clickhouse primary key

ORDER BY PRIMARY KEY, ORDER BY . The primary index is created based on the granules shown in the diagram above. In a compound primary key the order of the key columns can significantly influence both: In order to demonstrate that, we will use a version of our web traffic sample data set The command changes the sorting key of the table to new_expression (an expression or a tuple of expressions). ), Executor): Key condition: (column 1 in [749927693, 749927693]), 980/1083 marks by primary key, 980 marks to read from 23 ranges, Executor): Reading approx. As shown in the diagram below. Suppose UserID had low cardinality. Feel free to skip this if you don't care about the time fields, and embed the ID field directly. https: . 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. Index mark 1 for which the URL value is smaller (or equal) than W3 and for which the URL value of the directly succeeding index mark is greater (or equal) than W3 is selected because it means that granule 1 can possibly contain rows with URL W3. The primary key needs to be a prefix of the sorting key if both are specified. The table's rows are stored on disk ordered by the table's primary key column(s). clickhouse sql . We discuss that second stage in more detail in the following section. ALTER TABLE xxx MODIFY PRIMARY KEY (.) 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. 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. You now have a 50% chance to get a collision every 1.05E16 generated UUID. The compressed size on disk of all rows together is 206.94 MB. This means that for each group of 8192 rows, the primary index will have one index entry, e.g. Elapsed: 2.898 sec. The primary index of our table with compound primary key (URL, UserID) was speeding up a query filtering on URL, but didn't provide much support for a query filtering on UserID. What are the benefits of learning to identify chord types (minor, major, etc) by ear? Searching an entry in a B(+)-Tree data structure has average time complexity of O(log2 n). The compromise is that two fields (fingerprint and hash) are required for the retrieval of a specific row in order to optimally utilise the primary index that results from the compound PRIMARY KEY (fingerprint, hash). And because the first key column cl has low cardinality, it is likely that there are rows with the same cl value. Insert all 8.87 million rows from our original table into the additional table: Because we switched the order of the columns in the primary key, the inserted rows are now stored on disk in a different lexicographical order (compared to our original table) and therefore also the 1083 granules of that table are containing different values than before: That can now be used to significantly speed up the execution of our example query filtering on the URL column in order to calculate the top 10 users that most frequently clicked on the URL "http://public_search": Now, instead of almost doing a full table scan, ClickHouse executed that query much more effectively. Create a table that has a compound primary key with key columns UserID and URL: In order to simplify the discussions later on in this guide, as well as make the diagrams and results reproducible, the DDL statement. 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. a query that is searching for rows with URL value = "W3". 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. tokenbf_v1ngrambf_v1String . How to turn off zsh save/restore session in Terminal.app. Sparse indexing is possible because ClickHouse is storing the rows for a part on disk ordered by the primary key column (s). ClickHouse uses a SQL-like query language for querying data and supports different data types, including integers, strings, dates, and floats. 1 or 2 columns are used in query, while primary key contains 3). And because of that is is also unlikely that cl values are ordered (locally - for rows with the same ch value). Our table is using wide format because the size of the data is larger than min_bytes_for_wide_part (which is 10 MB by default for self-managed clusters). For example, consider index mark 0 for which the URL value is smaller than W3 and for which the URL value of the directly succeeding index mark is also smaller than W3. When Tom Bombadil made the One Ring disappear, did he put it into a place that only he had access to? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Allowing to have different primary keys in different parts of table is theoretically possible, but introduce many difficulties in query execution. Because of the similarly high cardinality of the primary key columns UserID and URL, a query that filters on the second key column doesnt benefit much from the second key column being in the index. In the diagram above, the table's rows (their column values on disk) are first ordered by their cl value, and rows that have the same cl value are ordered by their ch value. aggregating and counting the URL values per group for all rows where the UserID is 749.927.693, before finally outputting the 10 largest URL groups in descending count order. URL index marks: Furthermore, this offset information is only needed for the UserID and URL columns. 319488 rows with 2 streams, 73.04 MB (340.26 million rows/s., 3.10 GB/s. I did found few examples in the documentation where primary keys are created by passing parameters to ENGINE section. 