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  5. Apache Parquet vs MariaDB

Apache Parquet vs MariaDB

OverviewDecisionsComparisonAlternatives

Overview

MariaDB
MariaDB
Stacks16.5K
Followers12.8K
Votes468
GitHub Stars6.6K
Forks1.9K
Apache Parquet
Apache Parquet
Stacks97
Followers190
Votes0

Apache Parquet vs MariaDB: What are the differences?

Introduction

Apache Parquet and MariaDB are two different technologies used to store and process data. They have several key differences that make them suitable for different use cases.

  1. Storage format:

    • Apache Parquet is a columnar storage format designed for efficient read and write operations on large datasets. It organizes data by column, allowing for better compression and faster querying on specific columns.
    • MariaDB, on the other hand, is a relational database management system that stores data in rows. It provides support for SQL queries and transactions, making it suitable for traditional relational data models.
  2. Data organization:

    • Apache Parquet stores data in a nested schema format, where columns can have multiple levels of nesting. This allows for storing complex data types like arrays and structs efficiently.
    • MariaDB organizes data in tables with predefined columns and data types. It does not support nested data structures directly, although some workarounds can be implemented.
  3. Data compression:

    • Apache Parquet uses advanced compression techniques like dictionary encoding, run-length encoding, and bit packing to efficiently compress data. This results in reduced storage space and improved query performance.
    • MariaDB also offers compression options, but they are limited compared to Parquet. It supports compression at the table level using different algorithms like zlib and LZ4.
  4. Query performance:

    • Apache Parquet provides excellent query performance, especially when dealing with analytical workloads and aggregations on specific columns. The columnar storage format allows for skipping irrelevant data during query execution, resulting in faster response times.
    • MariaDB, being a traditional relational database system, provides good performance for general SQL queries. However, it may not be as efficient as Parquet for analytical workloads involving large datasets.
  5. Data durability and availability:

    • MariaDB offers features like replication, backup, and recovery mechanisms to ensure data durability and availability. It provides options for creating high-availability clusters and failover configurations.
    • Apache Parquet, being a file format, does not inherently provide data durability and availability features. It relies on external storage systems or distributed file systems for data replication and fault tolerance.
  6. Scalability:

    • MariaDB can scale horizontally by adding more servers to a cluster or vertically by upgrading hardware. It provides features like sharding and partitioning to distribute data across multiple servers.
    • Apache Parquet can also scale horizontally by storing data in distributed file systems like Hadoop Distributed File System (HDFS). As a standalone file format, Parquet can be easily integrated with Big Data processing frameworks like Apache Spark for distributed data processing.

In summary, Apache Parquet is a columnar storage format suitable for analytical workloads, providing excellent compression and query performance. MariaDB is a relational database management system that offers good general SQL query performance and features for data durability and availability.

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Advice on MariaDB, Apache Parquet

Omran
Omran

CTO & Co-founder at Bonton Connect

Jun 19, 2020

Needs advice

We actually use both Mongo and SQL databases in production. Mongo excels in both speed and developer friendliness when it comes to geospatial data and queries on the geospatial data, but we also like ACID compliance hence most of our other data (except on-site logs) are stored in a SQL Database (MariaDB for now)

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Comments

Detailed Comparison

MariaDB
MariaDB
Apache Parquet
Apache Parquet

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.

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.

Replication;Insert Delayed;Events;Dynamic;Columns;Full-text;Search;GIS;Locale;Settings;subqueries;Timezones;Triggers;XML;Functions;Views;SSL;Show Profile
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
6.6K
GitHub Stars
-
GitHub Forks
1.9K
GitHub Forks
-
Stacks
16.5K
Stacks
97
Followers
12.8K
Followers
190
Votes
468
Votes
0
Pros & Cons
Pros
  • 149
    Drop-in mysql replacement
  • 100
    Great performance
  • 74
    Open source
  • 55
    Free
  • 44
    Easy setup
No community feedback yet
Integrations
No integrations available
Hadoop
Hadoop
Java
Java
Apache Impala
Apache Impala
Apache Thrift
Apache Thrift
Apache Hive
Apache Hive
Pig
Pig

What are some alternatives to MariaDB, Apache Parquet?

MongoDB

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

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

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

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

SQLite

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

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

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.

RethinkDB

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

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

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.

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