Apache Hive features

Major changes to Apache Hive 2.x improve Apache Hive 3.x transactions and security. Knowing the major differences between these versions is critical for SQL users, including those who use Apache Spark and Apache Impala.

Hive is a data warehouse system for summarizing, querying, and analyzing huge, disparate data sets.

ACID transaction processing

Hive 3 tables are ACID (Atomicity, Consistency, Isolation, and Durability)-compliant. Hive 3 write and read operations improve the performance of transactional tables. Atomic operations include simple writes and inserts, writes to multiple partitions, and multiple inserts in a single SELECT statement. A read operation is not affected by changes that occur during the operation. You can insert or delete data, and it remains consistent throughout software and hardware crashes. Creation and maintenance of Hive tables is simplified because there is no longer any need to bucket tables.

Materialized views

Because multiple queries frequently need the same intermediate roll up or joined table, you can avoid costly, repetitious query portion sharing, by precomputing and caching intermediate tables into views.

Query results cache

Hive filters and caches similar or identical queries. Hive does not recompute the data that has not changed. Caching repetitive queries can reduce the load substantially when hundreds or thousands of users of BI tools and web services query Hive.

Scheduled Queries

Using SQL statements, you can schedule Hive queries to run on a recurring basis, monitor query progress, temporarily ignore a query schedule, and limit the number running in parallel. You can use scheduled queries to start compaction and periodically rebuild materialized views, for example.

Spark integration with Hive in Data Hub

Spark and Hive tables interoperate using the Hive Warehouse Connector and Spark Direct Reader to access ACID managed tables. You can access external tables from Spark directly using SparkSQL.

You do not need HWC to read or write Hive external tables. Spark users just read from or write to Hive directly. You can read Hive external tables in ORC or Parquet formats. You can write Hive external tables in ORC format only.

Security improvements

Apache Ranger secures Hive data by default. To meet demands for concurrency improvements, ACID support, render security, and other features, Hive tightly controls the location of the warehouse on a file system, or object store, and memory resources.

With Apache Ranger and Apache Hive ACID support, your organization will be ready to support and implement GDPR (General Data Protection Regulation).

Workload management at the query level

You can configure who uses query resources, how much can be used, and how fast Hive responds to resource requests. Workload management can improve parallel query execution, cluster sharing for queries, and query performance. Although the names are similar, Hive workload management queries are unrelated to Cloudera Workload XM for reporting and viewing millions of queries and hundreds of databases.

Connection Pooling

Hive supports HakariCP JDBC connection pooling.

Unsupported features

CDP does not support the following features that were available in HDP and CDH platforms:

  • CREATE TABLE that specifies a managed table location

    Do not use the LOCATION clause to create a managed table. Hive assigns a default location in the warehouse to managed tables.


    Hive builds and stores indexes in ORC or Parquet within the main table, instead of a different table, automatically. Set hive.optimize.index.filter to enable use (not recommended--use materialized views instead). Existing indexes are preserved and migrated in Parquet or ORC to CDP during upgrade.