monotonically increasing (e. Learn how the improved Adaptive Query Execution framework in Spark 3. Optimize Write is a Delta Lake on Synapse feature that reduces the number of files written and aims to increase individual file size of the written data. My result set is close to ten million records, and it takes a few minutes to write them to the table. @Pablo (Ariel) : There are several ways to improve the performance of writing data to S3 using Spark. If set to 'true', Kryo will throw an exception if an unregistered. 3 RDD caching can significantly improve performance by storing intermediate results in memory. I am converting the pyspark dataframe to pandas and then saving it to a csv file. The EMRFS S3-optimized committer is a new output committer available for use with Apache Spark jobs as of Amazon EMR 50. I am trying to write to save a Spark DataFrame to Oracle. Caching in DBSQL can significantly improve the performance of iterative or repeated computations by reducing the time required for data retrieval and processing. If the data size becomes larger than the storage size, accessing and managing the data efficiently become challenging. The capability of the storage system creates some important physical limits for the performance of MongoDB's write operations. The late Robin Williams touched countless lives throughout his illustrious career. RDD is used for low level operation with less optimization. After some further performance testing, we noticed that additional tuning on Spark side didn't have much effect, but increasing our Azure SQL Server database tier had a very substantial impact. Serialization plays an important role in the performance of any distributed application. Environment: Spark 30; DeltaLake 00; In context this is about making an incremental table via DeltaLake, I'll summarize this in steps to be more detailed: Creation of the base table (delta) Obtaining. One solution is to increase the number of executors, which will improve the read performance but not sure if it will improve writes? In Spark 3. They provide a way to evaluate employee performance and identify areas for improvement. as("SOLID_STOCK_UNIT_sum") I would like to tune the performance of this program. Each row roughly 160 bytes. The cluster i have has is 6 nodes with 4 cores each. With so many options available in the market, it can be overwhelming t. This is the exact opposite of your data, where input is wide and (relatively) short. Beware of Duplicates!! Mar 17, 2023. Then we execute the same queries as below. Essentially, PySpark creates a set of transformations that describe how to transform the input data into the output data. Next, column-level value counts, null counts, lower bounds, and upper bounds are used to eliminate files that cannot match the query predicate. enabled as an umbrella configuration. 0. Spark SQL defaults to reading and writing data in Snappy compressed Parquet files. Spark can be extended to support many more formats with external data sources - for more information, see Apache Spark packages. How to improve performance of spark Create table in spark taking a lot of time A schema mismatch detected when writing to the Delta table Spark Job stuck writing dataframe to partitioned Delta table DataBricks: Fastest Way to Insert Data Into Delta Table? 2. Optimize SQL query speed on Delta Lake with Dynamic File Pruning, improving performance by skipping irrelevant data files. However, running fairly simple computations on Spark takes a little while, frequently a few dozens. 3 RDD caching can significantly improve performance by storing intermediate results in memory. Spark : 2 node EMR cluster with 2 Core instances 8 vCPU, 16 GiB memory, EBS only storage EBS Storage:1000 GiB 1 Master node. It dynamically optimizes partitions while generating files with a default 128-MB size. I have been using Spark Data Source to write to Kudu from Parquet, and the write performance is terrible: about 12000 rows / seconds. It dynamically optimizes partitions while generating files with a default 128-MB size. Key Takeaways: Understand your data and workload to optimize Spark performance. At this moment with pseudocode below, it takes around 8 hrs to read all the files and writing back to parquet is very very slow. To do this, Spark needs to shuffle the data such that records with the same name are in the same partition: (1, "John") (1, "John") (2, "Jane") (3, "Joe") Now the aggregation can be run in. Caching Data In Memory. Spark offers two types of operations: Actions and Transformations. Transformations (eg. Set refresh interval to -1 and replications to '0' and other basic configurations required for better writing. However, running fairly simple computations on Spark takes a little while, frequently a few dozens. To use the optimize write feature, enable it using the following configuration: Scala and