Hive is a data warehousing tool that provides a SQL-like interface for querying large datasets stored in Hadoop Distributed File System (HDFS). As with any SQL-based tool, Hive relies on query optimization to improve query performance and reduce query execution time. Hive provides several optimization techniques to achieve this goal. In this article, we’ll explore the different types of Hive optimizers and how they work.
The first step in query optimization is parsing the SQL query to check its syntactical and semantic correctness. Hive’s query parser checks the query for any syntax errors and validates the table and column names used in the query against the metastore. If the query passes the parser, it proceeds to the next stage of optimization.
Hive’s semantic analyzer checks the query for semantic errors and resolves the table and column names used in the query. It also checks if the query conforms to the database schema and verifies the data types of the columns used in the query. If the query passes the semantic analyzer, it proceeds to the next stage of optimization.
Hive’s logical optimizer optimizes the query plan at the logical level by transforming the query into a more efficient form without changing its semantics. The logical optimizer applies a set of optimization rules to the query plan, such as predicate pushdown, column pruning, and join reordering. Predicate pushdown pushes down filters to the storage layer, which reduces the amount of data that needs to be read from disk. Column pruning removes unused columns from the query, which reduces the amount of data that needs to be processed. Join reordering changes the order of joins to reduce the amount of data that needs to be shuffled.
Hive’s physical optimizer optimizes the query plan at the physical level by choosing the best execution strategy for the query. The physical optimizer determines the best execution strategy based on the available hardware resources, data distribution, and query complexity. Hive provides several execution engines, such as MapReduce, Tez, and Spark, that can be used to execute the query plan. The physical optimizer selects the best execution engine based on the query characteristics and available resources.
Hive’s cost-based optimizer (CBO) is an advanced optimization technique that estimates the cost of executing a query plan and selects the most efficient execution plan based on the cost estimates. The cost-based optimizer uses statistics collected from the data to estimate the cost of executing the query plan. The cost estimates are used to choose the best execution plan from a set of candidate plans. The cost-based optimizer can significantly improve query performance for complex queries by choosing the most efficient execution plan.
Hive’s indexing feature allows users to create indexes on columns to improve query performance. Hive supports two types of indexes: bitmap indexes and compact indexes. Bitmap indexes are used for low cardinality columns, while compact indexes are used for high cardinality columns. Indexing can significantly improve query performance for queries that involve filtering on indexed columns.
Hive’s vectorization feature is an optimization technique that allows processing of multiple rows at once, rather than processing one row at a time. Vectorization improves query performance by reducing the overhead of processing each row separately. Vectorization is enabled by default in Hive 2.0 and later versions.
Hive provides several optimization techniques to improve query performance and reduce query execution time. These techniques include query parsing, semantic analysis, logical optimization, physical optimization, cost-based optimization, indexing, and vectorization. By using these optimization techniques, Hive can efficiently process large datasets stored in Hadoop Distributed File System.
Hive important pages to refer