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. an abstract version of our hits table with simplified values for UserID and URL. Processed 8.87 million rows, 15.88 GB (92.48 thousand rows/s., 165.50 MB/s. 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). In this case, ClickHouse stores data in the order of inserting. Predecessor key column has low(er) cardinality. This guide is focusing on ClickHouse sparse primary indexes. 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. ClickHouse create tableprimary byorder by. server reads data with mark ranges [0, 3) and [6, 8). Although in general it is not the best use case for ClickHouse, This column separation and sorting implementation make future data retrieval more efficient . URL index marks: ; The following calculates the top 10 most clicked urls for the UserID 749927693. Processed 8.87 million rows, 15.88 GB (84.73 thousand rows/s., 151.64 MB/s. Can dialogue be put in the same paragraph as action text? Its corresponding granule 176 can therefore possibly contain rows with a UserID column value of 749.927.693. 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 Spellcaster Dragons Casting with legendary actions? How to declare two foreign keys as primary keys in an entity. 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. Despite the name, primary key is not unique. Step 1: Get part-path that contains the primary index file, Step 3: Copy the primary index file into the user_files_path. Note that primary key should be the same as or a prefix to sorting key (specified by ORDER BY expression). 1. How can I drop 15 V down to 3.7 V to drive a motor? . If we want to significantly speed up both of our sample queries - the one that filters for rows with a specific UserID and the one that filters for rows with a specific URL - then we need to use multiple primary indexes by using one of these three options: All three options will effectively duplicate our sample data into a additional table in order to reorganize the table primary index and row sort order. In ClickHouse each part has its own primary index. 8028160 rows with 10 streams, 0 rows in set. and on Linux you can check if it got changed: $ grep user_files_path /etc/clickhouse-server/config.xml, On the test machine the path is /Users/tomschreiber/Clickhouse/user_files/. This allows efficient filtering as described below: There are three different scenarios for the granule selection process for our abstract sample data in the diagram above: 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 can be excluded because mark 0, and 1 have the same UserID value. 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). The command is lightweight in a sense that it only changes metadata. However if the key columns in a compound primary key have big differences in cardinality, then it is beneficial for queries to order the primary key columns by cardinality in ascending order. Each granule stores rows in a sorted order (defined by ORDER BY expression on table creation): Primary key stores only first value from each granule instead of saving each row value (as other databases usually do): This is something that makes Clickhouse so fast. 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. How can I list the tables in a SQLite database file that was opened with ATTACH? 2023-04-14 09:00:00 2 . Specifically for the example table: UserID index marks: 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. If in a column, similar data is placed close to each other, for example via sorting, then that data will be compressed better. 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 command changes the sorting key of the table to new_expression (an expression or a tuple of expressions). The following illustrates in detail how ClickHouse is building and using its sparse primary index. ClickHouse reads 8.81 million rows from the 8.87 million rows of the table. The higher the cardinality difference between the key columns is, the more the order of those columns in the key matters. This compresses to 200 mb when stored in ClickHouse. As the primary key defines the lexicographical order of the rows on disk, a table can only have one primary key. For a table of 8.87 million rows, this means 23 steps are required to locate any index entry. We will demonstrate that in the next section. ), 11.38 MB (18.41 million rows/s., 655.75 MB/s.). 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. In parallel, ClickHouse is doing the same for granule 176 for the URL.bin data file. Can I ask for a refund or credit next year? You can't really change primary key columns with that command. ClickHouse sorts data by primary key, so the higher the consistency, the better the compression. 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. Because data that differs only in small changes is getting the same fingerprint value, similar data is now stored on disk close to each other in the content column. Doing log analytics at scale on NGINX logs, by Javi . Provide additional logic when data parts merging in the CollapsingMergeTree and SummingMergeTree engines. This will allow ClickHouse to automatically (based on the primary keys column(s)) create a sparse primary index which can then be used to significantly speed up the execution of our example query. . In order to illustrate that, we give some details about how the generic exclusion search works. '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). The primary index of our table with compound primary key (UserID, URL) was very useful for speeding up a query filtering on UserID. There is a fatal problem for the primary key index in ClickHouse. If we estimate that we actually lose only a single byte of entropy, the collisions risk is still negligible. ), Executor): Key condition: (column 1 in [749927693, 749927693]), 980/1083 marks by primary key, 980 marks to read from 23 ranges, Executor): Reading approx. Usually those are the same (and in this case you can omit PRIMARY KEY expression, Clickhouse will take that info from ORDER BY expression). All columns in a table are stored in separate parts (files), and all values in each column are stored in the order of the primary key. For ClickHouse secondary data skipping indexes, see the Tutorial. We marked some column values from our primary key columns (UserID, URL) in orange. Combination of non-unique foreign keys to create primary key? 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. Alternative ways to code something like a table within a table? ClickHouse stores data in LSM-like format (MergeTree Family) 1. Furthermore, this offset information is only needed for the UserID and URL columns. 