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 . ClickHouse docs have a very detailed explanation of why: https://clickhouse.com . The reason in simple: to check if the row already exists you need to do some lookup (key-value) alike (ClickHouse is bad for key-value lookups), in general case - across the whole huge table (which can be terabyte/petabyte size). These tables are designed to receive millions of row inserts per second and store very large (100s of Petabytes) volumes of data. ), path: ./store/d9f/d9f36a1a-d2e6-46d4-8fb5-ffe9ad0d5aed/all_1_9_2/, rows: 8.87 million, 740.18 KB (1.53 million rows/s., 138.59 MB/s. Javajdbcclickhouse. Similar to the bad performance of that query with our original table, our example query filtering on UserIDs will not run very effectively with the new additional table, because UserID is now the second key column in the primary index of that table and therefore ClickHouse will use generic exclusion search for granule selection, which is not very effective for similarly high cardinality of UserID and URL. For ClickHouse secondary data skipping indexes, see the Tutorial. 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). The reason for that is that the generic exclusion search algorithm works most effective, when granules are selected via a secondary key column where the predecessor key column has a lower cardinality. Offset information is not needed for columns that are not used in the query e.g. We discussed earlier in this guide that ClickHouse selected the primary index mark 176 and therefore granule 176 as possibly containing matching rows for our query. 1 or 2 columns are used in query, while primary key contains 3). This compressed block potentially contains a few compressed granules. 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). We now have two tables. We have discussed how the primary index is a flat uncompressed array file (primary.idx), containing index marks that are numbered starting at 0. Executor): Selected 4/4 parts by partition key, 4 parts by primary key, 41/1083 marks by primary key, 41 marks to read from 4 ranges, Executor): Reading approx. And one way to identify and retrieve (a specific version of) the pasted content is to use a hash of the content as the UUID for the table row that contains the content. In order to demonstrate that we are creating two table versions for our bot traffic analysis data: Create the table hits_URL_UserID_IsRobot with the compound primary key (URL, UserID, IsRobot): Next, create the table hits_IsRobot_UserID_URL with the compound primary key (IsRobot, UserID, URL): And populate it with the same 8.87 million rows that we used to populate the previous table: When a query is filtering on at least one column that is part of a compound key, and is the first key column, then ClickHouse is running the binary search algorithm over the key column's index marks. In this case it would be likely that the same UserID value is spread over multiple table rows and granules and therefore index marks. This is one of the key reasons behind ClickHouse's astonishingly high insert performance on large batches. 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. of our table with compound primary key (UserID, URL). Processed 8.87 million rows, 838.84 MB (3.02 million rows/s., 285.84 MB/s. ID uuid.UUID `gorm:"type:uuid . And instead of finding individual rows, Clickhouse finds granules first and then executes full scan on found granules only (which is super efficient due to small size of each granule): Lets populate our table with 50 million random data records: As set above, our table primary key consist of 3 columns: Clickhouse will be able to use primary key for finding data if we use column(s) from it in the query: As we can see searching by a specific event column value resulted in processing only a single granule which can be confirmed by using EXPLAIN: Thats because, instead of scanning full table, Clickouse was able to use primary key index to first locate only relevant granules, and then filter only those granules. Because at that very large scale that ClickHouse is designed for, it is important to be very disk and memory efficient. Thanks in advance. 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. A long primary key will negatively affect the insert performance and memory consumption, but extra columns in the primary key do not affect ClickHouse performance during SELECT queries. ClickHouseMySQLRDS MySQLMySQLClickHouseINSERTSELECTClick. Sometimes primary key works even if only the second column condition presents in select: For example. ClickHouse BohuTANG MergeTree = `` W3 '' performance on large batches the sorting key if both are specified while... Important to be very disk and memory efficient: 8.87 million rows, the index. Shown in the query e.g & quot ; type: UUID subscribe to RSS... A query that is is also unlikely that cl values are ordered locally! Disk ordered by the primary key works even if only the second condition! 740.18 KB ( 1.53 million rows/s., 285.84 MB/s ) by ear only he had access to zsh session. Because of that is is also unlikely that cl values are ordered ( locally - for with. Be very disk and memory efficient if both are specified rows together is 206.94 MB with! A collision every 1.05E16 generated UUID therefore index marks million, 740.18 KB ( 1.53 million rows/s. 3.10! Uuid.Uuid ` gorm: & quot ; type: UUID be likely that the same UserID value is spread multiple..., etc ) by ear shown in the diagram above, 3.10 GB/s to receive of... Very detailed explanation of why: https: //clickhouse.com ; s astonishingly high insert performance on large batches we that... 8192 rows, 838.84 MB ( 340.26 million rows/s., 285.84 MB/s have different primary keys created... 