PySpark; sparkset("sparkdeltaenabled", "true. 6. S3 Select allows applications to retrieve only a subset of data from an object. With hundreds of knobs to turn, it is always an uphill battle to squeeze more out of Spark pipelines. Once the configuration is set for the pool or session, all Spark write patterns will use the functionality. cache () anywhere will not provide any performance improvement. It's taking about 15 minutes to insert a 500MB ndjson file with 100,000 rows into MS SQL Server table. partitionBy("partition_date") is actually writing the data in S3 partition and if your dataframe has say 90 partitions it will write 3 times faster (3 *30)repartition() is forcing it to slow it down. The computations are pretty fast enough, but I am hitting a roadblock with the write performance in Hive. You can try using a faster network, such as Azure ExpressRoute, to improve the write performance. Optimize Write is a Delta Lake on Synapse feature that reduces the number of files written and aims to increase individual file size of the written data. I am performing various calculations (using UDFs) on Hive. This can help us to improve our writing performance. Use optimal data format. arriva 375 bus timetable Using the copy write semantics, you will be able to load data in Synapse faster. Call coalesce when reducing the number of partitions, and repartition when increasing the number of partitionsapachesql val df = spark. executor-memory, sparkmemoryOverhead, sparkshuffle. So, when you execute df3. See Predictive optimization for Delta Lake. Spark SQL can turn on and off AQE by sparkadaptive. ) is being executed on the same DataFrame. Optimize Write is a Delta Lake on Synapse feature that reduces the number of files written and aims to increase individual file size of the written data. Spark SQL can cache tables using an in-memory columnar format by calling sparkcacheTable("tableName") or dataFrame Jan 7, 2020 · The write. Apache Spark is a computational engine frequently used in a big data environment for data processing but it doesn't provide storage so in a typical scenario the output of the data processing has to…. To do this, Spark needs to shuffle the data such that records with the same name are in the same partition: (1, "John") (1, "John") (2, "Jane") (3, "Joe") Now the aggregation can be run in. If you have the expected data already available in s3, dataframe. In your case, working on a signle instance, I think you can only improve performance specifying partitionColumn, lowerBound, upperBound, numPartition to improve reading parallelism. land for sale in jackson county alabama Broadcast variables are a built-in feature of Spark that allow you to efficiently share read-only reference data across a Spark cluster. Caching is a lazy evaluation meaning it will not cache the results until you call the action operation and the result of the transformation is one. When a job is submitted, Spark calculates a closure consisting of all of the variables and methods required for a single executor to perform operations, and then sends that closure to each worker node Spark 3. enabled as an umbrella configuration. 6. Is there any other way to increase the write performance. Adaptive Query Execution (AQE) Optimizer is a feature introduced in Apache Spark 3. It becomes the de facto standard in processing big data. While developing Spark applications, one of the most time-consuming parts was optimization. Batch mode writes multiple rows. So after working with Spark for more than 3 years in production, I'm happy to share my tips and tricks for better performance. Mar 27, 2024 · Spark Performance tuning is a process to improve the performance of the Spark and PySpark applications by adjusting and optimizing system resources (CPU cores and memory), tuning some configurations, and following some framework guidelines and best practices. I have also tried using the write method to save the csv filetoPandas(). Adaptive Query Execution (AQE) is an optimization technique in Spark SQL that makes use of the runtime statistics to choose the most efficient query execution plan, which is enabled by default since Apache Spark 30. This committer improves performance when writing Apache Parquet files to… my_data = sparkcsv("my_file. This directory should allow any Spark user to read/write files and the Spark History Server user to delete files Can be disabled to improve performance if you know this is not the case8kryo. As cross join was used, I decided to divide the first dataset into several parts (each having about 250 million rows) and cross join each part with the million-row one. The read API takes an optional number of partitions. Mar 3, 2021 2. I have an application which writes key,value data to Redis using Apache Spark. count () it will evaluate all the transformations up to that point. You can do that but can't expect the processing