Instead of querying the whole dataset, it will query partitioned dataset. With partitioning, data is stored in separate individual folders on HDFS. ORC supports compressed (ZLIB and Snappy), as well as uncompressed storage. Select * from Employee_Details Insert into Employee_Details_ORC Select a.EmployeeID, a.EmployeeName, b.Address,b.Designation from Employee_ORC a Select * from Employee Insert into Employee_ORC Ĭreate Table Employee_Details_ORC (EmployeeID int, Address varchar(100) STORED AS ORC tblproperties("compress.mode"="SNAPPY") ![]() Create Table Employee_ORC (EmployeeID int, EmployeeName varchar(100),Age int) Converting this table into ORCFile format will significantly reduce the query execution time. Select a.EmployeeID, a.EmployeeName, b.Address,b.Designation from Employee aĪbove query will take a long time, as the table is stored as text. Let's say we will use join to fetch details from both tables. It uses techniques like predicate push-down, compression, and more to improve the performance of the query.Ĭonsider two tables: employee and employee_details, tables that are stored in a text file. The ORCFile format is better than the Hive files format when it comes to reading, writing, and processing the data. Optimized Row Columnar format provides highly efficient ways of storing the hive data by reducing the data storage format by 75% of the original. Vectorization can be enabled in the environment by executing below commands. It improves the performance for operations like filter, join, aggregation, etc. Vectorization improves the performance by fetching 1,024 rows in a single operation instead of fetching single row each time. Tez engine can be enabled in your environment by setting to tez: set =tez Use Vectorization Tez improved the MapReduce paradigm by increasing the processing speed and maintaining the MapReduce ability to scale to petabytes of data. Use Tez EngineĪpache Tez Engine is an extensible framework for building high-performance batch processing and interactive data processing. Hive provides an SQL-like interface to query data stored in various data sources and file systems. Both file formats utilize different function for data retrieval: HiveTableScan vs.Apache Hive is a data warehouse built on the top of Hadoop for data analysis, summarization, and querying.Compare the ORC and Parquet executions with Snappy Configuration:.ORDER BY item_sk, review_sentence, sentiment, sentiment_word SELECT extract_sentiment ( pr_item_sk, pr_review_content )ĪS ( item_sk, review_sentence, sentiment, sentiment_word ) ![]() SELECT item_sk, review_sentence, sentiment, sentiment_word Exceptions are the queries involving text processing, which do not benefit from using any compression. Using ZLIB compression brings up to 60.2% improvement with ORC, while Parquet achieves up to 7% improvement with Snappy. We show that ORC generally performs better on Hive, whereas Parquet achieves best performance with SparkSQL. Our experiments confirm that the file format selection and its configuration significantly affect the overall performance. We use BigBench (TPCx-BB), a standardized application-level benchmark for Big Data scenarios. We apply our strategy to two processing engines, Hive and SparkSQL, and evaluate the performance of two columnar file formats, ORC and Parquet. In this work, we propose an alternative approach: by executing each file format on the same processing engine, we compare the different file formats as well as their different parameter settings. Related works consider the performance of processing engine and file format together, which makes it impossible to predict their individual impact. Columnar file formats provide an efficient way to store data to be queried by SQL-on-Hadoop engines.
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