Overview

Apache Impala

Stacks145

Followers301

Votes18

GitHub Stars34

Forks33

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Apache Flink vs Impala: What are the differences?

# Introduction
Apache Flink and Impala are two popular data processing frameworks with distinct characteristics. Below are the key differences between Apache Flink and Impala.

1. **Processing Model**:
Apache Flink is a stream processing framework that supports both batch and real-time data processing, while Impala is primarily designed for ad-hoc SQL queries on Hadoop data. Flink processes data in a continuous and event-driven manner, whereas Impala is more suitable for interactive and fast SQL queries on structured data.

2. **Latency**:
Apache Flink is known for its low latency and high throughput processing capabilities, making it suitable for real-time applications with strict latency requirements. On the other hand, Impala may have higher latency due to its architecture optimized for ad-hoc queries, which can impact real-time processing performance.

3. **State Management**:
Apache Flink provides native support for state management, enabling complex event processing and fault tolerance mechanisms. In contrast, Impala does not have built-in state management capabilities, limiting its ability to handle complex stateful computations efficiently.

4. **Programming Language**:
Apache Flink supports multiple programming languages such as Java, Scala, and Python, offering flexibility to developers in choosing their preferred language for writing data processing applications. Impala, on the other hand, primarily uses SQL for querying data stored in Hadoop, which may limit the options for developers to use other languages for data processing.

5. **Optimization Techniques**:
Apache Flink employs various optimization techniques such as operator fusion, query optimization, and dynamic resource allocation to enhance performance and efficiency in processing large-scale data sets. Impala focuses more on query optimization and execution planning to speed up SQL queries but may lack the comprehensive optimization techniques offered by Flink.

6. **Compatibility**:
Apache Flink is compatible with a wide range of data sources and systems, including Hadoop, Kafka, and other streaming platforms, providing seamless integration with existing data infrastructure. In comparison, Impala is tightly integrated with Hadoop ecosystem components like HDFS and Hive, which may limit its interoperability with non-Hadoop data sources and systems.

In Summary, Apache Flink and Impala differ in their processing models, latency characteristics, state management capabilities, programming language support, optimization techniques, and compatibility with external systems and data sources.

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Advice on Apache Impala, Apache Flink

Krishna Chaitanya

Head of Technology at Adonmo

Jun 27, 2021

Review

For such a more realtime-focused, data-centered application like an exchange, it's not the frontend or backend that matter much. In fact for that, they can do away with any of the popular frameworks like React/Vue/Angular for the frontend and Go/Python for the backend. For example uniswap's frontend (although much simpler than binance) is built in React. The main interesting part here would be how they are able to handle updating data so quickly. In my opinion, they might be heavily reliant on realtime processing systems like Kafka+Kafka Streams, Apache Flink or Apache Spark Stream or similar. For more processing heavy but not so real-time processing, they might be relying on OLAP and/or warehousing tools like Cassandra/Redshift. They could have also optimized few high frequent queries using NoSQL stores like mongodb (for persistance) and in-memory cache like Redis (for further perfomance boost to get millisecond latencies).

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Comments

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Comments

Detailed Comparison

Apache Impala	Apache Flink
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.	Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
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	Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance
Statistics
GitHub Stars 34	GitHub Stars 25.4K
GitHub Forks 33	GitHub Forks 13.7K
Stacks 145	Stacks 534
Followers 301	Followers 879
Votes 18	Votes 38
Pros & Cons
Pros 11 Super fast 1 Load Balancing 1 High Performance 1 Massively Parallel Processing 1 Open Sourse	Pros 16 Unified batch and stream processing 8 Easy to use streaming apis 8 Out-of-the box connector to kinesis,s3,hdfs 4 Open Source 2 Low latency
Integrations
Hadoop Mode Redash Apache Kudu	YARN Hadoop Hadoop HBase Kafka

What are some alternatives to Apache Impala, Apache Flink?

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.