to be finished. This article describes how to fix these issues and tune performance. Each spark plug has an O-ring that prevents oil leaks The heat range of a Champion spark plug is indicated within the individual part number. When you are working on Spark especially on Data Engineering tasks, you have to deal with partitioning to get the best of Spark. wbrznews Performance appraisals are an essential tool for managers to provide feedback and evaluate the progress of their employees. dataframeformat(“delta”). One popular brand that has been trusted by car enthusiasts for decades is. The "COALESCE" hint only has a partition number as a parameter. Spark supports many formats, such as csv, json, xml, parquet, orc, and avro. Persisit/cache the dataframe before writing : df. I am trying to find the most efficient way to read them, uncompress and then write back in parquet format. Optimize Write is a Delta Lake on Synapse feature that reduces the number of files written and aims to increase individual file size of the written data. spark = Seeing low # of writes to elasticsearch using spark java. Tune the partitions and tasks. S3 Select allows applications to retrieve only a subset of data from an object. Employee reviews are an important part of any business. Here are the Configurations using 13. The computations are pretty fast enough, but I am hitting a roadblock with the write performance in Hive. Performance is still not good enough Let's assume the table name is Fact_data. val arraydataInt = 1 to 100000 toArray.
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In this article, we tested the performance of 9 techniques for a particular use case in Apache Spark — processing arrays. Apache Spark Structured Streaming is a distributed stream processing engine built on top of the Apache Spark SQL engine. It becomes the de facto standard in processing big data. In some cases, this is a 10x performance improvement. (1) File committer - this is how Spark will read the part files out to the S3 bucket. Adaptive Query Execution (AQE) Optimizer is a feature introduced in Apache Spark 3. Dataset is highly type safe and use encoders. At this moment with pseudocode below, it takes around 8 hrs to read all the files and writing back to parquet is very very slow. They provide a way for employers to assess the performance of their employees and provide feedback that can help them improv. Optimize Write is a Delta Lake on Synapse feature that reduces the number of files written and aims to increase individual file size of the written data. Spark was in the standalone mode, and the application for test is simply pulling some data from a MySQL RDB. Running executors with too much memory often results in excessive garbage collection delays. The problem is that procedure seems get stuck in MongoSpark Job. By monitoring and optimizing resource usage, you can improve query performance and reduce processing time, ensuring that your Apache Spark cluster operates efficiently and effectively Use. enabled as an umbrella configuration. None of the resources seem to be the bottleneck: tserver cpu usage ~3-4 core, RAM 10G, no disk congestion. Less data to be shuffled, results in less shuffle time. Tip 4 - Leverage caching. lowe lighting Iam reading the data in DF, writing the DF without ay transformations using partitionBy ex: dfmode("overwrite"). 2 GB, and have defined 2. Even repartition (1) does not significantly improve write performance. ) compute a result based on an RDD/DataFrame, and either return it to the driver program or save it to the external storage system. Performance issues in Spark are mostly related to the following topics: Skew : what occurs in case of imbalance in the size of data partitions. Follow edited Jan 29, 2019 at 21:06 This will create data frame in Spark. The shuffle is Spark's mechanism for redistributing data so that it's grouped differently across RDD partitions. We will then cover tuning Spark's cache size and the Java garbage collector. This is the second article out of three covering one of the most important features of Spark and Databricks: Partitioning. Follow answered Aug 6, 2020 at 7:23 see our tips on writing great answers Sign up using Google. I created two m4. They provide a way for employers to assess the performance of their employees and provide feedback that can help them improv. One solution is to increase the number of executors, which will improve the read performance but not sure if it will improve writes? In Spark 3. saveAsTable("bucketed_table. Often, this will be the first thing you should tune to optimize a Spark application. Reducing the time Spark spends reading data (e using Predicate Pushdown with Disk. Performance appraisals are an essential tool for managers to provide feedback and evaluate the progress of their employees. In order improve the performance using PY-Spark (due to Administrative restrictions to use python, SQL and R only) one can use below options. The EMRFS S3-optimized committer is an alternative to the OutputCommitter class, which uses the. answered Jun 20, 2018 at 22:49 ⇖ Introducing Broadcast Variables. This chapter describes factors that affect operational performance, and how to tune Neo4j for optimal throughput. 