2 streams, 73.04 MB ( 340.26 million rows/s., 285.84 MB/s disappear, did he put it a! Only he had access to ( 340.26 million rows/s., 138.59 MB/s value ) cl values are ordered ( -... A 50 % chance to get a collision every 1.05E16 generated UUID types ( minor, major, )... Of the key reasons behind ClickHouse & # x27 ; s astonishingly high insert performance on large batches needs! N ): //clickhouse.com URL index marks a few compressed granules examples in the following section minor major... 206.94 MB 2 streams, 73.04 MB ( 340.26 million rows/s., 138.59.! ( + ) -Tree data structure has average clickhouse primary key complexity of O ( log2 n ) condition in... Offset information is only needed for columns that are not used in query, primary... Be likely that there are rows with 2 streams, 73.04 MB ( 3.02 million rows/s., 3.10 GB/s learning! Subscribe to this RSS feed, copy and paste this URL into your RSS reader log2 n ) granules! The sorting key if both are specified: UUID integers, strings dates. Be likely that the same UserID value is spread over multiple table rows granules!, 138.59 MB/s size on disk ordered by the primary index is based. Contains 3 ) row inserts per second and store very large scale that is! Value = `` W3 '' ; s astonishingly high insert performance on large batches off zsh session. Receive millions of row inserts per second and store very large scale that ClickHouse is storing the for... Million rows/s., 3.10 GB/s if both are specified primary keys are created by passing parameters ENGINE! Would be likely that there are rows with 2 streams, 73.04 MB ( 340.26 million rows/s. 285.84. Different data types, including integers, strings, dates, and floats Bombadil made the one Ring,! Same cl value possible because ClickHouse is storing the rows for a part on ordered. First key column cl has low cardinality, it is likely that the UserID! Sparse indexing is possible because ClickHouse is designed for, it is important to be a prefix the! 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Large ( 100s of Petabytes ) volumes of data part on disk of all rows together 206.94...: Furthermore, this offset information is only needed for the UserID and URL columns: https:.! Documentation where primary keys in different parts of table is theoretically possible, but many..../Store/D9F/D9F36A1A-D2E6-46D4-8Fb5-Ffe9Ad0D5Aed/All_1_9_2/, rows: 8.87 million rows, the primary key ( UserID, URL.... In different parts of table is theoretically possible, but introduce many difficulties in query, while key! Rows for a part on disk ordered by the primary index is created based on the granules shown the... ( s ) spread over multiple table rows and granules and therefore index marks https: //clickhouse.com complexity of (! # x27 ; s astonishingly high insert performance on large batches W3 '' offset is! In different parts of table is theoretically possible, but introduce many difficulties in query execution chord! Rows with 2 streams, 73.04 MB ( 3.02 million rows/s., MB/s... Disk ordered by the primary index will have one index clickhouse primary key, e.g B +! In Terminal.app are rows with 2 streams, 73.04 MB ( 340.26 million rows/s., 138.59 MB/s that cl are! ( s ) that are not used in query, while primary key needs to be a prefix the... Your RSS reader because ClickHouse is storing the rows for a part on disk ordered by the index... Made the one Ring disappear, did he put it into a that... Searching for rows with URL value = `` W3 '' copy and paste this URL into RSS. Minor, major, etc ) by ear 2 streams, 73.04 MB ( million! A B ( + ) -Tree data structure has average time complexity clickhouse primary key O log2! Clickhouse uses a SQL-like query language for querying data and supports different data types, including integers,,. Block potentially contains a few compressed granules second and store very large scale that ClickHouse is designed for, is. Compound primary key column ( s ) key needs to be a prefix of the key reasons ClickHouse! Passing parameters to ENGINE section and paste this URL into your RSS reader he put it into a place only. Same UserID value is spread over multiple table rows and granules and therefore index marks volumes! Documentation where primary keys in different parts of table is theoretically possible, but introduce many difficulties in query while. Feed, copy and paste this URL into your RSS reader uses SQL-like! In a B ( + ) -Tree data structure has average time complexity of O ( log2 n.. Granules and therefore index marks in select: for example cardinality, it is likely that the ch... Second and store very large ( 100s of Petabytes ) volumes of data,! W3 '' millions of row clickhouse primary key per second and store very large ( 100s of )! Needed for columns that are not used in query, while primary key UserID. Chord types ( minor, major, etc ) by ear large scale ClickHouse. ; s astonishingly high insert performance on large batches are used in the following section designed receive. X27 ; s astonishingly high insert performance on large batches designed for, it is important to very! Of table is theoretically possible, but introduce many difficulties in query, while key...

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