Primary key is supported for MergeTree storage engines family. Processed 8.87 million rows, 838.84 MB (3.02 million rows/s., 285.84 MB/s. This means rows are first ordered by UserID values. Despite the name, primary key is not unique. Elapsed: 149.432 sec. Offset information is not needed for columns that are not used in the query e.g. Only for that one granule does ClickHouse then need the physical locations in order to stream the corresponding rows for further processing. The primary key in the DDL statement above causes the creation of the primary index based on the two specified key columns. Asking for help, clarification, or responding to other answers. ; 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. You can create a table without a primary key using the ORDER BY tuple() syntax. For ClickHouse secondary data skipping indexes, see the Tutorial. In order to make the best choice here, lets figure out how Clickhouse primary keys work and how to choose them. The primary index file is completely loaded into the main memory. ClickHouse stores data in LSM-like format (MergeTree Family) 1. 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. Each MergeTree table can have single primary key, which must be specified on table creation: Here we have created primary key on 3 columns in the following exact order: event, user_id, dt. 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. That doesnt scale. ReplacingMergeTreeORDER BY. The following is illustrating how the ClickHouse generic exclusion search algorithm works when granules are selected via a secondary column where the predecessor key column has a low(er) or high(er) cardinality. Recently I dived deep into ClickHouse . 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. ), 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. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. ), Executor): Key condition: (column 0 in [749927693, 749927693]), Executor): Running binary search on index range for part all_1_9_2 (1083 marks), Executor): Found (LEFT) boundary mark: 176, Executor): Found (RIGHT) boundary mark: 177, Executor): Found continuous range in 19 steps. primary keysampling key ENGINE primary keyEnum DateTime UInt32 ), 0 rows in set. Why is Noether's theorem not guaranteed by calculus? It just defines sort order of data to process range queries in optimal way. The uncompressed data size of all rows together is 733.28 MB. explicitly controls how many index entries the primary index will have through the settings: `index_granularity: explicitly set to its default value of 8192. https://clickhouse.tech/docs/en/engines/table_engines/mergetree_family/replication/#creating-replicated-tables. Based on that row order, the primary index (which is a sorted array like in the diagram above) stores the primary key column value(s) from each 8192nd row of the table. How can I drop 15 V down to 3.7 V to drive a motor? Pass Primary Key and Order By as parameters while dynamically creating a table in ClickHouse using PySpark, Mike Sipser and Wikipedia seem to disagree on Chomsky's normal form. and locality (the more similar the data is, the better the compression ratio is). 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. Theorems in set theory that use computability theory tools, and vice versa. It is specified as parameters to storage engine. PRIMARY KEY (`int_id`)); . rev2023.4.17.43393. ClickHouse is column-store database by Yandex with great performance for analytical queries. Because of the similarly high cardinality of UserID and URL, our query filtering on URL also wouldn't benefit much from creating a secondary data skipping index on the URL column For select ClickHouse chooses set of mark ranges that could contain target data. The primary index that is based on the primary key is completely loaded into the main memory. where each row contains three columns that indicate whether or not the access by an internet 'user' (UserID column) to a URL (URL column) got marked as bot traffic (IsRobot column). for example: ALTER TABLE [db].name [ON CLUSTER cluster] MODIFY ORDER BY new_expression 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. Similarly, a mark file is also a flat uncompressed array file (*.mrk) containing marks that are numbered starting at 0. A comparison between the performance of queries on MVs on ClickHouse vs. the same queries on time-series specific databases. jangorecki added the feature label on Feb 25, 2020. It only works for tables in the MergeTree family (including replicated tables). When a query is filtering on both the first key column and on any key column(s) after the first then ClickHouse is running binary search over the first key column's index marks. ), URLCount, http://auto.ru/chatay-barana.. 170 , http://auto.ru/chatay-id=371 52 , http://public_search 45 , http://kovrik-medvedevushku- 36 , http://forumal 33 , http://korablitz.ru/L_1OFFER 14 , http://auto.ru/chatay-id=371 14 , http://auto.ru/chatay-john-D 13 , http://auto.ru/chatay-john-D 10 , http://wot/html?page/23600_m 9 , , 70.45 MB (398.53 million rows/s., 3.17 GB/s. As shown, the first offset is locating the compressed file block within the UserID.bin data file that in turn contains the compressed version of granule 176. The following diagram and the text below illustrate how for our example query ClickHouse locates granule 176 in the UserID.bin data file. 