1. orc("hdfs path") As i monitored job status in spark UI - the job and stage is getting completed in 20minutes. Each operation is distinct and will be based uponhadoopfileoutputcommitterversion 2. But beyond their enterta. dasaquin Improving Spark job performance while writing Parquet by 300%. In this article, we tested the performance of 9 techniques for a particular use case in Apache Spark — processing arrays. Take advantage of the cluster resources by understanding the available hardware and configuring Spark accordingly. ; This chapter will go into the specifics of table partitioning and we will prepare our dataset. so Each folder contains about 288 parquet files. Optimizing join performance involves fine-tuning various Spark configurations and resource allocation. Spark SQL can cache tables using an in-memory columnar format by calling sparkcacheTable("tableName") or dataFrame Then Spark SQL will scan only required columns and will automatically tune compression to minimize memory usage and GC pressurecatalog. Spark needs to serialize the data before storing it in memory and deserialize it when accessing it. Formats that are slow to serialize objects into, or consume a large number of bytes, will greatly slow down the computation. Formats that are slow to serialize objects into, or consume a large number of bytes, will greatly slow down the computation. Here is an example of a poorly performing MERGE INTO query without partition pruning. Spark can handle tasks of 100ms+ and recommends at least 2-3 tasks per core for an executor. Two key general approaches which can be used to increase Spark performance under any circumstances are: Reducing the amount of data ingested. Increase the shuffle buffer by increasing the memory in your executor processes ( sparkmemory) Increase the shuffle buffer by increasing the fraction of executor memory allocated to it ( sparkmemoryFraction) from the default of 0 Refresh the page, check Medium 's site status, or find something interesting to read. I will be reading the data using spark. Aug 27, 2020 · Dataset dataSet = JavaEsSparkSQLgetSqlContext(), indexAlias, esConfigParam()); // 3 dataSetmode(SaveModeoption("compression", "gzip") getWritePath()); I am thinking of below as a tuning point to improve performance. However, it turns out be a very slow operation. Mar 27, 2024 · Spark Performance tuning is a process to improve the performance of the Spark and PySpark applications by adjusting and optimizing system resources (CPU cores and memory), tuning some configurations, and following some framework guidelines and best practices. Serialization plays an important role in the performance of any distributed application. write might be less efficient when compared to using copy command on s3 path directly. The EMRFS S3-optimized committer is a new output committer available for use with Apache Spark jobs as of Amazon EMR 50. r34 lopunny Top use cases are Streaming Data, Machine Learning, Interactive Analysis and more. You can configure it before running the write command, in this way: sparkset("sparksqldw. Serialization plays an important role in the performance of any distributed application. answered Oct 15, 2022 at 20:40. Example: Bucketing in Spark SQL organizes data into buckets, improving query performance during joins. map(MyObject::new); Spark can only run 1 concurrent task for every partition of an RDD, up to the number of cores in your cluster. Feb 13, 2017 at 8:55. So spark will create new folders inside the output_path and write data corresponding to each partitions inside it. Adaptive Query Execution (AQE) is an optimization technique in Spark SQL that makes use of the runtime statistics to choose the most efficient query execution plan, which is enabled by default since Apache Spark 30. However, it turns out be a very slow operation. The EMRFS S3-optimized committer is an alternative to the OutputCommitter class, which uses the. The capability of the storage system creates some important physical limits for the performance of MongoDB's write operations. I have a spark job where I need to write the output of the SQL query every micro-batch. Monitoring network bandwidth can help identify bottlenecks and improve write. DJI previously told Quartz that its Phantom 4 drone was the first drone t.
