LevelDB vs TimescaleDB

Overview

LevelDB

Stacks108

Followers111

Votes0

GitHub Stars38.3K

Forks8.1K

TimescaleDB

Stacks226

Followers374

Votes44

GitHub Stars20.6K

Forks988

LevelDB vs TimescaleDB: What are the differences?

Developers describe LevelDB as "*An open-source on-disk key-value store *". It is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values. It has been ported to a variety of Unix-based systems, macOS, Windows, and Android. On the other hand, TimescaleDB is detailed as "Scalable time-series database optimized for fast ingest and complex queries. Purpose-built as a PostgreSQL extension". TimescaleDB is the only open-source time-series database that natively supports full-SQL at scale, combining the power, reliability, and ease-of-use of a relational database with the scalability typically seen in NoSQL databases.

LevelDB and TimescaleDB can be primarily classified as "Databases" tools.

LevelDB and TimescaleDB are both open source tools. It seems that LevelDB with 18.3K GitHub stars and 4.32K forks on GitHub has more adoption than TimescaleDB with 7.39K GitHub stars and 393 GitHub forks.

WakaTime, ScreenAware, and AgFlow are some of the popular companies that use TimescaleDB, whereas LevelDB is used by Google, Vigour, and Compile Inc. TimescaleDB has a broader approval, being mentioned in 21 company stacks & 19 developers stacks; compared to LevelDB, which is listed in 5 company stacks and 3 developer stacks.

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Advice on LevelDB, TimescaleDB

Anonymous

Apr 21, 2020

Needs advice

We are building an IOT service with heavy write throughput and fewer reads (we need downsampling records). We prefer to have good reliability when comes to data and prefer to have data retention based on policies.

So, we are looking for what is the best underlying DB for ingesting a lot of data and do queries easily

381k views381k

Comments

Benoit

Principal Engineer at Sqreen

Sep 21, 2019

Decided

I chose TimescaleDB because to be the backend system of our production monitoring system. We needed to be able to keep track of multiple high cardinality dimensions.

The drawbacks of this decision are our monitoring system is a bit more ad hoc than it used to (New Relic Insights)

We are combining this with Grafana for display and Telegraf for data collection

155k views155k

Comments

Detailed Comparison

LevelDB	TimescaleDB
It is a fast key-value storage library written at Google that provides an ordered mapping from string keys to string values. It has been ported to a variety of Unix-based systems, macOS, Windows, and Android.	TimescaleDB: An open-source database built for analyzing time-series data with the power and convenience of SQL — on premise, at the edge, or in the cloud.
Simple key-value stores with Go, C++, Node.js and more!	Packaged as a PostgreSQL extension;Full ANSI SQL;JOINs (e.g., across PostgreSQL tables);Complex queries;Secondary indexes;Composite indexes;Support for very high cardinality data;Triggers;Constraints;UPSERTS;JSON/JSONB;Ability to ingest out of order data;Ability to perform accurate rollups;Data retention policies;Fast deletes;Integration with PostGIS and the rest of the PostgreSQL ecosystem;
Statistics
GitHub Stars 38.3K	GitHub Stars 20.6K
GitHub Forks 8.1K	GitHub Forks 988
Stacks 108	Stacks 226
Followers 111	Followers 374
Votes 0	Votes 44
Pros & Cons
No community feedback yet	Pros 9 Open source 8 Easy Query Language 7 Time-series data analysis 5 Established postgresql API and support 4 Reliable Cons 5 Licensing issues when running on managed databases
Integrations
Java Windows macOS	Prometheus Equinix Metal Ruby PostgreSQL Django Kubernetes pgAdmin Python Kafka Datadog

What are some alternatives to LevelDB, TimescaleDB?

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.