Apache Impala vs Apache Spark

Overview

Apache Impala

Stacks145

Followers301

Votes18

GitHub Stars34

Forks33

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Impala vs Apache Spark: What are the differences?

Introduction: In this article, we will explore the key differences between Apache Impala and Apache Spark, two popular open-source big data processing frameworks.

Data Processing Model: One major difference between Impala and Spark is their data processing model. Impala is designed for interactive SQL queries and provides real-time, low-latency querying capabilities on large datasets stored in Hadoop Distributed File System (HDFS). On the other hand, Spark offers a more flexible and general-purpose data processing model, supporting various data manipulation tasks like batch processing, streaming, machine learning, and graph processing.
Data Storage: Another significant difference lies in their data storage options. Impala relies on HDFS for storing data, while Spark can work with various data sources, including HDFS, Apache Parquet, Apache Avro, Apache Cassandra, and more. This versatility allows Spark to integrate with a wide range of data storage technologies, making it suitable for diverse use cases.
Query Optimization: Impala and Spark employ different query optimization techniques. Impala uses a cost-based optimizer that evaluates various query execution plans and selects the most efficient one based on statistics and heuristics. Spark, on the other hand, utilizes a rule-based optimizer that applies a set of predefined rules to optimize query plans. While both approaches have their strengths, Impala's cost-based optimization can often lead to faster query execution.
Concurrency and Scalability: When it comes to handling concurrent queries and scaling to larger datasets, the two frameworks differ in their approach. Impala is built to support high concurrency, allowing multiple users to execute queries simultaneously. It achieves this through an MPP (Massively Parallel Processing) architecture that leverages distributed computing resources. Meanwhile, Spark uses a shared-nothing architecture that partitions the data across a cluster and processes it in parallel. This design enables Spark to scale horizontally by adding more nodes to the cluster.
Language Support: Impala mainly supports SQL for querying and processing data, making it suitable for users familiar with SQL-based analytics. On the other hand, Spark provides support for multiple programming languages, such as Scala, Java, Python, and R. This broader language support makes Spark more flexible, allowing developers to choose the language they are most comfortable with for data processing tasks.
Familiarity with Apache Hadoop: Another key difference is the level of familiarity required with Apache Hadoop. Impala is tightly integrated with the Hadoop ecosystem and relies on Hadoop services like HDFS and Hive for metadata management. Therefore, users familiar with Hadoop can leverage their existing knowledge to work with Impala. In contrast, while Spark can also integrate with Hadoop, it can be used as a standalone framework as well, reducing the dependency on Hadoop-specific components.

In Summary, Apache Impala is optimized for interactive SQL querying with a focus on low-latency, real-time performance and tight integration with the Hadoop ecosystem. In contrast, Apache Spark offers a more versatile and general-purpose data processing framework, supporting various data manipulation tasks, multiple data sources, and programming languages, with a focus on scalability and flexibility in distributed computing.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Apache Impala, Apache Spark

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.

576k views576k

Comments

Detailed Comparison

Apache Impala	Apache Spark
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.	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.
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	Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
Statistics
GitHub Stars 34	GitHub Stars 42.2K
GitHub Forks 33	GitHub Forks 28.9K
Stacks 145	Stacks 3.1K
Followers 301	Followers 3.5K
Votes 18	Votes 140
Pros & Cons
Pros 11 Super fast 1 Scalability 1 Distributed 1 Replication 1 Load Balancing	Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed
Integrations
Hadoop Mode Redash Apache Kudu	No integrations available

What are some alternatives to Apache Impala, Apache Spark?

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.

Apache Flink

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.

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.

Apache Kudu

A new addition to the open source Apache Hadoop ecosystem, Kudu completes Hadoop's storage layer to enable fast analytics on fast data.

Related Comparisons

Apache Impala vs Apache Spark: What are the differences?

Introduction: In this article, we will explore the key differences between Apache Impala and Apache Spark, two popular open-source big data processing frameworks.

Data Processing Model: One major difference between Impala and Spark is their data processing model. Impala is designed for interactive SQL queries and provides real-time, low-latency querying capabilities on large datasets stored in Hadoop Distributed File System (HDFS). On the other hand, Spark offers a more flexible and general-purpose data processing model, supporting various data manipulation tasks like batch processing, streaming, machine learning, and graph processing.
Data Storage: Another significant difference lies in their data storage options. Impala relies on HDFS for storing data, while Spark can work with various data sources, including HDFS, Apache Parquet, Apache Avro, Apache Cassandra, and more. This versatility allows Spark to integrate with a wide range of data storage technologies, making it suitable for diverse use cases.
Query Optimization: Impala and Spark employ different query optimization techniques. Impala uses a cost-based optimizer that evaluates various query execution plans and selects the most efficient one based on statistics and heuristics. Spark, on the other hand, utilizes a rule-based optimizer that applies a set of predefined rules to optimize query plans. While both approaches have their strengths, Impala's cost-based optimization can often lead to faster query execution.
Concurrency and Scalability: When it comes to handling concurrent queries and scaling to larger datasets, the two frameworks differ in their approach. Impala is built to support high concurrency, allowing multiple users to execute queries simultaneously. It achieves this through an MPP (Massively Parallel Processing) architecture that leverages distributed computing resources. Meanwhile, Spark uses a shared-nothing architecture that partitions the data across a cluster and processes it in parallel. This design enables Spark to scale horizontally by adding more nodes to the cluster.
Language Support: Impala mainly supports SQL for querying and processing data, making it suitable for users familiar with SQL-based analytics. On the other hand, Spark provides support for multiple programming languages, such as Scala, Java, Python, and R. This broader language support makes Spark more flexible, allowing developers to choose the language they are most comfortable with for data processing tasks.
Familiarity with Apache Hadoop: Another key difference is the level of familiarity required with Apache Hadoop. Impala is tightly integrated with the Hadoop ecosystem and relies on Hadoop services like HDFS and Hive for metadata management. Therefore, users familiar with Hadoop can leverage their existing knowledge to work with Impala. In contrast, while Spark can also integrate with Hadoop, it can be used as a standalone framework as well, reducing the dependency on Hadoop-specific components.

Apache Impala vs Apache Spark

Overview