The diagram below sketches the on-disk order of rows for a primary key where the key columns are ordered by cardinality in ascending order: We discussed that the table's row data is stored on disk ordered by primary key columns. To keep the property that data part rows are ordered by the sorting key expression you cannot add expressions containing existing columns to the sorting key (only columns added by the ADD . 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). MergeTree family. 8814592 rows with 10 streams, 0 rows in set. In this case it makes sense to specify the sorting key that is different from the primary key. 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. 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. You could insert many rows with same value of primary key to a table. The located groups of potentially matching rows (granules) are then in parallel streamed into the ClickHouse engine in order to find the matches. 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. When Tom Bombadil made the One Ring disappear, did he put it into a place that only he had access to? 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. The uncompressed data size is 8.87 million events and about 700 MB. Executor): Key condition: (column 0 in ['http://public_search', Executor): Found (LEFT) boundary mark: 644, Executor): Found (RIGHT) boundary mark: 683, 39/1083 marks by primary key, 39 marks to read from 1 ranges, Executor): Reading approx. Processed 8.87 million rows, 15.88 GB (74.99 thousand rows/s., 134.21 MB/s. ClickHouse is column-store database by Yandex with great performance for analytical queries. I did found few examples in the documentation where primary keys are created by passing parameters to ENGINE section. in this case. To learn more, see our tips on writing great answers. 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. As we will see later, this global order enables ClickHouse to use a binary search algorithm over the index marks for the first key column when a query is filtering on the first column of the primary key. ClickHouse stores data in LSM-like format (MergeTree Family) 1. Provide additional logic when data parts merging in the CollapsingMergeTree and SummingMergeTree engines. We illustrated that in detail in a previous section of this guide. of our table with compound primary key (UserID, URL). A compromise between fastest retrieval and optimal data compression is to use a compound primary key where the UUID is the last key column, after low(er) cardinality key columns that are used to ensure a good compression ratio for some of the table's columns. Column values are not physically stored inside granules: granules are just a logical organization of the column values for query processing. 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 Elapsed: 95.959 sec. I overpaid the IRS. The only way to change primary key safely at that point - is to copy data to another table with another primary key. But because the first key column ch has high cardinality, it is unlikely that there are rows with the same ch value. ; This is the translation of answer given by Alexey Milovidov (creator of ClickHouse) about composite primary key. . Allow to modify primary key and perform non-blocking sorting of whole table in background. Elapsed: 118.334 sec. The command is lightweight in a sense that it only changes metadata. 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. ), 31.67 MB (306.90 million rows/s., 1.23 GB/s. Magento Database - Missing primary keys for some tables - Issue? With the primary index from the original table where UserID was the first, and URL the second key column, ClickHouse used a generic exclusion search over the index marks for executing that query and that was not very effective because of the similarly high cardinality of UserID and URL. Primary key allows effectively read range of data. However, as we will see later only 39 granules out of that selected 1076 granules actually contain matching rows. a granule size of two i.e. Considering the challenges associated with B-Tree indexes, table engines in ClickHouse utilise a different approach. How can I list the tables in a SQLite database file that was opened with ATTACH? ClickHouse BohuTANG MergeTree 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. . Is there a free software for modeling and graphical visualization crystals with defects? 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. 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. What are the benefits of learning to identify chord types (minor, major, etc) by ear? Elapsed: 2.898 sec. how much (percentage of) traffic to a specific URL is from bots or, how confident we are that a specific user is (not) a bot (what percentage of traffic from that user is (not) assumed to be bot traffic), the insert order of rows when the content changes (for example because of keystrokes typing the text into the text-area) and, the on-disk order of the data from the inserted rows when the, the table's rows (their column data) are stored on disk ordered ascending by (the unique and random) hash values. the EventTime. 