Not the answer you're looking for? 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. In this case it makes sense to specify the sorting key that is different from the primary key. jangorecki added the feature label on Feb 25, 2020. 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. Rows with the same UserID value are then ordered by URL. To learn more, see our tips on writing great answers. When using ReplicatedMergeTree, there are also two additional parameters, identifying shard and replica. 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. The corresponding trace log in the ClickHouse server log file confirms that: ClickHouse selected only 39 index marks, instead of 1076 when generic exclusion search was used. Theorems in set theory that use computability theory tools, and vice versa. Note that primary key should be the same as or a prefix to sorting key (specified by ORDER BY expression). You could insert many rows with same value of primary key to a table. Existence of rational points on generalized Fermat quintics. https: . ALTER TABLE xxx MODIFY PRIMARY KEY (.) 3. The following illustrates in detail how ClickHouse is building and using its sparse primary index. Open the details box for specifics. The primary key needs to be a prefix of the sorting key if both are specified. 1. A comparison between the performance of queries on MVs on ClickHouse vs. the same queries on time-series specific databases. For installation of ClickHouse and getting started instructions, see the Quick Start. 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. Later on in the article, we will discuss some best practices for choosing, removing, and ordering the table columns that are used to build the index (primary key columns). Because effectively the hidden table (and it's primary index) created by the projection is identical to the secondary table that we created explicitly, the query is executed in the same effective way as with the explicitly created table. 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. And because of that is is also unlikely that cl values are ordered (locally - for rows with the same ch value). Executor): Key condition: (column 1 in ['http://public_search', Executor): Used generic exclusion search over index for part all_1_9_2, 1076/1083 marks by primary key, 1076 marks to read from 5 ranges, Executor): Reading approx. This means that for each group of 8192 rows, the primary index will have one index entry, 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. if the combined row data size for n rows is less than 10 MB but n is 8192. . We discussed that because a ClickHouse table's row data is stored on disk ordered by primary key column(s), having a very high cardinality column (like a UUID column) in a primary key or in a compound primary key before columns with lower cardinality is detrimental for the compression ratio of other table columns. `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. We can now execute our queries with support from the primary index. 4ClickHouse . 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 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. Note that the query is syntactically targeting the source table of the projection. 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. 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. In ClickHouse the physical locations of all granules for our table are stored in mark files. And that is very good for the compression ratio of the content column, as a compression algorithm in general benefits from data locality (the more similar the data is the better the compression ratio is). We marked some column values from our primary key columns (UserID, URL) in orange. We mentioned in the beginning of this guide in the "DDL Statement Details", that we disabled adaptive index granularity (in order to simplify the discussions in this guide, as well as make the diagrams and results reproducible). Allowing to have different primary keys in different parts of table is theoretically possible, but introduce many difficulties in query execution. 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 ClickHouse is a column-oriented database management system. When I want to use ClickHouse mergetree engine I cannot do is as simply because it requires me to specify a primary key. ), 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. If the file is larger than the available free memory space then ClickHouse will raise an error. Specifically for the example table: UserID index marks: Update/Delete Data Considerations: Distributed table don't support the update/delete statements, if you want to use the update/delete statements, please be sure to write records to local table or set use-local to true. Mark 176 was identified (the 'found left boundary mark' is inclusive, the 'found right boundary mark' is exclusive), and therefore all 8192 rows from granule 176 (which starts at row 1.441.792 - we will see that later on in this guide) are then streamed into ClickHouse in order to find the actual rows with a UserID column value of 749927693. For our data set this would result in the primary index - often a B(+)-Tree data structure - containing 8.87 million entries. Therefore all granules (except the last one) of our example table have the same size. ClickHouse is an open-source column-oriented DBMS (columnar database management system) for online analytical processing (OLAP) that allows users to generate analytical reports using SQL queries in real-time. Processed 8.87 million rows, 18.40 GB (59.38 thousand rows/s., 123.16 MB/s. And because the first key column cl has low cardinality, it is likely that there are rows with the same cl value. MergeTreePRIMARY KEYprimary.idx. Sometimes primary key works even if only the second column condition presents in select: 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. 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. This ultimately prevents ClickHouse from making assumptions about the maximum URL value in granule 0. 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. ), 31.67 MB (306.90 million rows/s., 1.23 GB/s. 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? 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 (author_id, photo_id), what if we need to query with photo_id alone? 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. tokenbf_v1ngrambf_v1String . 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. 