Improving SparkSQL Performance by 30%: How We Optimize Parquet Pushdown and Parquet Reader
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The document discusses optimizations made to Spark SQL performance when working with Parquet files at ByteDance. It describes how Spark originally reads Parquet files and identifies two main areas for optimization: Parquet filter pushdown and the Parquet reader. For filter pushdown, sorting columns improved statistics and reduced data reads by 30%. For the reader, splitting it to first filter then read other columns prevented loading unnecessary data. These changes improved Spark SQL performance at ByteDance without changing jobs.
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Improving SparkSQL Performance by 30%: How We Optimize Parquet Pushdown and Parquet Reader
- 1. Improving Spark SQL Performance by 30%: How We Optimize Parquet Filter Pushdown and Parquet Reader Ke Sun ([email protected]) Senior Engineer of Data Engine Team, ByteDance
- 2. Who We Are l Data Engine team of ByteDance l Build a platform of one-stop experience for OLAP , on which users can analyze EB level data by writing SQL without caring about the underlying execution engine
- 3. What We Do l Manage Spark SQL / Presto / Hive workload l Offer open API and serverless OLAP platform l Optimize Spark SQL / Presto / Hudi / Hive engine l Design data architecture for most business lines in ByteDance
- 4. Agenda Spark SQL at ByteDance How Spark Read Parquet Optimization of Parquet Filter Pushdown and Parquet Reader at ByteDance
- 5. Spark SQL at ByteDance
- 6. 2017 2019 20202016 2018 Spark SQL at ByteDance Small Scale Experiments Ad-hoc Workload Few ETL Workload Full-production deployment & migration Main engine in DW area
- 7. Spark SQL at ByteDance ▪ Spark SQL covers 98%+ ETL workload ▪ Parquet is the default file format in data warehouse and vectorizedReader is also enabled by default
- 8. How Spark Read Parquet
- 9. How Spark Read Parquet ▪ Overview of Parquet ▪ Procedure of Parquet Reading ▪ What We can Optimize
- 10. How Spark Read Parquet ▪ Overview of Parquet Column Pruning More efficient compression Parquet can skip useless data by spark filter pushdown using Footer & RowGroup statistics https://raw.github.com/apache/parquet-format/master/doc/images/FileLayout.gif
- 11. How Spark Read Parquet ▪ Procedure of Parquet Reading ▪ VectorizedParquetRecordReader skips useless RowGroups by filter pushdown translated by ParquetFilters ▪ VectorizedParquetRecordReader builds column readers for every target column and these column readers read data together in batch DataSourceScanExec .inputRDD ParquetFileFormat .buildReaderWithPartitionValues VectorizedParquetRecordReader . nextBatch
- 12. How Spark Read Parquet ▪ Optimization – Statistics are not distinguishable select * from table_name where date = ‘***’ and category = ‘test’ (date is partition column and category is predicate column) For example, Spark reads all the 3 RowGroups for statistics are not distinguishable. min of category max of category read or not RowGroup1 a1 z1 Yes RowGroup2 a2 z2 Yes RowGroup3 a3 z3 Yes Min/Max Statistics of RowGroup for a Parquet File
- 13. How Spark Read Parquet ▪ Optimization – Statistics are not distinguishable Parquet filter pushdown works poorly when predicate columns are out of order in parquet files and this phenomenon is reasonable It is valuable to sort the common used predicate columns in parquet files to reduce IO
- 14. How Spark Read Parquet ▪ Optimization – Spark reads too much unnecessary data select col1 from table_name where date = ‘***’ and col2 = ‘test’ col1 col2 col3 col1 col2 col3 col1 col2 col3 ParquetFile RowGroup1 RowGroup2 RowGroup3 ▪ RowGroup1 is skipped by filter pushdown ▪ Col3 is skipped by column pruning ▪ Col1 and col2 are read together by vectorized reader
- 15. How Spark Read Parquet ▪ Optimization – Spark reads too much unnecessary data select col1 from table_name where date = ‘***’ and col2 = ‘test’ col1 col2 col3 col1 col2 col3 col1 col2 col3 ParquetFile RowGroup1 RowGroup2 RowGroup3 ▪ Most data of col1 is unnecessary to read if filter ratio (col2=‘test’) is very high ▪ It is valuable to read & filter data by filter columns firstly and then read data of other columns
- 16. Optimization of Parquet Filter Pushdown and Parquet Reader at ByteDance
- 17. Optimization of Parquet Filter Pushdown ▪ Statistics are not distinguishable ▪ Increase parquet statistics discrimination ▪ Low Overhead: sort all data is expensive or even impossible ▪ Automation: users do not need to update ETL jobs LocalSort: Add a SortExec node before InsertIntoHiveTable node
- 18. ▪ Which columns to be sorted? ▪ Analyze the history queries and choose the most common used predicate columns ▪ Configure sort columns to table property of hive table and Spark SQL will read this property ▪ It is a automatic procedure without manual intervention Optimization of Parquet Filter Pushdown … Project SortExec … Project InsertIntoHiveTable InsertIntoHiveTable
- 19. Optimization of Parquet Filter Pushdown ▪ Spark reads less data for more efficient statistics ▪ Parquet file size is much smaller for data is more efficient to compress ▪ Only near 5% overhead Spark reads only one RowGroup after sorting data by column category min of category max of category read or not RowGroup1 a1 g1 No RowGroup2 g2 u2 Yes RowGroup3 u3 z3 No Min/Max Statistics of RowGroup for a Parquet File
- 20. Optimization of Parquet Reader ▪ Spark reads too much unnecessary data ▪ Filter unnecessary data as soon as possible Prewhere: Read data of filter columns in batch firstly and skip other columns if unmatched (It is a good idea from ClickHouse)
- 21. Optimization of Parquet Reader ▪ Split parquet reader into 2 reader: FilterReader for filter columns and NonFilterReader for other columns col1 col2 col3 col1 col2 col3 col1 col2 col3 RowGroup1 RowGroup2 RowGroup3 VectorizedParquetRecordReader col1 col2 col3 col1 col2 col3 col1 col2 col3 FilterReader col1 col2 col3 col1 col2 col3 col1 col2 col3 NonFilterReader PrewhereVectorizedParquetRecordReader
- 22. Optimization of Parquet Reader ▪ Skip Page ▪ Skip Decoding ▪ Skip RowGroup Potential BenefitFilterReader reads data in batch Apply filter expressions to data match NonFilterReader reads data in batch and skip unnecessary data N Y Union data and return in batch
- 23. Optimization of Parquet Reader ▪ ByteType ▪ ShortType ▪ IntegerType ▪ LongType ▪ FloatType ▪ DoubleType ▪ StringType ▪ > ▪ >= ▪ < ▪ <= ▪ = ▪ In ▪ isNull ▪ isNotNull Supported Filter TypeSupported Data Type of Filter Column
- 24. Databricks simplifies data and AI so data teams can innovate faster
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