Hive : Hive optimizer – Detailed walk through

Hive is a popular open-source data warehouse system that allows users to store, manage, and analyze large datasets using SQL-like queries. One of the key components of Hive is the optimizer, which plays a crucial role in optimizing queries for performance. In this article, we will discuss the Hive optimizer, its functions, and the techniques it uses to optimize queries.

Hive optimizer

The Hive optimizer is a module in the Hive query processing engine that aims to optimize queries for faster execution. The optimizer works by analyzing the query plan and making changes to it to reduce the time and resources required to execute the query. The main objective of the optimizer is to reduce the number of MapReduce jobs that are required to execute a query.

Functions of the Hive Optimizer

The Hive optimizer performs several functions to optimize queries, including the following:

1. Query Parsing and Validation: The optimizer starts by parsing and validating the query to ensure that it is syntactically correct and that all the required tables and columns exist.
2. Query Rewrite: The optimizer can rewrite the query to make it more efficient. For example, it can push down filters to reduce the amount of data that needs to be read from the disk.
3. Cost-Based Optimization: The optimizer uses a cost-based approach to determine the best query execution plan. It considers the estimated cost of different execution plans and selects the one with the lowest cost.
4. Join Optimization: The optimizer can rearrange the order in which joins are executed to minimize the amount of data that needs to be shuffled across the network.
5. Partition Pruning: The optimizer can eliminate partitions that are not required for the query, based on the query conditions, to reduce the amount of data that needs to be scanned.
6. Predicate Pushdown: The optimizer can push filters down to the storage layer, such as HDFS or HBase, to reduce the amount of data that needs to be read.
7. Vectorization: The optimizer can use vectorized processing to process large amounts of data in a single batch, which can significantly improve query performance.

Techniques used by the Hive Optimizer.

The Hive optimizer uses several techniques to optimize queries, including the following:

1. Join Reordering: The optimizer can reorder the joins to reduce the amount of data that needs to be shuffled across the network. It does this by selecting the join with the smallest input first, and then recursively applying this process to the remaining joins.
2. Predicate Pushdown: The optimizer can push filters down to the storage layer to reduce the amount of data that needs to be read. It does this by analyzing the query conditions and selecting the predicates that can be pushed down.
3. Partition Pruning: The optimizer can eliminate partitions that are not required for the query, based on the query conditions. It does this by analyzing the partitioning scheme of the table and selecting the partitions that can be eliminated.
4. Cost-Based Optimization: The optimizer uses a cost-based approach to determine the best query execution plan. It considers the estimated cost of different execution plans and selects the one with the lowest cost.
5. Vectorization: The optimizer can use vectorized processing to process large amounts of data in a single batch. It does this by converting the query plan into a vectorized form and using vectorized operators to process the data.

Differences between Tez and Spark Execution Engines

While both Tez and Spark are popular execution engines used with Hive, there are some differences between them. The Tez engine is designed to work with Hadoop and uses the YARN resource manager to manage resources. It uses a DAG-based execution model and provides support for dynamic partitioning and vectorized processing. The Spark engine, on the other hand, is a standalone engine that does not require Hadoop. It uses a different execution.