If not sure, put columns with low cardinality . ClickHouse stores data in LSM-like format (MergeTree Family) 1. Although in both tables exactly the same data is stored (we inserted the same 8.87 million rows into both tables), the order of the key columns in the compound primary key has a significant influence on how much disk space the compressed data in the table's column data files requires: Having a good compression ratio for the data of a table's column on disk not only saves space on disk, but also makes queries (especially analytical ones) that require the reading of data from that column faster, as less i/o is required for moving the column's data from disk to the main memory (the operating system's file cache). Elapsed: 149.432 sec. The generic exclusion search algorithm that ClickHouse is using instead of the binary search algorithm when a query is filtering on a column that is part of a compound key, but is not the first key column is most effective when the predecessor key column has low(er) cardinality. Each mark file entry for a specific column is storing two locations in the form of offsets: The first offset ('block_offset' in the diagram above) is locating the block in the compressed column data file that contains the compressed version of the selected granule. Processed 8.87 million rows, 18.40 GB (60.78 thousand rows/s., 126.06 MB/s. Only for that one granule does ClickHouse then need the physical locations in order to stream the corresponding rows for further processing. These orange-marked column values are the primary key column values of each first row of each granule. Offset information is not needed for columns that are not used in the query e.g. ), Executor): Key condition: (column 1 in [749927693, 749927693]), 980/1083 marks by primary key, 980 marks to read from 23 ranges, Executor): Reading approx. ClickHouse now uses the selected mark number (176) from the index for a positional array lookup in the UserID.mrk mark file in order to get the two offsets for locating granule 176. What are the benefits of learning to identify chord types (minor, major, etc) by ear? It just defines sort order of data to process range queries in optimal way. Open the details box for specifics. Once the located file block is uncompressed into the main memory, the second offset from the mark file can be used to locate granule 176 within the uncompressed data. mark 1 in the diagram above thus indicates that the UserID values of all table rows in granule 1, and in all following granules, are guaranteed to be greater than or equal to 4.073.710. In this case it makes sense to specify the sorting key that is different from the primary key. MergeTreePRIMARY KEYprimary.idx. in this case. We use this query for calculating the cardinalities of the three columns that we want to use as key columns in a compound primary key (note that we are using the URL table function for querying TSV data ad-hocly without having to create a local table). allows you only to add new (and empty) columns at the end of primary key, or remove some columns from the end of primary key . ), 11.38 MB (18.41 million rows/s., 655.75 MB/s.). The output of the ClickHouse client shows: If we would have specified only the sorting key, then the primary key would be implicitly defined to be equal to the sorting key. ), 0 rows in set. Elapsed: 2.935 sec. In order to confirm (or not) that some row(s) in granule 176 contain a UserID column value of 749.927.693, all 8192 rows belonging to this granule need to be streamed into ClickHouse. Can dialogue be put in the same paragraph as action text? However if the key columns in a compound primary key have big differences in cardinality, then it is beneficial for queries to order the primary key columns by cardinality in ascending order. In traditional relational database management systems, the primary index would contain one entry per table row. ClickHouse. Is the amplitude of a wave affected by the Doppler effect? The diagram below shows that the index stores the primary key column values (the values marked in orange in the diagram above) for each first row for each granule. The quite similar cardinality of the primary key columns UserID and URL The command is lightweight in a sense that it only changes metadata. The corresponding trace log in the ClickHouse server log file confirms that ClickHouse is running binary search over the index marks: Create a projection on our existing table: ClickHouse is storing the column data files (.bin), the mark files (.mrk2) and the primary index (primary.idx) of the hidden table in a special folder (marked in orange in the screenshot below) next to the source table's data files, mark files, and primary index files: The hidden table (and it's primary index) created by the projection can now be (implicitly) used to significantly speed up the execution of our example query filtering on the URL column. ID uuid.UUID `gorm:"type:uuid . What screws can be used with Aluminum windows? Creates a table named table_name in the db database or the current database if db is not set, with the structure specified in brackets and the engine engine. This is a query that is filtering on the UserID column of the table where we ordered the key columns (URL, UserID, IsRobot) by cardinality in descending order: This is the same query on the table where we ordered the key columns (IsRobot, UserID, URL) by cardinality in ascending order: We can see that the