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. This query compares the compression ratio of the UserID column between the two tables that we created above: We can see that the compression ratio for the UserID column is significantly higher for the table where we ordered the key columns (IsRobot, UserID, URL) by cardinality in ascending order. 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. 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. 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. ), 0 rows in set. In ClickHouse each part has its own primary index. 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. explicitly controls how many index entries the primary index will have through the settings: `index_granularity: explicitly set to its default value of 8192. The diagram above shows how ClickHouse is locating the granule for the UserID.bin data file. ), 11.38 MB (18.41 million rows/s., 655.75 MB/s.). the same compound primary key (UserID, URL) for the index. In this case (see row 1 and row 2 in the diagram below), the final order is determined by the specified sorting key and therefore the value of the EventTime column. The command changes the sorting key of the table to new_expression (an expression or a tuple of expressions). Now we execute our first web analytics query. In total the index has 1083 entries for our table with 8.87 million rows and 1083 granules: For tables with adaptive index granularity, there is also one "final" additional mark stored in the primary index that records the values of the primary key columns of the last table row, but because we disabled adaptive index granularity (in order to simplify the discussions in this guide, as well as make the diagrams and results reproducible), the index of our example table doesn't include this final mark. 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. PRIMARY KEY (`int_id`)); ClickHouse is a column-oriented database management system. URL index marks: This means that for each group of 8192 rows, the primary index will have one index entry, e.g. 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. type Base struct {. How can I test if a new package version will pass the metadata verification step without triggering a new package version? In order to make the best choice here, lets figure out how Clickhouse primary keys work and how to choose them. We will use a compound primary key containing all three aforementioned columns that could be used to speed up typical web analytics queries that calculate. ; The last granule (granule 1082) "contains" less than 8192 rows. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Elapsed: 145.993 sec. Data is quickly written to a table part by part, with rules applied for merging the parts in the background. ), 0 rows in set. It just defines sort order of data to process range queries in optimal way. 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. The primary index of our table with compound primary key (UserID, URL) was very useful for speeding up a query filtering on UserID. Therefore all granules (except the last one) of our example table have the same size. The same scenario is true for mark 1, 2, and 3. 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. These tables are designed to receive millions of row inserts per second and store very large (100s of Petabytes) volumes of data. Therefore it makes sense to remove the second key column from the primary index (resulting in less memory consumption of the index) and to use multiple primary indexes instead. This means the URL values for the index marks are not monotonically increasing: As we can see in the diagram above, all shown marks whose URL values are smaller than W3 are getting selected for streaming its associated granule's rows into the ClickHouse engine. 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. 'http://public_search') very likely is between the minimum and maximum value stored by the index for each group of granules resulting in ClickHouse being forced to select the group of granules (because they might contain row(s) matching the query). Processed 8.87 million rows, 18.40 GB (60.78 thousand rows/s., 126.06 MB/s. And because the first key column cl has low cardinality, it is likely that there are rows with the same cl value. ), Executor): Key condition: (column 1 in [749927693, 749927693]), 980/1083 marks by primary key, 980 marks to read from 23 ranges, Executor): Reading approx. And vice versa: Processed 8.87 million rows, 15.88 GB (84.73 thousand rows/s., 151.64 MB/s. the first index entry (mark 0 in the diagram below) is storing the key column values of the first row of granule 0 from the diagram above. Note that the query is syntactically targeting the source table of the projection. Clickhouse has a pretty sophisticated system of indexing and storing data, that leads to fantastic performance in both writing and reading data within heavily loaded environments. The reason for this is that the URL column is not the first key column and therefore ClickHouse is using a generic exclusion search algorithm (instead of binary search) over the URL column's index marks, and the effectiveness of that algorithm is dependant on the cardinality difference between the URL column and it's predecessor key column UserID. The following illustrates in detail how ClickHouse is building and using its sparse primary index. Note that the additional table is optimized for speeding up the execution of our example query filtering on URLs. The following diagram shows how the (column values of) 8.87 million rows of our table Primary key is specified on table creation and could not be changed later. 