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65 lines
5.1 KiB
Markdown
# Distinctive Features of ClickHouse {#distinctive-features-of-clickhouse}
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## True Column-Oriented DBMS {#true-column-oriented-dbms}
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In a true column-oriented DBMS, no extra data is stored with the values. Among other things, this means that constant-length values must be supported, to avoid storing their length “number” next to the values. As an example, a billion UInt8-type values should actually consume around 1 GB uncompressed, or this will strongly affect the CPU use. It is very important to store data compactly (without any “garbage”) even when uncompressed, since the speed of decompression (CPU usage) depends mainly on the volume of uncompressed data.
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This is worth noting because there are systems that can store values of different columns separately, but that can’t effectively process analytical queries due to their optimization for other scenarios. Examples are HBase, BigTable, Cassandra, and HyperTable. In these systems, you will get throughput around a hundred thousand rows per second, but not hundreds of millions of rows per second.
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It’s also worth noting that ClickHouse is a database management system, not a single database. ClickHouse allows creating tables and databases in runtime, loading data, and running queries without reconfiguring and restarting the server.
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## Data Compression {#data-compression}
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Some column-oriented DBMSs (InfiniDB CE and MonetDB) do not use data compression. However, data compression does play a key role in achieving excellent performance.
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## Disk Storage of Data {#disk-storage-of-data}
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Keeping data physically sorted by primary key makes it possible to extract data for it’s specific values or value ranges with low latency, less than few dozen milliseconds. Some column-oriented DBMSs (such as SAP HANA and Google PowerDrill) can only work in RAM. This approach encourages the allocation of a larger hardware budget than is actually necessary for real-time analysis. ClickHouse is designed to work on regular hard drives, which means the cost per GB of data storage is low, but SSD and additional RAM are also fully used if available.
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## Parallel Processing on Multiple Cores {#parallel-processing-on-multiple-cores}
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Large queries are parallelized in a natural way, taking all the necessary resources that available on the current server.
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## Distributed Processing on Multiple Servers {#distributed-processing-on-multiple-servers}
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Almost none of the columnar DBMSs mentioned above have support for distributed query processing.
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In ClickHouse, data can reside on different shards. Each shard can be a group of replicas that are used for fault tolerance. The query is processed on all the shards in parallel. This is transparent for the user.
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## SQL Support {#sql-support}
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ClickHouse supports a declarative query language based on SQL that is identical to the SQL standard in many cases.
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Supported queries include GROUP BY, ORDER BY, subqueries in FROM, IN, and JOIN clauses, and scalar subqueries.
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Dependent subqueries and window functions are not supported.
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## Vector Engine {#vector-engine}
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Data is not only stored by columns, but is processed by vectors (parts of columns). This allows us to achieve high CPU efficiency.
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## Real-time Data Updates {#real-time-data-updates}
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ClickHouse supports tables with a primary key. In order to quickly perform queries on the range of the primary key, the data is sorted incrementally using the merge tree. Due to this, data can continually be added to the table. No locks are taken when new data is ingested.
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## Index {#index}
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Having a data physically sorted by primary key makes it possible to extract data for it’s specific values or value ranges with low latency, less than few dozen milliseconds.
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## Suitable for Online Queries {#suitable-for-online-queries}
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Low latency means that queries can be processed without delay and without trying to prepare answer in advance, right at the same moment while user interface page is loading. In other words, online.
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## Support for Approximated Calculations {#support-for-approximated-calculations}
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ClickHouse provides various ways to trade accuracy for performance:
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1. Aggregate functions for approximated calculation of the number of distinct values, medians, and quantiles.
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2. Running a query based on a part (sample) of data and getting an approximated result. In this case, proportionally less data is retrieved from the disk.
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3. Running an aggregation for a limited number of random keys, instead of for all keys. Under certain conditions for key distribution in the data, this provides a reasonably accurate result while using fewer resources.
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## Data replication and data integrity support {#data-replication-and-data-integrity-support}
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Uses asynchronous multimaster replication. After being written to any available replica, data is distributed to all the remaining replicas in the background. The system maintains identical data on different replicas. Recovery after most failures is performed automatically, and in complex cases — semi-automatically.
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For more information, see the section [Data replication](../operations/table_engines/replication.md).
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[Original article](https://clickhouse.tech/docs/en/introduction/distinctive_features/) <!--hide-->
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