Stinger : Interactive Query for Hive
Speed, Scale and SQL Compatibility with Apache Hive
Apache Hive is the de facto standard for SQL-in-Hadoop with more enterprises relying on this open source project than any alternative. The Stinger Initiative is a broad, community-based effort to drive the future of Apache Hive, delivering 100x performance improvements at petabyte scale with familiar SQL semantics.
Deliver interactive query through 100x performance increases as compared to Hive 10.
The only SQL interface to Hadoop designed for queries that scale from Terabytes to Petabytes.
Support the broadest array of SQL semantics for analytic applications running in Hadoop.
The Stinger initiative outlined three phases, and the Apache community delivered each on schedule. During Stinger’s thirteen months of continuous community collaboration 145 developers from 44 companies closed 1,672 Jira and added 392,000 lines of Java code. In just over one year, Stinger delivered speed, scale and SQL semantics.
In the first phase of delivery, with HDP 1.3, we saw:
- Performance improvements of 35x-45x for common analytical queries and
- Introduction of SQL windowing functions such as Rank, Lead, Lag, etc.
- Introduction of the ORCFile format
The release of HDP 2.0 marked the second major milestone of Stinger based improvements for Hive, introducing:
- A preview of the vectorized query engine, jointly developed with Microsoft and other community contributors, that speeds all types of queries, adding another 5x-10x improvement.
- Simplified SQL interoperability through the new VARCHAR and DATE datatypes and
- A new query optimizer that speeds complex queries by several factors.
Phase 3, delivered with HDP 2.1, will complete the Stinger Initiative on schedule with:
- Apache Hive on Apache Tez, with pre-launched hot containers
- Vectorized query execution
- ACID framework for managing dimension tables and other master data
- Support for additional SQL functions and operators
- Support for data access: HTTP, SSL, Kerberos authentication
The improvements to Apache Hive—through the Stinger Initiative—delivered order-of-magnitude improvements in query latency and pushed several types of queries past 100x faster than in Hive 0.10.
HDP 2.0 introduced several major new performance features that benefit both small reporting queries and deep analytical queries. Some of which are described in this table:
We looked at TPC-DS Query 27, a fairly simple reporting query, back in February 2013 and showed that some improvements to the Hive query planner led to massive performance benefits. HDP 2.0 brings incremental progress by introducing vectorized query, which makes the map stages far more efficient.
HDP 2.1 delivers Apache Hive 0.13 on Apache Tez. With Hive on Tez, users have the option of executing queries on Tez. Tez’s dataflow model on a DAG of nodes facilitates simpler, more efficient query plans, which translates to significant performance improvements.
Hive 0.13 also delivers vectorized query execution mode that performs CPU computations 5-10x faster, translating to a 2-3x improvement in query performance.
ORCFile was introduced in Hive 0.11 and offered excellent compression, delivered through a number of techniques including run-length encoding, dictionary encoding for strings and bitmap encoding. These improvements meant:
- Sustained Query Times. Apache Hive 0.12 provides sustained acceptable query times even at petabyte scale.
- Smaller Footprint. Better encoding with ORCFile in Apache Hive 12 reduces resource requirements for a cluster.
This picture shows the sizes of the TPC-DS dataset at Scale 500 in various encodings. This dataset contains randomly generated data including strings, floating point and integer data.
We’ve already seen customers whose clusters were maxed out for storage move to ORCFile as a way to free up space while preserving complete compatibility with existing jobs.
Data stored in ORCFile can be read or written through HCatalog, so any Pig or Map/Reduce process works seamlessly. Hive 0.12 built on Hive 0.11’s impressive compression ratios and delivered deep integration at the Hive and execution layers, which further accelerated queries even over larger datasets.
Our goal with SQL support is simple: Make Apache Hive a comprehensive and compliant SQL engine that meets enterprise needs. Hive 0.13 introduces the DECIMAL and CHAR datatypes. With the SQL standard-based authorization feature in Hive 0.13, users can now define their authorization policies in an SQL-compliant fashion. The Apache Hive community extended SQL language to support grant and revoke on entities. Hive also now supports show roles, user privileges, and active privileges.