Apache Parquet vs Apache Spark

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Apache Parquet

Stacks97

Followers190

Votes0

Apache Parquet vs Apache Spark: What are the differences?

Introduction

Apache Parquet and Apache Spark are both widely used technologies in the big data space. While Apache Parquet is a columnar storage file format, Apache Spark is a fast and general-purpose cluster computing system. In this comparison, we will highlight the key differences between the two.

Data Storage Mechanism: Parquet is a columnar storage format that stores data in columns, making it efficient for analytical workloads. On the other hand, Spark is a distributed computing framework that allows for processing large datasets across a cluster of machines. While Parquet focuses on efficient storage, Spark provides a powerful framework for distributed data processing.
File Format vs. Computing System: Parquet primarily focuses on how data is stored on disk, providing efficient compression and encoding techniques for columnar data. In contrast, Spark is a computing system that provides APIs for processing structured, semi-structured, and unstructured data. Spark can work with different file formats, including Parquet, among others.
Optimization Techniques: Parquet employs various optimization techniques such as predicate pushdown, column pruning, and dictionary encoding to achieve better query performance. It leverages the metadata stored within each file to skip unnecessary data while reading. Spark, on the other hand, offers a range of optimization techniques such as query optimization, data partitioning, and caching to optimize data processing and improve performance.
Use Cases: Parquet is commonly used in scenarios where efficient columnar storage and analytical query processing is required. It is widely used in big data analytics platforms and data warehousing systems. On the other hand, Spark is suitable for large-scale data processing tasks, including data ingestion, ETL (Extract, Transform, Load), machine learning, and real-time streaming analytics.
Language Support: Parquet provides support for multiple programming languages like Java, C++, Python, and R, enabling developers to work with Parquet files in their preferred programming language. Spark, being a distributed computing framework, offers APIs in various languages, including Scala, Java, Python, and R. It provides a unified interface for data processing across different programming languages.
Integration with Ecosystem: Parquet is designed to work well with a variety of big data processing frameworks such as Apache Hadoop, Apache Hive, Apache Pig, and Apache Impala. It seamlessly integrates with these technologies to provide efficient data storage and processing capabilities. On the other hand, Spark integrates with a wide range of big data ecosystem tools and libraries, making it highly versatile and suitable for complex data workflows.

In summary, Apache Parquet is a columnar storage file format focused on efficient data storage and retrieval, primarily used in analytics and data warehousing scenarios. Apache Spark, on the other hand, is a flexible and powerful distributed computing framework that can process large datasets across a cluster of machines, supporting a wide range of use cases from ETL to machine learning.

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 Spark, Apache Parquet

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 Spark	Apache Parquet
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.	It is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.
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	Columnar storage format;Type-specific encoding; Pig integration; Cascading integration; Crunch integration; Apache Arrow integration; Apache Scrooge integration;Adaptive dictionary encoding; Predicate pushdown; Column stats
Statistics
GitHub Stars 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 97
Followers 3.5K	Followers 190
Votes 140	Votes 0
Pros & Cons
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	No community feedback yet
Integrations
No integrations available	Hadoop Java Apache Impala Apache Thrift Apache Hive Pig

What are some alternatives to Apache Spark, Apache Parquet?

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.

PostgreSQL

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.

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.

Related Comparisons

Apache Parquet vs Apache Spark: What are the differences?

Introduction

Data Storage Mechanism: Parquet is a columnar storage format that stores data in columns, making it efficient for analytical workloads. On the other hand, Spark is a distributed computing framework that allows for processing large datasets across a cluster of machines. While Parquet focuses on efficient storage, Spark provides a powerful framework for distributed data processing.
File Format vs. Computing System: Parquet primarily focuses on how data is stored on disk, providing efficient compression and encoding techniques for columnar data. In contrast, Spark is a computing system that provides APIs for processing structured, semi-structured, and unstructured data. Spark can work with different file formats, including Parquet, among others.
Optimization Techniques: Parquet employs various optimization techniques such as predicate pushdown, column pruning, and dictionary encoding to achieve better query performance. It leverages the metadata stored within each file to skip unnecessary data while reading. Spark, on the other hand, offers a range of optimization techniques such as query optimization, data partitioning, and caching to optimize data processing and improve performance.
Use Cases: Parquet is commonly used in scenarios where efficient columnar storage and analytical query processing is required. It is widely used in big data analytics platforms and data warehousing systems. On the other hand, Spark is suitable for large-scale data processing tasks, including data ingestion, ETL (Extract, Transform, Load), machine learning, and real-time streaming analytics.
Language Support: Parquet provides support for multiple programming languages like Java, C++, Python, and R, enabling developers to work with Parquet files in their preferred programming language. Spark, being a distributed computing framework, offers APIs in various languages, including Scala, Java, Python, and R. It provides a unified interface for data processing across different programming languages.
Integration with Ecosystem: Parquet is designed to work well with a variety of big data processing frameworks such as Apache Hadoop, Apache Hive, Apache Pig, and Apache Impala. It seamlessly integrates with these technologies to provide efficient data storage and processing capabilities. On the other hand, Spark integrates with a wide range of big data ecosystem tools and libraries, making it highly versatile and suitable for complex data workflows.

Apache Parquet vs Apache Spark

Overview

Apache Parquet vs Apache Spark: What are the differences?