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  5. Apache Parquet vs Couchbase

Apache Parquet vs Couchbase

OverviewDecisionsComparisonAlternatives

Overview

Couchbase
Couchbase
Stacks505
Followers606
Votes110
Apache Parquet
Apache Parquet
Stacks97
Followers190
Votes0

Apache Parquet vs Couchbase: What are the differences?

<Apache Parquet vs Couchbase>

1. **Data Storage Format**: Apache Parquet is a columnar storage format that organizes data by columns, which allows for efficient data compression and query performance. On the other hand, Couchbase is a NoSQL database that stores data in JSON format, offering flexibility in data modeling but potentially lower query performance compared to columnar storage.
2. **Usage and Purpose**: Apache Parquet is primarily used for storing and analyzing large datasets efficiently, especially in data warehousing and analytics applications. On the contrary, Couchbase is a multi-model NoSQL database that can be used for various use cases such as real-time applications, caching, and mobile data synchronization.
3. **Query Performance**: Due to its columnar storage format, Apache Parquet provides faster query performance when dealing with analytical queries that involve scanning a subset of columns. Couchbase, being a document-oriented NoSQL database, may have slower query performance for analytical queries compared to Apache Parquet.
4. **Data Compression**: Apache Parquet utilizes efficient compression techniques such as run-length encoding and dictionary encoding to optimize storage space and improve query performance. In contrast, Couchbase provides flexible data modeling with JSON format but may not offer as efficient data compression as Apache Parquet.
5. **Data Consistency and Transactions**: Couchbase offers strong consistency and supports ACID transactions, making it suitable for applications that require transactional operations and data integrity. Apache Parquet, being a storage format, does not directly handle data consistency or transactional operations.
6. **Scalability**: Couchbase is designed for horizontal scalability and distributed data management, making it suitable for scaling out data storage and processing across multiple nodes. While Apache Parquet can benefit from distributed processing systems like Apache Hadoop for scalability, its primary focus is on efficient storage and retrieval of columnar data.

In Summary, Apache Parquet and Couchbase differ in their data storage format, usage, query performance, data compression, data consistency, and scalability capabilities.

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Advice on Couchbase, Apache Parquet

Gabriel
Gabriel

CEO at Naologic

Jan 2, 2020

DecidedonCouchDBCouchDBCouchbaseCouchbaseMemcachedMemcached

We implemented our first large scale EPR application from naologic.com using CouchDB .

Very fast, replication works great, doesn't consume much RAM, queries are blazing fast but we found a problem: the queries were very hard to write, it took a long time to figure out the API, we had to go and write our own @nodejs library to make it work properly.

It lost most of its support. Since then, we migrated to Couchbase and the learning curve was steep but all worth it. Memcached indexing out of the box, full text search works great.

592k views592k
Comments
Mike
Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k
Comments

Detailed Comparison

Couchbase
Couchbase
Apache Parquet
Apache Parquet

Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.

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.

JSON document database; N1QL (SQL-like query language); Secondary Indexing; Full-Text Indexing; Eventing/Triggers; Real-Time Analytics; Mobile Synchronization for offline support; Autonomous Operator for Kubernetes and OpenShift
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
Stacks
505
Stacks
97
Followers
606
Followers
190
Votes
110
Votes
0
Pros & Cons
Pros
  • 18
    Flexible data model, easy scalability, extremely fast
  • 18
    High performance
  • 9
    Mobile app support
  • 7
    You can query it with Ansi-92 SQL
  • 6
    All nodes can be read/write
Cons
  • 4
    Terrible query language
No community feedback yet
Integrations
Hadoop
Hadoop
Kafka
Kafka
Elasticsearch
Elasticsearch
Kubernetes
Kubernetes
Apache Spark
Apache Spark
Hadoop
Hadoop
Java
Java
Apache Impala
Apache Impala
Apache Thrift
Apache Thrift
Apache Hive
Apache Hive
Pig
Pig

What are some alternatives to Couchbase, 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.

MariaDB

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

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.

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