Apache Hive and Apache Impala enable powerful SQL-based data analysis on Hadoop and cloud object stores. Both tools allow you to query, analyze, and process massive datasets using familiar SQL syntax.
However, Hive and Impala have key technical differences that make each better suited for certain big data analytics use cases. So when building a production-grade big data pipeline, should you use Hive or Impala? Or both?
This comprehensive technical guide explains everything an IT leader needs to know about Hive and Impala to decide which solution fits their architectural needs and performance requirements. You’ll learn:
- Detailed overviews of Hive and Impala architectures
- How to benchmark Hive vs. Impala performance for your workload
- Optimization, storage, data format, and migration considerations
- Strategies for unifying Hive and Impala in a robust analytics architecture
- Real-world use cases and decision criteria for choosing Hive vs. Impala
Let’s start by looking at what Hive and Impala are at their core and how they work under the hood…
Apache Hive Architecture Overview
Apache Hive is an open source SQL-based data warehousing framework built on top of Hadoop and Spark. It allows you to query and analyze petabyte-scale data in distributed storage like HDFS or cloud object stores such as Amazon S3…
Key Components of Apache Hive Architecture
Behind the scenes, several components power the Hive architecture:
The Driver
The Driver coordinates the lifecycle of a Hive query including compilation, optimization, and orchestrating distributed execution…
The Compiler & Optimizer
Hive‘s compiler translates SQL-like HiveQL statements into a directed acyclic graph (DAG) of MapReduce or Spark jobs. The optimizer then applies techniques such as partition pruning to make the execution plan more efficient…
Data Processing Engines
For distributed execution, Hive leverages the parallelism of Hadoop MapReduce or the in-memory speed of Apache Spark…
Metastore
The metastore is Hive‘s centralized repository containing metadata about databases, tables, partitions, columns and their data types. By default Hive uses an embedded Derby database or a MySQL database backend for the metastore…
Security Features
Hive integrates with Hadoop security including authentication via Kerberos and authorization using Apache Ranger…
Key Features of Hive
Benefits of Using Hive
Apache Impala Architecture
Apache Impala is an open source MPP SQL query engine designed from the ground up to enable low latency queries on data stored in Hadoop. It utilizes a specialized distributed architecture that avoids MapReduce to achieve real-time analytic performance…
Impala Architecture Components
The key components that power the Impala architecture include:
Impala Daemons
Impalad processes run distributed queries on each data node in the cluster. They stream and partition query inputs in parallel…
Statestore
Statestore monitors the availability of Impala daemons across the cluster for load balancing and failure detection…
Catalog
Catalog broadcasts metadata changes to Impala daemons when tables and partitions are updated…
Key Features of Impala
Benefits of Using Impala
Hive vs. Impala: Commonalities and Differences
Despite very different architectures, Hive and Impala have some key similarities. But underneath, there are significant technical differences between these two SQL-on-Hadoop engines…
Key Similarities Between Hive and Impala
Common interfaces:
- SQL query language (HiveQL)
- JDBC/ODBC connectivity from BI tools
- Metadata integration via Hive Metastore
10 Key Technical Differences
| Apache Hive | Apache Impala | |
|---|---|---|
| Language | Java | C++ / Java UDFs |
Benchmarking Performance: Hive vs. Impala
Published benchmarks reveal significant performance differences between Impala and Hive, especially for certain types of query workloads. Let‘s look at some real-world benchmark results…
Hive vs. Impala Query Benchmark at Fortune 500 Company
One detailed benchmark test conducted at a financial services firm compared Hive 11.0 and Impala 2.11 running analytics queries against a 12 node Hadoop cluster with 24 TB of structured data…
Technology Company Behavioral Benchmark
A technology company also shared key metrics on behavioral differences observed between Impala and Hive when running production workloads with thousands of users and queries per day…
Unifying Impala and Hive: Best Practices
Given their strengths and weaknesses, the best practice at many organizations is to deploy Impala and Hive in a unified SQL analytics architecture…
Strategies for Transitioning from Hive to Impala
Integrating Impala into Existing Hive Pipelines
Conclusion
By understanding key technical differentiators like query performance, flexibility, and scalability in Hive vs. Impala, architects can choose the right SQL engine for their analytics use cases…
