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InfluxDB vs TokuMX: What are the differences?
InfluxDB: 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.; TokuMX: A high-performance, concurrent, compressing, drop-in replacement engine for MongoDB. TokuMX is a drop-in replacement for MongoDB, and offers 20X performance improvements, 90% reduction in database size, and support for ACID transactions with MVCC. TokuMX has the same binaries, supports the same drivers, data model, and features of MongoDB, because it shares much of its code with MongoDB.
InfluxDB and TokuMX belong to "Databases" category of the tech stack.
"Time-series data analysis" is the primary reason why developers consider InfluxDB over the competitors, whereas "When your two-week MongoDB love affair ends, try this" was stated as the key factor in picking TokuMX.
InfluxDB and TokuMX are both open source tools. It seems that InfluxDB with 16.7K GitHub stars and 2.38K forks on GitHub has more adoption than TokuMX with 679 GitHub stars and 90 GitHub forks.
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.
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.
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
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.
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.
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.
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
Pros of InfluxDB
- Time-series data analysis59
- Easy setup, no dependencies30
- Fast, scalable & open source24
- Open source21
- Real-time analytics20
- Continuous Query support6
- Easy Query Language5
- HTTP API4
- Out-of-the-box, automatic Retention Policy4
- Offers Enterprise version1
- Free Open Source version1
Pros of TokuMX
- When your two-week MongoDB love affair ends, try this3
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Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
Cons of TokuMX
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