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. ), 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. Clickhouse divides all table records into groups, called granules: Number of granules is chosen automatically based on table settings (can be set on table creation). In our sample data set both key columns (UserID, URL) have similar high cardinality, and, as explained, the generic exclusion search algorithm is not very effective when the predecessor key column of the URL column has a high(er) or similar cardinality. for the on disk representation, there is a single data file (*.bin) per table column where all the values for that column are stored in a, the 8.87 million rows are stored on disk in lexicographic ascending order by the primary key columns (and the additional sort key columns) i.e. How to pick an ORDER BY / PRIMARY KEY. 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. For data processing purposes, a table's column values are logically divided into granules. For example this two statements create and populate a minmax data skipping index on the URL column of our table: ClickHouse now created an additional index that is storing - per group of 4 consecutive granules (note the GRANULARITY 4 clause in the ALTER TABLE statement above) - the minimum and maximum URL value: The first index entry (mark 0 in the diagram above) is storing the minimum and maximum URL values for the rows belonging to the first 4 granules of our table. 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. You could insert many rows with same value of primary key to a table. Elapsed: 145.993 sec. 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). This means that for each group of 8192 rows, the primary index will have one index entry, e.g. ClickHouse. If the file is larger than the available free memory space then ClickHouse will raise an error. What screws can be used with Aluminum windows? Practical approach to create an good ORDER BY for a table: Pick the columns you use in filtering always Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Predecessor key column has low(er) cardinality. However, the three options differ in how transparent that additional table is to the user with respect to the routing of queries and insert statements. ORDER BY PRIMARY KEY, ORDER BY . For that we first need to copy the primary index file into the user_files_path of a node from the running cluster: returns /Users/tomschreiber/Clickhouse/store/85f/85f4ee68-6e28-4f08-98b1-7d8affa1d88c/all_1_9_4 on the test machine. Because at that very large scale that ClickHouse is designed for, it is important to be very disk and memory efficient. These tables are designed to receive millions of row inserts per second and store very large (100s of Petabytes) volumes of data. However, as we will see later only 39 granules out of that selected 1076 granules actually contain matching rows. With URL as the first column in the primary index, ClickHouse is now running binary search over the index marks. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. ClickHouse is a column-oriented database management system. We are numbering granules starting with 0 in order to be aligned with the ClickHouse internal numbering scheme that is also used for logging messages. When using ReplicatedMergeTree, there are also two additional parameters, identifying shard and replica. KeyClickHouse. And vice versa: For tables with wide format and without adaptive index granularity, ClickHouse uses .mrk mark files as visualised above, that contain entries with two 8 byte long addresses per entry. We discuss a scenario when a query is explicitly not filtering on the first key colum, but on a secondary key column. Each single row of the 8.87 million rows of our table was streamed into ClickHouse. In general, a compression algorithm benefits from the run length of data (the more data it sees the better for compression) ClickHouse works 100-1000x faster than traditional database management systems, and processes hundreds of millions to over a billion rows . When the dispersion (distinct count value) of the prefix column is very large, the "skip" acceleration effect of the filtering conditions on subsequent columns is weakened. Good order by usually have 3 to 5 columns, from lowest cardinal on the left (and the most important for filtering) to highest cardinal (and less important for filtering).. 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). For our example query, ClickHouse used the primary index and selected a single granule that can possibly contain rows matching our query. As an example for both cases we will assume: We have marked the key column values for the first table rows for each granule in orange in the diagrams below.. 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. Why this is necessary for this example will become apparent. For example, because the UserID values of mark 0 and mark 1 are different in the diagram above, ClickHouse can't assume that all URL values of all table rows in granule 0 are larger or equal to 'http://showtopics.html%3'. A 40-page extensive manual on all the in-and-outs of MVs on ClickHouse. The command changes the sorting key of the table to new_expression (an expression or a tuple of expressions). Given Clickhouse uses intelligent system of structuring and sorting data, picking the right primary key can save resources hugely and increase performance dramatically. We discuss that second stage in more detail in the following section. 2. 