Hive : How can you increase parallelism in Hive?

Introduction to Parallelism in Hive:

Parallelism refers to the ability to execute multiple tasks simultaneously. In the context of Hive, parallelism is used to speed up data processing by dividing a large data set into smaller subsets and processing them in parallel on multiple nodes or cores. This can significantly reduce the time it takes to complete a data processing job.

In Hive, parallelism can be increased by optimizing the query execution plan and configuring various properties to control the degree of parallelism. In this article, we will discuss some techniques to increase parallelism in Hive.

Techniques to Increase Parallelism in Hive:

1. Partitioning: Partitioning is the process of dividing a large table into smaller, more manageable pieces called partitions. By partitioning a table, you can limit the amount of data that needs to be processed at any given time, which can improve query performance. Hive supports both static and dynamic partitioning.
2. Bucketing: Bucketing is a technique that involves dividing data into buckets based on the values of a specific column. This can be used to evenly distribute data across different nodes, which can improve parallelism. Bucketing is especially useful when you need to perform joins on large tables.
3. MapReduce Settings: Hive uses MapReduce to execute queries. You can increase the parallelism of MapReduce jobs by adjusting the settings in the MapReduce configuration file. For example, you can increase the number of mappers and reducers used in a job, which can improve parallelism.
4. Table Statistics: Hive uses table statistics to optimize query execution plans. By collecting statistics about your tables, Hive can make better decisions about how to execute queries. You can collect table statistics using the ANALYZE TABLE command.
5. Execution Engine: Hive supports two execution engines: MapReduce and Tez. Tez is a newer and more efficient execution engine that can improve parallelism. You can switch to Tez by setting the hive.execution.engine configuration property to “tez”.
6. Compression: Compression is a technique that can be used to reduce the amount of data that needs to be processed. Compressed data can be read and written more quickly, which can improve query performance. Hive supports several compression codecs, including Snappy and Gzip.

Configuring Parallelism in Hive:

To increase parallelism in Hive, you can use the following techniques:

1. Partitioning: To partition a table, you can use the PARTITIONED BY clause when creating a table. For example:

CREATE TABLE my_table (col1 string, col2 int)
PARTITIONED BY (col3 string);

CREATE TABLE my_table (col1 string, col2 int)
CLUSTERED BY (col1) SORTED BY (col2) INTO 10 BUCKETS;

mapreduce.job.maps=<number of mappers>
mapreduce.job.reduces=<number of reducers>

ANALYZE TABLE my_table COMPUTE STATISTICS;

SET hive.execution.engine=tez;