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Overview

Citus DB is a scalable analytics database that is built on top of PostgreSQL. Designed with parallelism in mind, it is the first such database that enables running distributed SQL queries on data that's external to the database.

Citus DB gives flexible and fast access to large volumes of data. Datasets that are append-only are particularly applicable; and these include user actions, event streams, log files, and machine generated data. Citus DB partitions these data and the queries that operate on the data; and efficiently executes queries that involve look-ups, complex selections, groupings and orderings, and analytic functions. Further, Citus DB supports joins between one large and multiple small tables.

Citus DB also enables real-time responsiveness. Query run times start at 100ms for simple queries, and increase depending on query complexity and the dataset size. One feature that is missing from Citus DB involves real-time inserts and updates; and the database doesn't yet fully support real-time analytics.


High-level Architecture

At a high level, Citus DB distributes the data across a cluster of nodes, and then processes incoming analytic queries in parallel across these nodes. Citus DB achieves this by making three particular changes to the underlying database:

1.  

Master / worker nodes: The user chooses one of the nodes in the cluster as the master node, and adds the names of worker nodes to a membership file on the master node. From that point on, the user interacts with the master node through standard PostgreSQL interfaces for data loading and querying. Behind the covers, data and the queries are distributed across worker nodes in the cluster.

2.  

Data storage: When the user is loading data into the cluster, data are split into shards of fixed size, and these shards are then replicated across multiple worker nodes. Then, metadata and statistics about these shards are saved on the master node. Further, for users who don't want to load their data into a database, CitusDB enables efficient SQL querying through distributed foreign tables.

3.  

Query processing: When the user issues a query, the master node partitions the query into smaller queries where each smaller query can be run independently on a shard. The master node then assigns these smaller queries to worker nodes, oversees their execution, merges their results, and returns the final result to the user. To ensure that all queries are executed in a scalable manner, the master node also applies optimizations that minimize the amount of data transferred across the network.

These three changes closely align Citus DB's architecture with that of Hadoop's. In fact, they allow us to combine the scalability and fault tolerance of Hadoop with the performance and SQL-compliance of databases. Users can easily scale out a cluster as more data are added, take advantage of standard data visualization tools, and also benefit from performance improvements that databases typically employ (indexes, join order optimizations, etc).


  • Single Node Cluster

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