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PerconaXtraDBCluster vs TimescaleDB: What are the differences?
Developers describe PerconaXtraDBCluster as "Percona XtraDB Cluster is an active/active high availability and high scalability open source solution for MySQL® clustering". Percona XtraDB Cluster is an active/active high availability and high scalability open source solution for MySQL® clustering. It integrates Percona Server and Percona XtraBackup with the Codership Galera library of MySQL high availability solutions in a single package that enables you to create a cost-effective MySQL high availability cluster. 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.
PerconaXtraDBCluster and TimescaleDB can be primarily classified as "Databases" tools.
Some of the features offered by PerconaXtraDBCluster are:
- ProxySQL load balancer
- Multi-master replication
- Synchronous replication
On the other hand, TimescaleDB provides the following key features:
- Packaged as a PostgreSQL extension
- Full ANSI SQL
- JOINs (e.g., across PostgreSQL tables)
TimescaleDB is an open source tool with 7.28K GitHub stars and 385 GitHub forks. Here's a link to TimescaleDB's open source repository on GitHub.
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
PostgreSQLWe 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.
DruidDruid 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.
Google BigQueryif 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 PerconaXtraDBCluster
Pros of TimescaleDB
- Open source8
- Easy Query Language7
- Time-series data analysis6
- Established postgresql API and support5
- Reliable4
- Chunk-based compression2
- High-performance2
- Paid support for automatic Retention Policy2
- Postgres integration2
- Fast and scalable2
- Case studies1
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Cons of PerconaXtraDBCluster
Cons of TimescaleDB
- Licensing issues when running on managed databases5
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What is PerconaXtraDBCluster?
What is TimescaleDB?
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