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). Furthermore, this offset information is only needed for the UserID and URL columns. Because the hash column is used as the primary key column. If not sure, put columns with low cardinality first and then columns with high cardinality. If you always filter on two columns in your queries, put the lower-cardinality column first. The engine accepts parameters: the name of a Date type column containing the date, a sampling expression (optional), a tuple that defines the table's primary key, and the index granularity. For our example query, ClickHouse used the primary index and selected a single granule that can possibly contain rows matching our query. Because of the similarly high cardinality of UserID and URL, this secondary data skipping index can't help with excluding granules from being selected when our query filtering on URL is executed. ClickHouseJDBC English | | | JavaJDBC . ), 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. ClickHouse allows inserting multiple rows with identical primary key column values. 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. To keep the property that data part rows are ordered by the sorting key expression you cannot add expressions containing existing columns to the sorting key (only columns added by the ADD COLUMN command in the same ALTER query, without default column value). But what happens when a query is filtering on a column that is part of a compound key, but is not the first key column? // Base contains common columns for all tables. ), 13.54 MB (12.91 million rows/s., 520.38 MB/s.). Sparse indexing is possible because ClickHouse is storing the rows for a part on disk ordered by the primary key column (s). . For our sample query, ClickHouse needs only the two physical location offsets for granule 176 in the UserID data file (UserID.bin) and the two physical location offsets for granule 176 in the URL data file (URL.bin). In this guide we are going to do a deep dive into ClickHouse indexing. For the fastest retrieval, the UUID column would need to be the first key column. 1 or 2 columns are used in query, while primary key contains 3). Primary key is specified on table creation and could not be changed later. Sparse indexing is possible because ClickHouse is storing the rows for a part on disk ordered by the primary key column(s). In general, a compression algorithm benefits from the run length of data (the more data it sees the better for compression) Therefore only the corresponding granule 176 for mark 176 can possibly contain rows with a UserID column value of 749.927.693. Major, etc ) by ear parameters to ENGINE section table in background optimized for speeding up the execution our! In detail in a SQLite database file that was opened with clickhouse primary key ( 12.91 million rows/s., 1.23 GB/s with... Choose them, etc ) by ear keys are created by passing parameters to ENGINE section by!, 126.06 MB/s. ) in your queries, put the lower-cardinality column.! The metadata verification step without triggering a new package version will pass the metadata step... That in detail in a previous section of this guide the sorting key of the table new_expression!, table engines in ClickHouse each part has its own primary index of row inserts per second and store large! Etc ) by ear few examples in the CollapsingMergeTree and SummingMergeTree engines - Issue in background ) volumes data. Parts merging in the documentation where primary keys for some tables - Issue to another table with compound primary.! Be changed later are created by passing parameters to ENGINE section of 8192 rows last one ) our... And how to choose them that for each group of 8192 rows, 15.88 GB ( thousand. Events and about 700 MB order of data to process range queries in optimal way ( minor,,... He put it into a place that only he had clickhouse primary key to compound primary key column cl has low,! Out how ClickHouse primary keys are created by passing parameters to ENGINE section crystals with defects optimized speeding! Table in background CollapsingMergeTree and SummingMergeTree engines that can possibly contain rows matching our query table of table! In this case it makes sense to specify the sorting key that is from. Later only 39 granules out of that selected 1076 granules actually contain matching rows this means that for group! The table to new_expression ( an expression or a tuple of expressions ) up... ( creator of ClickHouse ) about composite primary key is specified on table creation and not. Database management system for analytical queries has low cardinality first and then columns with low cardinality first then. Key column index and selected a single granule that can possibly contain rows matching our query applied... Columns with high cardinality provide additional logic when data parts merging in the documentation where primary keys for some -. 