Apache Impala — Technology Profile & Architecture

Core Information

#core_information

Name, description, vendor website, logo, technology type and tags.

Overview & Role

Apache Impala is an open-source, massively parallel processing (MPP) SQL query engine for data stored in Apache Hadoop. It provides high-performance, low-latency SQL queries on large datasets, making it suitable for interactive analytics and business intelligence applications directly on HDFS, Apache Kudu, and Apache HBase.

Domain Classification & Tags

Analytics Relational Multi Model

Performance Profile

#performance_profile

Typical read and write throughput, payload size and processing latency.

Read Performance Profile

Throughput: Hundreds of thousands to millions of rows/sec, or GBs/sec depending on query complexity and data size

Payload Size: Varies widely, from single rows (tens of bytes) to large analytical result sets (MBs to GBs)

Processing Latency: Sub-second for simple queries on warm data, seconds to tens of seconds for complex analytical queries on large datasets

Write Performance Profile

Throughput: Impala is primarily a read-optimized query engine. Writes typically go through other systems like Hive/Spark and then Impala queries the data. Direct writes are not its primary use case, but can involve INSERT OVERWRITE or INSERT INTO, which can range from thousands to tens of thousands of rows/sec depending on data format and cluster size.

Payload Size: Varies, from single rows to large batches (MBs to GBs) for INSERT operations

Processing Latency: Seconds to minutes for INSERT operations, depending on data volume and complexity of the query generating the data

Architecture Diagram

#architecture_diagram

Reference diagram of the technology's internal architecture.

Component Architecture & Topology

Features

#features

Catalogued product capabilities and what each one does.

Empirical data for features is currently being compiled in the global catalog.

Typical Use Cases

#typical_use_cases

Scenarios the technology is commonly chosen for.

Empirical data for typical use cases is currently being compiled in the global catalog.

Known Customers

#known_customers

Publicly referenced organisations using the technology.

Empirical data for known customers is currently being compiled in the global catalog.

Known Integrations

#known_integrations

Other products and services it is documented to work with.

Empirical data for known integrations is currently being compiled in the global catalog.

Connectors

#connectors

Directional data connections to other technologies, with direction and maturity.

Empirical data for connectors is currently being compiled in the global catalog.

Reference Architectures

#reference_architectures

Published architectures where the technology is used or mentioned.

No published reference architecture blueprints currently link to Apache Impala.

Security Features

#security_features

Built-in security and access-control capabilities.

Empirical data for security features is currently being compiled in the global catalog.

Known Issues

#known_issues

Documented limitations, defects and operational pitfalls.

Empirical data for known issues is currently being compiled in the global catalog.

Guidelines

#guidelines

Recommended practices for adopting and operating the technology.

Empirical data for guidelines is currently being compiled in the global catalog.

Standards & Compliance

#standards_and_compliance

Standards, certifications and control requirements it maps to.

Empirical data for standards & compliance is currently being compiled in the global catalog.

Sources

#sources

Documentation and research references behind the recorded information.

Empirical data for sources is currently being compiled in the global catalog.

Expert Validation

#expert_validation

Whether domain experts reviewed and confirmed the content.

Empirical data for expert validation is currently being compiled in the global catalog.