Apache HBase vs. ClickHouse

HBase is well suited for streaming ingest, fast lookups, massive datasets, data warehouse lookup tables, RDBMS replacement, MongoDB replacement, key-value store, data scans, logs, JSON storage and some binary storage. My preferred use case is for storing data points like time series or data produced by sensors. I often use HBase when I need data available immediately and I am not looking for transactions. This is a great store for really wide tables with tons of columns. It is also great if you are not sure what type of data you are going to have. It really excels at sparse data.

Incentivized

ClickHouse delivers exceptional speed and performance, positioning it as the top choice for managing large-scale analytical workloads. With a bunch of built-in functions, it empowers analysts to extract maximum insights from data effortlessly. If your scenario is to deal with analytical questions, then ClickHouse is for you, but if you are looking into a transactional database, that's not the case; even their table engines are not made for this.

Incentivized

• Scalable and truly non-relational data
• HBase operations run in real-time on its database rather than MapReduce jobs
• Scales linearly to support billions of rows with millions of columns

Incentivized

• Their MergeTree table engine provide impressive performance for data insert in bulk
• Not only data insert but also the way MergeTree engine uses Primary Keys to sort the data and perform data skipping based on the granules its also their secret for ridiculous fast queries
• Data compression its also great
• They provide especial table engines that allow you to read data directly from other sources like S3
• Since its written with C++ you have very granular data types and especial ones like enum, LowCardinality and etc, they save you a lot of storage since are stored as integer values
• ClickHouse functions besides the ones that respect ANSI Standards are also awesome and useful

Incentivized

• Write performance
• Performance support for parquet file format. supports, but performance wise still not there
• API / library availability for spark, rather than creating a new library for it

Incentivized

• Avro data manipulation
• Kafka consistency
• DDL operations errors (by replica configuration)

Incentivized

There's really not anything else out there that I've seen comparable for my use cases. HBase has never proven me wrong. Some companies align their whole business on HBase and are moving all of their infrastructure from other database engines to HBase. It's also open source and has a very collaborative community.

Incentivized

Compared NoSQL databases with traditional databases for faster retrieval and consistency. As MongoDB is a NoSQL supports dynamic fields, however, query performance is bad for aggregations and added maintenance. When compared with MySQL and Teradata, it could not scale up as fast as Hbase and added cost involved to it. HBase can be easily scalable to a huge volume of records, have a faster lookup and provides consistency

Incentivized

ClickHouse was not compared to them as a competitor but as the ideal partner to complete an information analysis system, providing users with the most complete and efficient tools. Therefore, in this case it was considered that it would be the ideal candidate due to its characteristics compared to the other competitors.

Incentivized

• Positive: Open source, easy to use, good to store big data.
• Negative: SQL functionalities are not available.
• More memory utilization
• More troubleshooting

Incentivized

• Queries that used to take more than 2 minutes now take less than 1 second
• Possibility to analyze use cases in real time (before was impossible)
• The applications are more complete and the users decisions are better

Incentivized