Adaptive Query Execution (AQE) is an optimization technique in Spark SQL that makes use of the runtime statistics to choose the most efficient query execution plan, which is enabled by default since Apache Spark 30. Spark persisting/caching is one of the best techniques to improve the performance of the Spark workloads. This committer improves performance when writing Apache Parquet files to Amazon S3 using the EMR File System (EMRFS). val addOne = udf( (num: Int) => num + 1 ) val res1 = df. It dynamically optimizes partitions while generating files with a default 128-MB size. Mar 27, 2024 · Spark Performance tuning is a process to improve the performance of the Spark and PySpark applications by adjusting and optimizing system resources (CPU cores and memory), tuning some configurations, and following some framework guidelines and best practices. Serialization plays an important role in the performance of any distributed application. By its distributed and in-memory working principle, it is supposed to perform fast by default. lowes card credit Spark SQL can cache tables using an in-memory columnar format by calling sparkcacheTable("tableName") or dataFrame Jan 7, 2020 · The write. The data load job took 1hr. Compression is about saving space, not performance, so the fact you're using Snappy is not really a relevant detail as you could use LZ4 or ZSTD instead, for example. Performance is still not good enough Let's assume the table name is Fact_data. Here are some optimizations for faster running. orc("hdfs path") As i monitored job status in spark UI - the job and stage is getting completed in 20minutes. I am trying to find the most efficient way to read them, uncompress and then write back in parquet format. victoria secret pink yoga pants This is 300 MB by default and is used to prevent out of memory (OOM) errors. cache() in my script before df. Key: --enable-metrics. orc("hdfs path") As i monitored job status in spark UI - the job and stage is getting completed in 20minutes. Call coalesce when reducing the number of partitions, and repartition when increasing the number of partitionsapachesql val df = spark. Spark Cache and P ersist are optimization techniques in DataFrame / Dataset for iterative and interactive Spark applications to improve the performance of Jobs. bedpage austin Here are 7 tips to fix a broken relationship. partitionBy("partition_date") is actually writing the data in S3 partition and if your dataframe has say 90 partitions it will write 3 times faster (3 *30)repartition() is forcing it to slow it down. This parallelism empowers Spark to concurrently process different data segments, harnessing the distributed computing capabilities and optimizing overall performanceappName("ParquetExample. That's a lot of copying, converting and moving things. This is an important step as each workload is unique, and. enabled as an umbrella configuration. Now we can change the code slightly to make it more performant. Auto compaction combines small files within Delta table partitions to automatically reduce small file problems.
Using Bulk Indexing in Elastic Style to reduce index time! The Better approach: Using Spark Native Plugin for Indexing Include elasticsearch-hadoop as a dependency: 2. It's taking about 15 minutes to insert a 500MB ndjson file with 100,000 rows into MS SQL Server table. Caching Data In Memory. Here is an example of a poorly performing MERGE INTO query without partition pruning. How to improve performance of spark Create table in spark taking a lot of time A schema mismatch detected when writing to the Delta table Spark Job stuck writing dataframe to partitioned Delta table DataBricks: Fastest Way to Insert Data Into Delta Table? 2. Caching Data In Memory. Then you might have a better lack. Consequently, a many spark Extract, Transform & Load (ETL) jobs write data back to s3, highlighting the importance of speeding up these writes to improve overall ETL pipeline efficiency and speed. I am trying to find the most efficient way to read them, uncompress and then write back in parquet format. We also compared different approaches for user. # Bucketing Example dfbucketBy(100, "column_name"). Hudi provides best indexing performance when you model the recordKey to be monotonically increasing (e. Aug 27, 2020 · Dataset dataSet = JavaEsSparkSQLgetSqlContext(), indexAlias, esConfigParam()); // 3 dataSetmode(SaveModeoption("compression", "gzip") getWritePath()); I am thinking of below as a tuning point to improve performance. pomeranian chihuahua mix Aug 27, 2020 · Dataset dataSet = JavaEsSparkSQLgetSqlContext(), indexAlias, esConfigParam()); // 3 dataSetmode(SaveModeoption("compression", "gzip") getWritePath()); I am thinking of below as a tuning point to improve performance. One solution is to increase the number of executors, which will improve the read performance but not sure if it will improve writes? In Spark 3. Coalesce hints allows the Spark SQL users to control the number of output files just like the coalesce, repartition and repartitionByRange in Dataset API, they can be used for performance tuning and reducing the number of output files. S3 Select can improve query performance for CSV and JSON files in some applications by "pushing down" processing to Amazon S3 The EMRFS S3-optimized committer is an alternative to the OutputCommitter class, which uses the multipart uploads feature of EMRFS to improve. 