query execution is significantly more effective and faster on the table where we ordered the key columns by cardinality in ascending order. When we create MergeTree table we have to choose primary key which will affect most of our analytical queries performance. a granule size of two i.e. If trace logging is enabled then the ClickHouse server log file shows that ClickHouse was running a binary search over the 1083 UserID index marks, in order to identify granules that possibly can contain rows with a UserID column value of 749927693. In order to significantly improve the compression ratio for the content column while still achieving fast retrieval of specific rows, pastila.nl is using two hashes (and a compound primary key) for identifying a specific row: Now the rows on disk are first ordered by fingerprint, and for rows with the same fingerprint value, their hash value determines the final order. ), Executor): Running binary search on index range for part prj_url_userid (1083 marks), Executor): Choose complete Normal projection prj_url_userid, Executor): projection required columns: URL, UserID, cardinality_URLcardinality_UserIDcardinality_IsRobot, 2.39 million 119.08 thousand 4.00 , , 1 row in set. To make this (way) more efficient and (much) faster, we need to use a table with a appropriate primary key. Elapsed: 2.898 sec. if the table contains 16384 rows then the index will have two index entries. The uncompressed data size is 8.87 million events and about 700 MB. And because of that is is also unlikely that cl values are ordered (locally - for rows with the same ch value). Theorems in set theory that use computability theory tools, and vice versa. The following diagram shows the three mark files UserID.mrk, URL.mrk, and EventTime.mrk that store the physical locations of the granules for the tables UserID, URL, and EventTime columns. Each single row of the 8.87 million rows of our table was streamed into ClickHouse. we switch the order of the key columns (compared to our, the implicitly created table is listed by the, it is also possible to first explicitly create the backing table for a materialized view and then the view can target that table via the, if new rows are inserted into the source table hits_UserID_URL, then that rows are automatically also inserted into the implicitly created table, Effectively the implicitly created table has the same row order and primary index as the, if new rows are inserted into the source table hits_UserID_URL, then that rows are automatically also inserted into the hidden table, a query is always (syntactically) targeting the source table hits_UserID_URL, but if the row order and primary index of the hidden table allows a more effective query execution, then that hidden table will be used instead, please note that projections do not make queries that use ORDER BY more efficient, even if the ORDER BY matches the projection's ORDER BY statement (see, Effectively the implicitly created hidden table has the same row order and primary index as the, the efficiency of the filtering on secondary key columns in queries, and. This column separation and sorting implementation make future data retrieval more efficient . Elapsed: 95.959 sec. Lastly, in order to simplify the discussions later on in this guide and to make the diagrams and results reproducible, we optimize the table using the FINAL keyword: In general it is not required nor recommended to immediately optimize a table If not sure, put columns with low cardinality first and then columns with high cardinality. Thanks in advance. Default granule size is 8192 records, so number of granules for a table will equal to: A granule is basically a virtual minitable with low number of records (8192 by default) that are subset of all records from main table. and locality (the more similar the data is, the better the compression ratio is). Despite the name, primary key is not unique. The first (based on physical order on disk) 8192 rows (their column values) logically belong to granule 0, then the next 8192 rows (their column values) belong to granule 1 and so on. The output for the ClickHouse client is now showing that instead of doing a full table scan, only 8.19 thousand rows were streamed into ClickHouse. The compressed size on disk of all rows together is 206.94 MB. For both the efficient filtering on secondary key columns in queries and the compression ratio of a table's column data files it is beneficial to order the columns in a primary key by their cardinality in ascending order. ClickHouse sorts data by primary key, so the higher the consistency, the better the compression. 4ClickHouse . When creating a second table with a different primary key then queries must be explicitly send to the table version best suited for the query, and new data must be inserted explicitly into both tables in order to keep the tables in sync: With a materialized view the additional table is implicitly created and data is automatically kept in sync between both tables: And the projection is the most transparent option because next to automatically keeping the implicitly created (and hidden) additional table in sync with data changes, ClickHouse will automatically choose the most effective table version for queries: In the following we discuss this three options for creating and using multiple primary indexes in more detail and with real examples. The following calculates the top 10 most clicked urls for the UserID 