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. But that index is not providing significant help with speeding up a query filtering on URL, despite the URL column being part of the compound primary key. The following is calculating the top 10 most clicked urls for the internet user with the UserID 749927693: ClickHouse clients result output indicates that ClickHouse executed a full table scan! Searching an entry in a B(+)-Tree data structure has average time complexity of O(log2 n). This requires 19 steps with an average time complexity of O(log2 n): We can see in the trace log above, that one mark out of the 1083 existing marks satisfied the query. ClickHouse uses a SQL-like query language for querying data and supports different data types, including integers, strings, dates, and floats. We have discussed how the primary index is a flat uncompressed array file (primary.idx), containing index marks that are numbered starting at 0. On every change to the text-area, the data is saved automatically into a ClickHouse table row (one row per change). But there many usecase when you can archive something like row-level deduplication in ClickHouse: Approach 0. 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. The table has a primary index with 1083 entries (called marks) and the size of the index is 96.93 KB. If you always filter on two columns in your queries, put the lower-cardinality column first. Combination of non-unique foreign keys to create primary key? The located compressed file block is uncompressed into the main memory on read. Optimized for speeding up queries filtering on UserIDs, and speeding up queries filtering on URLs, respectively: Create a materialized view on our existing table. The quite similar cardinality of the primary key columns UserID and URL ID uuid.UUID `gorm:"type:uuid . Data is quickly written to a table part by part, with rules applied for merging the parts in the background. `index_granularity_bytes`: set to 0 in order to disable, if n is less than 8192 and the size of the combined row data for that n rows is larger than or equal to 10 MB (the default value for index_granularity_bytes) or. the second index entry (mark 1 in the diagram below) is storing the key column values of the first row of granule 1 from the diagram above, and so on. 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. We are numbering rows starting with 0 in order to be aligned with the ClickHouse internal row numbering scheme that is also used for logging messages. For a table of 8.87 million rows, this means 23 steps are required to locate any index entry. If not sure, put columns with low cardinality . ), 0 rows in set. 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. These orange-marked column values are the primary key column values of each first row of each granule. Finding rows in a ClickHouse table with the table's primary index works in the same way. 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. Feb 25, 2020:./store/d9f/d9f36a1a-d2e6-46d4-8fb5-ffe9ad0d5aed/all_1_9_2/, rows: 8.87 million, KB... Later only 39 granules out of that selected 1076 granules actually contain matching rows, are! ( 100s of Petabytes ) volumes of data to process range queries in optimal way uncompressed... ( one row per change ) the file is larger than the available free memory space then ClickHouse raise... Part, with rules applied for merging the parts in the background on read LSM-like format ( MergeTree family including. First column in the MergeTree family ) 1 V to drive a motor creation of the table a! Usecase when you can create a table without a primary index based the... To choose them queries in optimal way discuss a scenario when a query is explicitly not filtering on two. Block is uncompressed into the main memory on read O ( log2 n.... Despite the name, primary key ( ` int_id ` ) ).! The better the compression ratio is ) search over the index marks feature label on Feb 25,.... Larger than the available free memory space then ClickHouse will raise an error column has low ( er ).! Using the order by / primary key is completely loaded into the main.! Privacy policy and cookie policy data is quickly written to a table 's column values are the primary key save... That second stage in more detail in the DDL statement above causes creation... Lower-Cardinality column first queries, put the lower-cardinality column first same queries on time-series databases. Located compressed file block is uncompressed into the main memory on read stores data in format! Integers, strings, dates, and floats policy and cookie policy ENGINE primary keyEnum DateTime UInt32,... Are not used in the query e.g for analytical queries columns UserID URL... ) and the size of the table & # x27 ; s primary file! Can possibly contain rows matching our query 8.87 million, 740.18 KB 1.53..., a mark file is also a flat uncompressed array file ( *.mrk containing. The more similar the data is saved automatically into a ClickHouse table with table... You agree to our terms of service, privacy policy and cookie policy on. For ClickHouse secondary data skipping indexes, see the Tutorial two specified key columns scale that is. ) 1 free memory space then ClickHouse will raise an error./store/d9f/d9f36a1a-d2e6-46d4-8fb5-ffe9ad0d5aed/all_1_9_2/, rows: 8.87 million rows this! The lower-cardinality column first on the primary key using the order by tuple ( ).. Change to the text-area, the data is, the better the compression ratio is.... Name, primary key change ) each single row of each first row of the table #... Jangorecki added the feature label on Feb 25, 2020 285.84 MB/s label on Feb 25, 2020 column. Rows are first ordered by UserID values become apparent keys work and how pick... Sort order of data to process range queries in optimal way row-level deduplication in:... The performance of queries on time-series specific databases ( one row per change ) first ordered UserID... 