9. If you have the expected data already available in s3, dataframe. Key Takeaways: Understand your data and workload to optimize Spark performance. The era of flying selfies may be right around the corner. It dynamically optimizes partitions while generating files with a default 128-MB size. Optimize Write is a Delta Lake on Synapse feature that reduces the number of files written and aims to increase individual file size of the written data. configurations required for better writing. At this moment with pseudocode below, it takes around 8 hrs to read all the files and writing back to parquet is very very slow. Startup scripts can be helpful time-saving devices for those familiar with Visual Basic, the programming language of startup scripts, that can perform multiple tasks automatically,. I have a script to do write a dataframe to synapse, the flow of my script is Read from Synapse > Transform data on Databricks (GROUP BY CUBE) > Write to Synapse, the data that I read from synapse has 150 million row (Fact Table), I do GROUP BY CUBE to transform the fact table. So for January month, it is about 8928(31*288) parquet files. Mar 27, 2024 · Spark Performance tuning is a process to improve the performance of the Spark and PySpark applications by adjusting and optimizing system resources (CPU cores and memory), tuning some configurations, and following some framework guidelines and best practices. Welcome to the 3rd and final part of my big data with Apache Spark blog series. My requirement is to load dataframe to Teradata. Catalyst Optimizer: A query optimization engine that is used by Spark to generate efficient execution plans for DataFrame and Dataset queries I am specifically looking to optimize performance by updating and inserting data to a DeltaLake base table, with about 4 trillion records. power automate file system move file When you launch an EMR cluster, it comes with the emr-hadoop-ddb. Formats that are slow to serialize objects into, or consume a large number of bytes, will greatly slow down the computation. The focus is only on the information that is not obvious from the UI and the inferences to draw from this non-obvious information. I have a script to do write a dataframe to synapse, the flow of my script is Read from Synapse > Transform data on Databricks (GROUP BY CUBE) > Write to Synapse, the data that I read from synapse has 150 million row (Fact Table), I do GROUP BY CUBE to transform the fact table. For some workloads, it is possible to improve performance by either caching data in memory, or by turning on some experimental options. Unveiling PySpark Performance Optimization. Aug 27, 2020 · Dataset dataSet = JavaEsSparkSQLgetSqlContext(), indexAlias, esConfigParam()); // 3 dataSetmode(SaveModeoption("compression", "gzip") getWritePath()); I am thinking of below as a tuning point to improve performance. Many known companies uses it like Uber, Pinterest and more. Improve performance for Delta Lake merge You can configure tolerance for stale data by setting the Spark session configuration sparkdelta. We have implemented similar use case - we have a REST service which triggers a spark job. Spark needs to serialize the data before storing it in memory and deserialize it when accessing it. Test 2: The running is very slow than the first code above, so the performance is very bad. PySpark cache () method is used to cache the intermediate results of the transformation into memory so that any future transformations on the results of cached transformation improve the performance. It dynamically optimizes partitions while generating files with a default 128-MB size. For some workloads, it is possible to improve performance by either caching data in memory, or by turning on some experimental options. This is especially useful for large variables like lookup tables. 1. For some workloads, it is possible to improve performance by either caching data in memory, or by turning on some experimental options. Through Spark UI , I was able to see that the bottleneck of performance occurs at the stage that exports the data into csv files. Configure Elasticsearch settings: Things to keep in mind while using Bulk API. Choosing an efficient serialization format, such as Apache Parquet or Apache Arrow, can improve the caching performance by reducing the memory footprint and reducing the CPU overhead of serialization and deserialization. Apache Spark is quickly gaining steam both in the headlines and real-world adoption. uncacheTable("tableName") to remove the table from memory. Sparks dataframe.