749927693. Although in general it is not the best use case for ClickHouse, In contrast to the diagram above, the diagram below sketches the on-disk order of rows for a primary key where the key columns are ordered by cardinality in descending order: Now the table's rows are first ordered by their ch value, and rows that have the same ch value are ordered by their cl value. Processed 8.87 million rows, 15.88 GB (84.73 thousand rows/s., 151.64 MB/s. Because the hash column is used as the primary key column. However, if the UserID values of mark 0 and mark 1 would be the same in the diagram above (meaning that the UserID value stays the same for all table rows within the granule 0), the ClickHouse could assume that all URL values of all table rows in granule 0 are larger or equal to 'http://showtopics.html%3'. ), 0 rows in set. ClickHouseClickHouse. An intuitive solution for that might be to use a UUID column with a unique value per row and for fast retrieval of rows to use that column as a primary key column. How can I test if a new package version will pass the metadata verification step without triggering a new package version? A granule is the smallest indivisible data set that is streamed into ClickHouse for data processing. For the second case the ordering of the key columns in the compound primary key is significant for the effectiveness of the generic exclusion search algorithm. For the fastest retrieval, the UUID column would need to be the first key column. In order to have consistency in the guides diagrams and in order to maximise compression ratio we defined a separate sorting key that includes all of our table's columns (if in a column similar data is placed close to each other, for example via sorting, then that data will be compressed better). Now we can inspect the content of the primary index via SQL: This matches exactly our diagram of the primary index content for our example table: The primary key entries are called index marks because each index entry is marking the start of a specific data range. Thanks for contributing an answer to Stack Overflow! The indirection provided by mark files avoids storing, directly within the primary index, entries for the physical locations of all 1083 granules for all three columns: thus avoiding having unnecessary (potentially unused) data in main memory. This capability comes at a cost: additional disk and memory overheads and higher insertion costs when adding new rows to the table and entries to the index (and also sometimes rebalancing of the B-Tree). Find centralized, trusted content and collaborate around the technologies you use most. The second offset ('granule_offset' in the diagram above) from the mark-file provides the location of the granule within the uncompressed block data. This compressed block potentially contains a few compressed granules. Index marks 2 and 3 for which the URL value is greater than W3 can be excluded, since index marks of a primary index store the key column values for the first table row for each granule and the table rows are sorted on disk by the key column values, therefore granule 2 and 3 can't possibly contain URL value W3. The higher the cardinality difference between the key columns is, the more the order of those columns in the key matters. The primary index is created based on the granules shown in the diagram above. The second index entry (mark 1) is storing the minimum and maximum URL values for the rows belonging to the next 4 granules of our table, and so on. (ClickHouse also created a special mark file for to the data skipping index for locating the groups of granules associated with the index marks.). ClickHouse needs to locate (and stream all values from) granule 176 from both the UserID.bin data file and the URL.bin data file in order to execute our example query (top 10 most clicked URLs for the internet user with the UserID 749.927.693). Despite the name, primary key is not unique. The following illustrates in detail how ClickHouse is building and using its sparse primary index. The ClickHouse MergeTree Engine Family has been designed and optimized to handle massive data volumes. Processed 8.87 million rows, 15.88 GB (74.99 thousand rows/s., 134.21 MB/s. Why is Noether's theorem not guaranteed by calculus? . As a consequence, if we want to significantly speed up our sample query that filters for rows with a specific URL then we need to use a primary index optimized to that query. Processed 8.87 million rows, 18.40 GB (59.38 thousand rows/s., 123.16 MB/s. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Once ClickHouse has identified and selected the index mark for a granule that can possibly contain matching rows for a query, a positional array lookup can be performed in the mark files in order to obtain the physical locations of the granule. Optimal way size is 8.87 million rows, 15.88 GB ( 60.78 thousand rows/s., 126.06 MB/s. ) is... 18.40 GB ( 84.73 thousand rows/s., 655.75 MB/s. ) process range queries in optimal way clickhouse primary key create. Compressed size on disk of all rows together is 206.94 MB management systems, the uuid would! 151.64 MB/s. ) I test if a new package version will pass metadata! Clickhouse for data processing more similar the data is, the better the compression is... In optimal way create MergeTree table we have to choose primary key columns UserID and URL the command lightweight! 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