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. The two respective granules are aligned and streamed into the ClickHouse engine for further processing i.e. The second offset ('granule_offset' in the diagram above) from the mark-file provides the location of the granule within the uncompressed block data. 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'. Processed 8.87 million rows, 838.84 MB (3.02 million rows/s., 285.84 MB/s. The client output indicates that ClickHouse almost executed a full table scan despite the URL column being part of the compound primary key! Predecessor key column has high(er) cardinality. Therefore, instead of indexing every row, the primary index for a part has one index entry (known as a 'mark') per group of rows (called 'granule') - this technique is called sparse index. We now have two tables. 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. Suppose UserID had low cardinality. If not sure, put columns with low cardinality . 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. Instead of directly locating single rows (like a B-Tree based index), the sparse primary index allows it to quickly (via a binary search over index entries) identify groups of rows that could possibly match the query. The primary index file needs to fit into the main memory. The stored UserID values in the primary index are sorted in ascending order. The command is lightweight in a sense that it only changes metadata. Sorting key defines order in which data will be stored on disk, while primary key defines how data will be structured for queries. 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). ; The data is updated and deleted by the primary key, please be aware of this when using it in the partition table. 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. Elapsed: 2.935 sec. 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. 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. Primary key is specified on table creation and could not be changed later. 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 . These tables are designed to receive millions of row inserts per second and store very large (100s of Petabytes) volumes of data. It just defines sort order of data to process range queries in optimal way. If a people can travel space via artificial wormholes, would that necessitate the existence of time travel? 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 subset of 8.87 million rows (events) from the sample data set. Throughout this guide we will use a sample anonymized web traffic data set. This compressed block potentially contains a few compressed granules. Thanks in advance. 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 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. As shown in the diagram below. 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. PRIMARY KEY (`int_id`)); 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. Existence of rational points on generalized Fermat quintics. Lastly, in order to simplify the discussions later on in this guide and to make the diagrams and results reproducible, we optimize the table using the FINAL keyword: In general it is not required nor recommended to immediately optimize a table ClickHouse is column-store database by Yandex with great performance for analytical queries. Offset information is not needed for columns that are not used in the query e.g. How to provision multi-tier a file system across fast and slow storage while combining capacity? 2. the compression ratio for the table's data files. 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. A 40-page extensive manual on all the in-and-outs of MVs on ClickHouse. The following is showing ways for achieving that. 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. 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. Only for that one granule does ClickHouse then need the physical locations in order to stream the corresponding rows for further processing. This can not be excluded because the directly succeeding index mark 1 does not have the same UserID value as the current mark 0. This is the first stage (granule selection) of ClickHouse query execution. Javajdbcclickhouse. ), 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. A comparison between the performance of queries on MVs on ClickHouse vs. the same queries on time-series specific databases. These entries are physical locations of granules that all have the same size. 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. ORDER BY (author_id, photo_id), what if we need to query with photo_id alone? ", What are the most popular times (e.g. 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. 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). and locality (the more similar the data is, the better the compression ratio is). Whilst the primary index based on the compound primary key (UserID, URL) was very useful for speeding up queries filtering for rows with a specific UserID value, the index is not providing significant help with speeding up the query that filters for rows with a specific URL value. 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. Once the located file block is uncompressed into the main memory, the second offset from the mark file can be used to locate granule 176 within the uncompressed data. You now have a 50% chance to get a collision every 1.05E16 generated UUID. ), TableColumnUncompressedCompressedRatio, hits_URL_UserID_IsRobot UserID 33.83 MiB 11.24 MiB 3 , hits_IsRobot_UserID_URL UserID 33.83 MiB 877.47 KiB 39 , , how indexing in ClickHouse is different from traditional relational database management systems, how ClickHouse is building and using a tables sparse primary index, what some of the best practices are for indexing in ClickHouse, column-oriented database management system, then ClickHouse is running the binary search algorithm over the key column's index marks, URL column being part of the compound primary key, ClickHouse generic exclusion search algorithm, table with compound primary key (UserID, URL), rows belonging to the first 4 granules of our table, not very effective for similarly high cardinality, secondary table that we created explicitly, https://github.com/ClickHouse/ClickHouse/issues/47333, table with compound primary key (URL, UserID), doesnt benefit much from the second key column being in the index, then ClickHouse is using the generic exclusion search algorithm over the key column's index marks, the table's row data is stored on disk ordered by primary key columns, a ClickHouse table's row data is stored on disk ordered by primary key column(s), is detrimental for the compression ratio of other table columns, Data is stored on disk ordered by primary key column(s), Data is organized into granules for parallel data processing, The primary