InfluxDB vs RocksDB

Need advice about which tool to choose?Ask the StackShare community!

InfluxDB

891
928
+ 1
164
RocksDB

88
208
+ 1
11
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InfluxDB vs RocksDB: What are the differences?

Developers describe InfluxDB as "An open-source distributed time series database with no external dependencies". 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.. On the other hand, RocksDB is detailed as "Embeddable persistent key-value store for fast storage, developed and maintained by Facebook Database Engineering Team". RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.

InfluxDB and RocksDB can be categorized as "Databases" tools.

Some of the features offered by InfluxDB are:

  • Time-Centric Functions
  • Scalable Metrics
  • Events

On the other hand, RocksDB provides the following key features:

  • Designed for application servers wanting to store up to a few terabytes of data on locally attached Flash drives or in RAM
  • Optimized for storing small to medium size key-values on fast storage -- flash devices or in-memory
  • Scales linearly with number of CPUs so that it works well on ARM processors

"Time-series data analysis" is the top reason why over 36 developers like InfluxDB, while over 2 developers mention "Very fast" as the leading cause for choosing RocksDB.

InfluxDB and RocksDB are both open source tools. It seems that InfluxDB with 16.7K GitHub stars and 2.38K forks on GitHub has more adoption than RocksDB with 14.3K GitHub stars and 3.12K GitHub forks.

According to the StackShare community, InfluxDB has a broader approval, being mentioned in 119 company stacks & 39 developers stacks; compared to RocksDB, which is listed in 6 company stacks and 7 developer stacks.

Advice on InfluxDB and RocksDB
Needs advice
on
KafkaKafkaInfluxDBInfluxDB
and
HadoopHadoop

I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.

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Replies (1)
Recommends
DruidDruid

Druid Could be an amazing solution for your use case, My understanding, and the assumption is you are looking to export your data from MariaDB for Analytical workload. It can be used for time series database as well as a data warehouse and can be scaled horizontally once your data increases. It's pretty easy to set up on any environment (Cloud, Kubernetes, or Self-hosted nix system). Some important features which make it a perfect solution for your use case. 1. It can do streaming ingestion (Kafka, Kinesis) as well as batch ingestion (Files from Local & Cloud Storage or Databases like MySQL, Postgres). In your case MariaDB (which has the same drivers to MySQL) 2. Columnar Database, So you can query just the fields which are required, and that runs your query faster automatically. 3. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. 4. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures 5. Gives ana amazing centralized UI to manage data sources, query, tasks.

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Needs advice
on
TimescaleDBTimescaleDBMongoDBMongoDB
and
InfluxDBInfluxDB

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

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Replies (3)
Yaron Lavi
Recommends
PostgreSQLPostgreSQL

We had a similar challenge. We started with DynamoDB, Timescale, and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us a We had a similar challenge. We started with DynamoDB, Timescale and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us better performance by far.

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Recommends
DruidDruid

Druid is amazing for this use case and is a cloud-native solution that can be deployed on any cloud infrastructure or on Kubernetes. - Easy to scale horizontally - Column Oriented Database - SQL to query data - Streaming and Batch Ingestion - Native search indexes It has feature to work as TimeSeriesDB, Datawarehouse, and has Time-optimized partitioning.

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Ankit Malik
Software Developer at CloudCover · | 3 upvotes · 102.8K views
Recommends
Google BigQueryGoogle BigQuery

if you want to find a serverless solution with capability of a lot of storage and SQL kind of capability then google bigquery is the best solution for that.

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Decisions about InfluxDB and RocksDB
Benoit Larroque
Principal Engineer at Sqreen · | 2 upvotes · 63.1K views

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

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Pros of InfluxDB
Pros of RocksDB
  • 52
    Time-series data analysis
  • 28
    Easy setup, no dependencies
  • 24
    Fast, scalable & open source
  • 21
    Open source
  • 19
    Real-time analytics
  • 6
    Continuous Query support
  • 5
    Easy Query Language
  • 4
    HTTP API
  • 4
    Out-of-the-box, automatic Retention Policy
  • 1
    Offers Enterprise version
  • 1
    Free Open Source version
  • 5
    Very fast
  • 3
    Made by Facebook
  • 2
    Consistent performance
  • 1
    Ability to add logic to the database layer where needed

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Cons of InfluxDB
Cons of RocksDB
  • 4
    Instability
  • 1
    HA or Clustering is only in paid version
    Be the first to leave a con

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    - No public GitHub repository available -

    What is 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.

    What is RocksDB?

    RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use InfluxDB?
    What companies use RocksDB?
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    What tools integrate with InfluxDB?
    What tools integrate with RocksDB?

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    What are some alternatives to InfluxDB and RocksDB?
    TimescaleDB
    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.
    Redis
    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.
    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.
    Elasticsearch
    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
    Prometheus
    Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.
    See all alternatives