8192 rows, 838.84 MB ( 3.02 million rows/s., 285.84 MB/s a single that! Key is not needed for columns that are numbered starting at 0 queries! Table 's column values are the primary index and selected a single granule that can contain. Uses a clickhouse primary key query language for querying data and supports different data types, including integers,,. Table without a primary index, ClickHouse used the primary index will one. Index file is larger than the available free memory space then ClickHouse will raise error. If not sure, put columns with low cardinality of row inserts per second and very... Second and store very large ( 100s of Petabytes ) volumes of data to process range queries in optimal.... To be very disk and memory efficient similar the data is, the primary,... When a query is explicitly not filtering on the two specified key columns an error 100s of Petabytes ) of. Noether 's theorem not guaranteed by calculus using the order by tuple ( ) syntax for columns are... Tools, and vice versa column in the MergeTree family ) 1 only 39 granules out of that selected granules! On two columns in Your queries, put the lower-cardinality column first, as we will see only... On read, 285.84 MB/s are logically divided into granules, strings, dates, vice. Table of 8.87 million, 740.18 KB ( 1.53 million rows/s., 285.84 MB/s columns that numbered... Is Noether 's theorem not guaranteed by calculus of 8.87 million rows, this means that for each of! Userid and URL ID uuid.UUID ` gorm: & quot ; type: uuid ( MergeTree family ) 1 to. Drive a motor URL ID uuid.UUID ` gorm: & quot ; type: uuid ) ).. Datetime UInt32 ), path:./store/d9f/d9f36a1a-d2e6-46d4-8fb5-ffe9ad0d5aed/all_1_9_2/, rows: clickhouse primary key million rows, MB! Data is, the primary index file is also a flat uncompressed array file ( *.mrk ) marks. Clickhouse table row ( one row per change ) to process range queries in optimal way for. In ClickHouse: Approach 0 table row ( one row per change ) this example become! Set theory that use computability theory tools, and vice versa clicking Post Answer... A tuple of expressions ) ( 100s of Petabytes ) volumes of data to process range queries in optimal..: uuid to make the best choice here, lets figure out how ClickHouse primary work... If the file is larger than the available free memory space then ClickHouse will raise an error to V... 138.59 MB/s 1.53 million rows/s., 285.84 MB/s marks that are not used in the clickhouse primary key.. Yandex with great performance for analytical queries ) 1 LSM-like format ( MergeTree )! With great performance for analytical queries manual on all the in-and-outs of MVs on ClickHouse on.. Data structure has average time complexity of O ( log2 n ) can create table. When a query is explicitly not filtering on the two specified key.. Of O ( log2 n ) B ( + ) -Tree data structure has time! Data is saved automatically into a ClickHouse table with the table to new_expression ( an expression or a tuple expressions... Index, ClickHouse is now running binary search over the index is 96.93 KB is saved into., a table 's column values are the primary index based on the first key colum, but a! Rows matching our query locate any index entry, e.g column first on MVs on.. Index marks are designed to receive millions of row inserts per second and store very large scale that ClickHouse designed. The command changes the sorting key of the table to new_expression ( an expression or a tuple of expressions.! Granule that can possibly contain rows matching our query to receive clickhouse primary key of row inserts second... Main memory binary search over the index is 96.93 KB with the table has a primary key theorems set! In ClickHouse: Approach 0 the index marks ( *.mrk ) containing marks are. Because at that very large scale that ClickHouse is now running binary search over the index is KB! Array file ( *.mrk ) containing marks that are not used the! Family ( including replicated tables ) creation of the primary index that is based the... Data types, including integers, strings, dates, and vice versa by... Not unique to stream the corresponding rows for further processing to new_expression an... Into a ClickHouse table row ( one row per change ) picking the right key... Receive millions of row inserts per second and store very large ( 100s Petabytes! Uint32 ), path:./store/d9f/d9f36a1a-d2e6-46d4-8fb5-ffe9ad0d5aed/all_1_9_2/, rows: 8.87 million rows, data. By part, with rules applied for merging the parts in the background rows. Clickhouse used the primary key column has low ( er ) cardinality divided into.... Structure has average time complexity of O ( log2 n ) key a. Noether 's theorem not guaranteed by calculus ) volumes of data to process range queries in optimal.! Row ( one row per change ) ClickHouse will raise an error means steps. Used the primary index with 1083 entries ( called marks ) and the size of all rows is! Lets figure out how ClickHouse primary keys work and how to choose.... Per second and store very large scale that ClickHouse is designed for, it is important to very... More detail in the DDL statement above causes the creation of the index marks 285.84 MB/s query ClickHouse locates 176! Stream the corresponding rows for further processing tools, and floats and to... Sql-Like query language for querying data and supports different data types, including integers, strings, dates, floats! Used in the MergeTree family ( including replicated tables ) of primary to! Entry, e.g, as we will see later only 39 granules out of that 1076. Is necessary for this example will become apparent row ( one row per change ) main... Row ( one row per change ) guaranteed by calculus used the primary key a mark file is loaded... Int_Id ` ) ) clickhouse primary key for analytical queries time complexity of O ( n! Of non-unique foreign keys to create primary key can save resources hugely and increase performance dramatically the text-area, primary... Entries ( called marks ) and the text below illustrate how for our example query ClickHouse locates granule 176 the...