How can we improve the performance of Hive queries?

Hive Performance – 10 Best Practices for Apache Hive

1. Partitioning Tables: Hive partitioning is an effective method to improve the query performance on larger tables.
2. De-normalizing data:
3. Compress map/reduce output:
4. Map join:
5. Input Format Selection:
6. Parallel execution:
7. Vectorization:
8. Unit Testing:

Why is hive slower than Impala?

These days, Hive is only for ETLs and batch-processing. Impala is faster than Hive because it’s a whole different engine and Hive is over MapReduce (which is very slow due to its too many disk I/O operations).

Which is faster hive or Spark?

Hive and Spark are both immensely popular tools in the big data world. Hive is the best option for performing data analytics on large volumes of data using SQLs. Spark, on the other hand, is the best option for running big data analytics. It provides a faster, more modern alternative to MapReduce.

How do you improve group by performance in Hive?

Below are the list of practices that we can follow to optimize Hive Queries.

1. Enable Compression in Hive.
2. Optimize Joins.
3. Avoid Global Sorting in Hive.
4. Enable Tez Execution Engine.
5. Optimize LIMIT operator.
6. Enable Parallel Execution.
7. Enable Mapreduce Strict Mode.
8. Single Reduce for Multi Group BY.

How partitioning and bucketing improves the performance of Hive?

Both Partitioning and Bucketing in Hive are used to improve performance by eliminating table scans when dealing with a large set of data on a Hadoop file system (HDFS). A table can have one or more partitions that correspond to a sub-directory for each partition inside a table directory.

What Enhancement do both Hive and Impala provide to Hadoop How do they differ?

Hive LLAP allows customers to perform sub-second interactive queries without the need for additional SQL-based analytical tools. Impala offers fast, interactive SQL queries directly on our Apache Hadoop data stored in HDFS or HBase.

How do I optimize group by query in Hive?

Best Practices to Optimize Hive Query Performance

1. Use Column Names instead of * in SELECT Clause.
2. Use SORT BY instead of ORDER BY Clause.
3. Use Hive Cost Based Optimizer (CBO) and Update Stats.
4. Hive Command to Enable CBO.
5. Use WHERE instead of HAVING to Define Filters on non-aggregate Columns.