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. ClickHouseClickHouse. Feel free to skip this if you don't care about the time fields, and embed the ID field directly. Throughout this guide we will use a sample anonymized web traffic data set. As discussed above, ClickHouse is using its sparse primary index for quickly (via binary search) selecting granules that could possibly contain rows that match a query. The diagram above shows that mark 176 is the first index entry where both the minimum UserID value of the associated granule 176 is smaller than 749.927.693, and the minimum UserID value of granule 177 for the next mark (mark 177) is greater than this value. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. 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. 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. To make this (way) more efficient and (much) faster, we need to use a table with a appropriate primary key. The higher the cardinality difference between the key columns is, the more the order of those columns in the key matters. The last granule (granule 1082) "contains" less than 8192 rows. 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. KeyClickHouse. Why this is necessary for this example will become apparent. primary keysampling key ENGINE primary keyEnum DateTime UInt32 Similarly, a mark file is also a flat uncompressed array file (*.mrk) containing marks that are numbered starting at 0. How to declare two foreign keys as primary keys in an entity. These orange-marked column values are the primary key column values of each first row of each granule. days of the week) at which a user clicks on a specific URL?, specifies a compound sorting key for the table via an `ORDER BY` clause. There is a fatal problem for the primary key index in ClickHouse. 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). The located groups of potentially matching rows (granules) are then in parallel streamed into the ClickHouse engine in order to find the matches. ClickHouse sorts data by primary key, so the higher the consistency, the better the compression. This can not be excluded because the directly succeeding index mark 1 does not have the same UserID value as the current mark 0. 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. This compressed block potentially contains a few compressed granules. ), 81.28 KB (6.61 million rows/s., 26.44 MB/s. It just defines sort order of data to process range queries in optimal way. ), 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. Therefore only the corresponding granule 176 for mark 176 can possibly contain rows with a UserID column value of 749.927.693. 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. ORDER BY PRIMARY KEY, ORDER BY . The primary key in the DDL statement above causes the creation of the primary index based on the two specified key columns. . ", What are the most popular times (e.g. If trace_logging is enabled then the ClickHouse server log file shows that ClickHouse used a generic exclusion search over the 1083 URL index marks in order to identify those granules that possibly can contain rows with a URL column value of "http://public_search": We can see in the sample trace log above, that 1076 (via the marks) out of 1083 granules were selected as possibly containing rows with a matching URL value. If in addition we want to keep the good performance of our sample query that filters for rows with a specific UserID then we need to use multiple primary indexes. Why does Paul interchange the armour in Ephesians 6 and 1 Thessalonians 5? If a people can travel space via artificial wormholes, would that necessitate the existence of time travel? Run this query in clickhouse client: We can see that there is a big difference between the cardinalities, especially between the URL and IsRobot columns, and therefore the order of these columns in a compound primary key is significant for both the efficient speed up of queries filtering on that columns and for achieving optimal compression ratios for the table's column data files. For example check benchmark and post of Mark Litwintschik. the EventTime. 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. 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. How can I test if a new package version will pass the metadata verification step without triggering a new package version? In order to see how a query is executed over our data set without a primary key, we create a table (with a MergeTree table engine) by executing the following SQL DDL statement: Next insert a subset of the hits data set into the table with the following SQL insert statement. The uncompressed data size is 8.87 million events and about 700 MB. ClickHouse stores data in LSM-like format (MergeTree Family) 1. We can also reproduce this by using the EXPLAIN clause in our example query: The client output is showing that one out of the 1083 granules was selected as possibly containing rows with a UserID column value of 749927693. In order to make the best choice here, lets figure out how Clickhouse primary keys work and how to choose them. The two respective granules are aligned and streamed into the ClickHouse engine for further processing i.e. The client output indicates that ClickHouse almost executed a full table scan despite the URL column being part of the compound primary key! Pick only columns that you plan to use in most of your queries. On a self-managed ClickHouse cluster we can use the file table function for inspecting the content of the primary index of our example table. 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). When the UserID has high cardinality then it is unlikely that the same UserID value is spread over multiple table rows and granules. 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). ClickHouse continues to crush time series, by Alexander Zaitsev. Url column being part of the sorting key that is is also unlikely that cl values are (... To choose them necessary for this example will become apparent triggering a new package version popular times ( e.g for... 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( locally - for rows with the same as or a prefix to sorting key if both specified! Engine for further processing i.e on MVs on ClickHouse vs. the same clickhouse primary key ). The compression not be excluded because the first key column cl has low cardinality it... And vice versa, 18.40 GB ( 59.38 thousand rows/s., 26.44 MB/s this block. Pick only columns that you plan to use in most of your.... ( locally - for rows with a UserID column value of 749.927.693 available free memory space ClickHouse... 26.44 MB/s 123.16 MB/s processed 8.87 million rows, the more the order of those columns in the columns!