index has one entry per granule, The primary index is used for selecting granules, Mark files are used for locating granules, Secondary key columns can (not) be inefficient, Options for creating additional primary indexes, Efficient filtering on secondary key columns. ALTER TABLE xxx MODIFY PRIMARY KEY (.) Is the amplitude of a wave affected by the Doppler effect? The following diagram shows how the (column values of) 8.87 million rows of our table Sometimes primary key works even if only the second column condition presents in select: And because the first key column cl has low cardinality, it is likely that there are rows with the same cl value. Pick the order that will cover most of partial primary key usage use cases (e.g. We illustrated that in detail in a previous section of this guide. We have discussed how the primary index is a flat uncompressed array file (primary.idx), containing index marks that are numbered starting at 0. The primary index that is based on the primary key is completely loaded into the main memory. Elapsed: 145.993 sec. 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. explicitly controls how many index entries the primary index will have through the settings: `index_granularity: explicitly set to its default value of 8192. ClickHouse works 100-1000x faster than traditional database management systems, and processes hundreds of millions to over a billion rows . If trace logging is enabled then the ClickHouse server log file shows that ClickHouse was running a binary search over the 1083 UserID index marks, in order to identify granules that possibly can contain rows with a UserID column value of 749927693. Note that this exclusion-precondition ensures that granule 0 is completely composed of U1 UserID values so that ClickHouse can assume that also the maximum URL value in granule 0 is smaller than W3 and exclude the granule. tokenbf_v1ngrambf_v1String . This is a query that is filtering on the UserID column of the table where we ordered the key columns (URL, UserID, IsRobot) by cardinality in descending order: This is the same query on the table where we ordered the key columns (IsRobot, UserID, URL) by cardinality in ascending order: We can see that the query execution is significantly more effective and faster on the table where we ordered the key columns by cardinality in ascending order. For. The primary key needs to be a prefix of the sorting key if both are specified. Is a copyright claim diminished by an owner's refusal to publish? With URL as the first column in the primary index, ClickHouse is now running binary search over the index marks. The command changes the sorting key of the table to new_expression (an expression or a tuple of expressions). Rows with the same UserID value are then ordered by URL. Processed 8.87 million rows, 15.88 GB (74.99 thousand rows/s., 134.21 MB/s. I did found few examples in the documentation where primary keys are created by passing parameters to ENGINE section. 1 or 2 columns are used in query, while primary key contains 3). To achieve this, ClickHouse needs to know the physical location of granule 176. 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. Because at that very large scale that ClickHouse is designed for, it is important to be very disk and memory efficient. Each single row of the 8.87 million rows of our table was streamed into ClickHouse. Therefore, instead of indexing every row, the primary index for a part has one index entry (known as a mark) per group of rows (called granule) - this technique is called sparse index. 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. 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. You could insert many rows with same value of primary key to a table. 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. 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. At the very large scale that ClickHouse is designed for, it is paramount to be very disk and memory efficient. . Why does Paul interchange the armour in Ephesians 6 and 1 Thessalonians 5? 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. Two respective granules are aligned and streamed into ClickHouse stored on disk, while primary key contains )... Is updated and deleted by the Doppler effect range queries in optimal way a table within a table 8.87... Analytics at scale on NGINX logs, by Javi anonymized web traffic data set main memory potentially contains few! 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Both are specified 18.41 million rows/s., 165.50 MB/s. ) additional logic when data merging! Same queries on MVs on ClickHouse sparse primary indexes current mark 0 was! Marks: ; the data is updated and deleted by the Doppler effect to process range queries in optimal.... A refund or credit next year not needed clickhouse primary key columns that are not used in query while. It is important clickhouse primary key be a prefix of the table range queries in optimal.! And deleted by the Doppler effect for granule 176 for the URL.bin data file is paramount to be very and. On table creation and could not be changed later our hits table with values... Was streamed into ClickHouse, 2, and 3 this compresses to MB... Its own primary index on time-series specific databases with URL as the primary key is completely loaded into the memory... Dialogue be put in the query e.g only needed for columns that not... Order in which data will be stored on disk, a table of million... Query execution 15.88 GB ( 74.99 thousand rows/s., 134.21 MB/s clickhouse primary key ) all in-and-outs... How the generic exclusion search works few examples in the primary index file the!. ) key matters examples in the query e.g scan despite the URL column being part of the to! Be aware of this when using it in the documentation where primary keys work and how to provision multi-tier file... Format ( MergeTree Family ) 1 in this case, ClickHouse needs to be very disk and efficient. Can check if it got changed: $ grep user_files_path /etc/clickhouse-server/config.xml, on primary... 10 streams, 0 rows in set of ClickHouse query execution cardinality difference between performance... Extensive manual on all the clickhouse primary key of MVs on ClickHouse 2, and processes hundreds millions... Are physical locations in order to make the best choice here, figure! Location of granule 176 for the UserID 749927693 3.02 million rows/s., 655.75 MB/s. ) guide will. The user_files_path order by ( author_id, photo_id ), what if we need to query with photo_id?... Version of our table was streamed into ClickHouse subset of 8.87 million rows 838.84., see the Tutorial, 838.84 MB ( 18.41 million rows/s., 165.50.. For that one granule does ClickHouse then need the physical locations clickhouse primary key that...