TiDB
MongoDB
Redis
Apache Cassandra
ClickHouse
BangDB
PostgreSQL
Cassandra.Global
Hadoop
Apache Spark
Apache Cassandra
Apache Flink
PostgreSQL
MongoDB
Apache Storm
Apache Kafka
TiDB HadoopHadoop might be a bit more popular than TiDB. We know about 15 links to it since March 2021 and only 15 links to TiDB. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Tidb has been around for a while, it is distributed, written in Go and Rust, and MySQL compatible. https://github.com/pingcap/tidb. - Source: Hacker News / 30 days ago
PingCAP | https://www.pingcap.com | Database Engineer, Product Manager, Developer Advocate and more | Remote in California | Full-time We work on a MySQL compatible distributed database called TiDB https://github.com/pingcap/tidb/. - Source: Hacker News / over 1 year ago
Isn't TiDB built on top of TiKV?[0] [0]: https://github.com/pingcap/tidb. - Source: Hacker News / over 1 year ago
OLTP usually comes with high throughput of transactions, which means usually write(e.g., IUD - insert, update, delete) to read (e.g., select) ratio is above 4 or 5 or even higher. There are some good benchmarks to test OLTP workload like TPC-C (https://www.tpc.org/tpcc/), and some benchmarks to test OLAP workload like TPC-H (https://www.tpc.org/tpch/). For mixed or hybrid OLTP and OLAP (it's called HTAP, see this... - Source: Hacker News / almost 2 years ago
I am very agree with some options of this blog. As the maintainer of the open source distributed database TiDB https://github.com/pingcap/tidb, we also face the same problem of choice. We have a community version, an enterprise version(of course, we must sell it to our customers to earn money) and also a cloud service named TiDB cloud. Seven years before, we started to build TiDB to solve MySQL sharding problem,... - Source: Hacker News / almost 2 years ago
Did you check out tools like https://hadoop.apache.org/ ? Source: about 1 year ago
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago
There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 1 year ago
A copy of Hadoop installed on each of these machines. You can download Hadoop from the Apache website, or you can use a distribution like Cloudera or Hortonworks. - Source: dev.to / over 1 year ago
The Apache™ Hadoop™ project develops open-source software for reliable, scalable, distributed computing. - Source: dev.to / over 1 year ago
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.
Apache Cassandra - The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
ClickHouse - ClickHouse is an open-source column-oriented database management system that allows generating analytical data reports in real time.
