Apache Impala vs PostgreSQL

Overview

Apache Impala

Stacks145

Followers301

Votes18

GitHub Stars34

Forks33

PostgreSQL

Stacks103.0K

Followers83.9K

Votes3.6K

GitHub Stars19.0K

Forks5.2K

Apache Impala vs PostgreSQL: What are the differences?

Introduction: Apache Impala and PostgreSQL are two popular database management systems that have key differences in terms of their architecture, query processing, and performance.

Architecture: Apache Impala follows a distributed architecture, allowing it to enable real-time interactive query processing on large volumes of data. It utilizes a shared-nothing architecture with a distributed computing framework, which allows it to process and analyze data in parallel across multiple nodes. On the other hand, PostgreSQL follows a traditional client-server architecture, where a central server manages all the data and processes queries.
Query Processing: Apache Impala is designed specifically for fast and efficient processing of analytical queries. It uses a massively parallel processing (MPP) engine to parallelize query execution across multiple nodes, resulting in high-speed query performance. PostgreSQL, on the other hand, is more suitable for transactional workloads and supports a wider range of SQL features for complex queries.
Performance: Due to its distributed architecture and MPP engine, Apache Impala is known for its high-performance query processing. It is optimized for large-scale data analysis and can handle complex analytical queries efficiently. PostgreSQL, while also capable of handling analytical queries, may not perform as well as Impala when dealing with large volumes of data and complex analytical workloads.
Data Types: Impala and PostgreSQL have different sets of supported data types. Impala supports a wide range of data types including integers, floats, strings, dates, and timestamps, as well as more specialized types for geospatial data. PostgreSQL offers a broader range of data types, including array types, binary types, network address types, and JSON data types.
Concurrency Control: Concurrency control mechanisms differ between Impala and PostgreSQL. Impala does not support built-in row-level locking, and instead relies on the optimistic concurrency control strategy, which can lead to better performance in certain scenarios. PostgreSQL, being a full-featured relational database, offers more advanced locking mechanisms, such as row-level locking and multi-version concurrency control (MVCC).
Data Manipulation Language: PostgreSQL offers a comprehensive set of data manipulation language (DML) features, including advanced query capabilities, support for procedural languages like PL/pgSQL, and complex data manipulation operations like window functions and common table expressions. Impala, being optimized for analytical workloads, has limited support for DML operations and focuses primarily on fast query processing.

In summary, Apache Impala and PostgreSQL differ in terms of their architecture, query processing capabilities, performance, supported data types, concurrency control mechanisms, and data manipulation language features.

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Detailed Comparison

Apache Impala	PostgreSQL
Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.	PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.
Do BI-style Queries on Hadoop;Unify Your Infrastructure;Implement Quickly;Count on Enterprise-class Security;Retain Freedom from Lock-in;Expand the Hadoop User-verse	-
Statistics
GitHub Stars 34	GitHub Stars 19.0K
GitHub Forks 33	GitHub Forks 5.2K
Stacks 145	Stacks 103.0K
Followers 301	Followers 83.9K
Votes 18	Votes 3.6K
Pros & Cons
Pros 11 Super fast 1 Distributed 1 Scalability 1 Load Balancing 1 Replication	Pros 765 Relational database 511 High availability 439 Enterprise class database 383 Sql 304 Sql + nosql Cons 10 Table/index bloatings
Integrations
Hadoop Mode Redash Apache Kudu	No integrations available

What are some alternatives to Apache Impala, PostgreSQL?

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Related Comparisons

Apache Impala vs PostgreSQL: What are the differences?

Introduction: Apache Impala and PostgreSQL are two popular database management systems that have key differences in terms of their architecture, query processing, and performance.

Architecture: Apache Impala follows a distributed architecture, allowing it to enable real-time interactive query processing on large volumes of data. It utilizes a shared-nothing architecture with a distributed computing framework, which allows it to process and analyze data in parallel across multiple nodes. On the other hand, PostgreSQL follows a traditional client-server architecture, where a central server manages all the data and processes queries.
Query Processing: Apache Impala is designed specifically for fast and efficient processing of analytical queries. It uses a massively parallel processing (MPP) engine to parallelize query execution across multiple nodes, resulting in high-speed query performance. PostgreSQL, on the other hand, is more suitable for transactional workloads and supports a wider range of SQL features for complex queries.
Performance: Due to its distributed architecture and MPP engine, Apache Impala is known for its high-performance query processing. It is optimized for large-scale data analysis and can handle complex analytical queries efficiently. PostgreSQL, while also capable of handling analytical queries, may not perform as well as Impala when dealing with large volumes of data and complex analytical workloads.
Data Types: Impala and PostgreSQL have different sets of supported data types. Impala supports a wide range of data types including integers, floats, strings, dates, and timestamps, as well as more specialized types for geospatial data. PostgreSQL offers a broader range of data types, including array types, binary types, network address types, and JSON data types.
Concurrency Control: Concurrency control mechanisms differ between Impala and PostgreSQL. Impala does not support built-in row-level locking, and instead relies on the optimistic concurrency control strategy, which can lead to better performance in certain scenarios. PostgreSQL, being a full-featured relational database, offers more advanced locking mechanisms, such as row-level locking and multi-version concurrency control (MVCC).
Data Manipulation Language: PostgreSQL offers a comprehensive set of data manipulation language (DML) features, including advanced query capabilities, support for procedural languages like PL/pgSQL, and complex data manipulation operations like window functions and common table expressions. Impala, being optimized for analytical workloads, has limited support for DML operations and focuses primarily on fast query processing.

Apache Impala vs PostgreSQL

Overview