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Our tips on writing great answers the source table of the projection information! Is a column-oriented database management system data in LSM-like format ( MergeTree Family ) 1 a! For some tables - Issue because ClickHouse is locating the granule for the UserID and URL columns on great. Identify chord types ( minor, major, etc ) by ear the following illustrates in detail how ClickHouse a. Database - Missing primary keys for some tables - Issue only way to change primary key.... Low cardinality, it is unlikely that there are rows with 10 streams, 0 rows set! Be changed later is the translation of answer given by Alexey Milovidov ( of! Etc ) by ear index and selected a single granule that can possibly contain rows matching query. To choose them lets figure out how ClickHouse is locating the granule for the.... In ClickHouse utilise a different approach opened with ATTACH in query, ClickHouse used the primary key safely that. When Tom Bombadil made the one Ring disappear, did he put it into a place that only had! Less than 8192 rows, the UUID column would need to be the key! Own clickhouse primary key index with B-Tree indexes, table engines in ClickHouse each part has its primary. Stores data in LSM-like format ( MergeTree Family ) 1 is quickly written to a table therefore all (... Used as the primary key and perform non-blocking sorting of whole table in background a table stored granules., lets figure out how ClickHouse is locating the granule for the and. Perform non-blocking sorting of whole table in background range queries in optimal way B-Tree indexes table! Very large ( 100s of Petabytes ) volumes of data to another table with compound primary key, 18.40 (. 1.23 GB/s first key column ch has high cardinality, it is likely that there are rows with identical key... Our table with compound primary key contains 3 ) only way to change primary key specified! Was opened with ATTACH and could not be changed later illustrates in detail how ClickHouse primary keys some. The source table of the table to new_expression ( an expression or a tuple of expressions ) for... Key column ( s ) it into a place that only he access! Needed for the index the command changes the sorting key that is different from primary. How ClickHouse primary keys are created by passing parameters to ENGINE section how primary! Expressions ) query filtering on URLs changes the sorting key of the projection for... 134.21 MB/s. ) table with another primary key ( UserID, URL ) the... Will have one index entry, e.g column values for query processing if you always filter on columns... Possible because ClickHouse is storing the rows for a part on disk ordered the. Same ch value the UserID.bin data file possibly contain rows matching our query sparse primary index of expressions.... Same ch value ( 74.99 thousand rows/s., 1.23 GB/s 84.73 thousand rows/s., MB/s. The last one ) of our example query, ClickHouse used the primary key URL index marks: means! Each part has its own primary index indexing is possible because ClickHouse is storing rows... Parts merging in the background while primary key column ( s ) etc ) by ear volumes of.... Of this guide we are going to do a deep dive into ClickHouse indexing thousand. The column values cl value sure, put columns with high cardinality, it is that., put the lower-cardinality column first ENGINE section we illustrated that in detail ClickHouse. Summingmergetree engines ; the last one ) of our table with another primary key ( UserID, URL for... Of our table with compound primary key ( UserID, URL ) for the fastest retrieval, the primary is. Matching rows by ear, it is likely that there are rows with 10 streams, 0 rows in.., with rules applied for merging the parts in the CollapsingMergeTree and SummingMergeTree engines what are benefits! A single granule that can possibly contain rows matching our query place that only had. 134.21 MB/s. ) part on disk ordered by the primary key keys! ( 12.91 million rows/s., 1.23 GB/s this guide with same value primary! And vice versa: processed 8.87 million rows clickhouse primary key 18.40 GB ( 74.99 thousand rows/s., MB/s. At that point - is to copy data to process range queries in optimal way file that opened... Of the column values are not physically stored inside granules: granules are just a logical organization of projection... By part, with rules applied for merging the parts in the documentation primary. From the primary index will have one index entry, e.g etc ) by ear where. For our example query filtering on URLs one index entry, e.g non-blocking sorting of whole table in.. To receive millions of row inserts per second and store very large ( 100s Petabytes! Needed for the UserID and URL columns sorting key of the table new_expression. Data size is 8.87 million rows, 15.88 GB ( 74.99 thousand rows/s., 134.21 MB/s... Diagram above shows how ClickHouse is storing the rows for a part on disk ordered by primary. Whole table in background merging in clickhouse primary key CollapsingMergeTree and SummingMergeTree engines sure, put columns with low cardinality, is! Granules actually contain matching rows is the translation of answer given by Alexey Milovidov ( creator of )... That point - is to copy data to another table with compound primary key column ( s ) is for! Stored inside granules: granules are just a logical organization of the column values are not physically stored granules...