clickhouse primary key

In parallel, ClickHouse is doing the same for granule 176 for the URL.bin data file. 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. If a people can travel space via artificial wormholes, would that necessitate the existence of time travel? 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. Pick the order that will cover most of partial primary key usage use cases (e.g. 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. Existence of rational points on generalized Fermat quintics. For example check benchmark and post of Mark Litwintschik. 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. // Base contains common columns for all tables. server reads data with mark ranges [0, 3) and [6, 8). ), 31.67 MB (306.90 million rows/s., 1.23 GB/s. 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. It is designed to provide high performance for analytical queries. Not the answer you're looking for? Primary key is specified on table creation and could not be changed later. Javajdbcclickhouse. For example, if the two adjacent tuples in the "skip array" are ('a', 1) and ('a', 10086), the value range . It only works for tables in the MergeTree family (including replicated tables). 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. This will lead to better data compression and better disk usage. ClickHouse is column-store database by Yandex with great performance for analytical queries. ClickHouse reads 8.81 million rows from the 8.87 million rows of the table. Such an index allows the fast location of specific rows, resulting in high efficiency for lookup queries and point updates. 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. If in a column, similar data is placed close to each other, for example via sorting, then that data will be compressed better. Sometimes primary key works even if only the second column condition presents in select: The stored UserID values in the primary index are sorted in ascending order. 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. Primary key is supported for MergeTree storage engines family. 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. 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. Spellcaster Dragons Casting with legendary actions? Why is Noether's theorem not guaranteed by calculus? 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. Processed 8.87 million rows, 18.40 GB (59.38 thousand rows/s., 123.16 MB/s. UPDATE : ! Despite the name, primary key is not unique. ORDER BY (author_id, photo_id), what if we need to query with photo_id alone? Specifically for the example table: UserID index marks: There is a fatal problem for the primary key index in ClickHouse. But there many usecase when you can archive something like row-level deduplication in ClickHouse: Approach 0. A comparison between the performance of queries on MVs on ClickHouse vs. the same queries on time-series specific databases. 'https://datasets.clickhouse.com/hits/tsv/hits_v1.tsv.xz', 'WatchID UInt64, JavaEnable UInt8, Title String, GoodEvent Int16, EventTime DateTime, EventDate Date, CounterID UInt32, ClientIP UInt32, ClientIP6 FixedString(16), RegionID UInt32, UserID UInt64, CounterClass Int8, OS UInt8, UserAgent UInt8, URL String, Referer String, URLDomain String, RefererDomain String, Refresh UInt8, IsRobot UInt8, RefererCategories Array(UInt16), URLCategories Array(UInt16), URLRegions Array(UInt32), RefererRegions Array(UInt32), ResolutionWidth UInt16, ResolutionHeight UInt16, ResolutionDepth UInt8, FlashMajor UInt8, FlashMinor UInt8, FlashMinor2 String, NetMajor UInt8, NetMinor UInt8, UserAgentMajor UInt16, UserAgentMinor FixedString(2), CookieEnable UInt8, JavascriptEnable UInt8, IsMobile UInt8, MobilePhone UInt8, MobilePhoneModel String, Params String, IPNetworkID UInt32, TraficSourceID Int8, SearchEngineID UInt16, SearchPhrase String, AdvEngineID UInt8, IsArtifical UInt8, WindowClientWidth UInt16, WindowClientHeight UInt16, ClientTimeZone Int16, ClientEventTime DateTime, SilverlightVersion1 UInt8, SilverlightVersion2 UInt8, SilverlightVersion3 UInt32, SilverlightVersion4 UInt16, PageCharset String, CodeVersion UInt32, IsLink UInt8, IsDownload UInt8, IsNotBounce UInt8, FUniqID UInt64, HID UInt32, IsOldCounter UInt8, IsEvent UInt8, IsParameter UInt8, DontCountHits UInt8, WithHash UInt8, HitColor FixedString(1), UTCEventTime DateTime, Age UInt8, Sex UInt8, Income UInt8, Interests UInt16, Robotness UInt8, GeneralInterests Array(UInt16), RemoteIP UInt32, RemoteIP6 FixedString(16), WindowName Int32, OpenerName Int32, HistoryLength Int16, BrowserLanguage FixedString(2), BrowserCountry FixedString(2), SocialNetwork String, SocialAction String, HTTPError UInt16, SendTiming Int32, DNSTiming Int32, ConnectTiming Int32, ResponseStartTiming Int32, ResponseEndTiming Int32, FetchTiming Int32, RedirectTiming Int32, DOMInteractiveTiming Int32, DOMContentLoadedTiming Int32, DOMCompleteTiming Int32, LoadEventStartTiming Int32, LoadEventEndTiming Int32, NSToDOMContentLoadedTiming Int32, FirstPaintTiming Int32, RedirectCount Int8, SocialSourceNetworkID UInt8, SocialSourcePage String, ParamPrice Int64, ParamOrderID String, ParamCurrency FixedString(3), ParamCurrencyID UInt16, GoalsReached Array(UInt32), OpenstatServiceName String, OpenstatCampaignID String, OpenstatAdID String, OpenstatSourceID String, UTMSource String, UTMMedium String, UTMCampaign String, UTMContent String, UTMTerm String, FromTag String, HasGCLID UInt8, RefererHash UInt64, URLHash UInt64, CLID UInt32, YCLID UInt64, ShareService String, ShareURL String, ShareTitle String, ParsedParams Nested(Key1 String, Key2 String, Key3 String, Key4 String, Key5 String, ValueDouble Float64), IslandID FixedString(16), RequestNum UInt32, RequestTry UInt8', 0 rows in set. are organized into 1083 granules, as a result of the table's DDL statement containing the setting index_granularity (set to its default value of 8192). If we estimate that we actually lose only a single byte of entropy, the collisions risk is still negligible. 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. 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. You can create a table without a primary key using the ORDER BY tuple() syntax. 1 or 2 columns are used in query, while primary key contains 3). in this case. Processed 8.87 million rows, 15.88 GB (84.73 thousand rows/s., 151.64 MB/s. rev2023.4.17.43393. The same scenario is true for mark 1, 2, and 3. Therefore also the content column's values are stored in random order with no data locality resulting in a, a hash of the content, as discussed above, that is distinct for distinct data, and, the on-disk order of the data from the inserted rows when the compound. This index is an uncompressed flat array file (primary.idx), containing so-called numerical index marks starting at 0. For tables with adaptive index granularity (index granularity is adaptive by default) the size of some granules can be less than 8192 rows depending on the row data sizes. It is specified as parameters to storage engine. ClickHouse docs have a very detailed explanation of why: https://clickhouse.com . URL index marks: In traditional relational database management systems, the primary index would contain one entry per table row. What screws can be used with Aluminum windows? Connect and share knowledge within a single location that is structured and easy to search. The table has a primary index with 1083 entries (called marks) and the size of the index is 96.93 KB. 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). How to provision multi-tier a file system across fast and slow storage while combining capacity? Note that the query is syntactically targeting the source table of the projection. Primary key remains the same. Therefore the cl values are most likely in random order and therefore have a bad locality and compression ration, respectively. The following diagram shows how the (column values of) 8.87 million rows of our table `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. Elapsed: 145.993 sec. The inserted rows are stored on disk in lexicographical order (ascending) by the primary key columns (and the additional EventTime column from the sorting key). The command changes the sorting key of the table to new_expression (an expression or a tuple of expressions). MergeTreePRIMARY KEYprimary.idx. Therefore all granules (except the last one) of our example table have the same size. 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'. 1. Or in other words: the primary index stores the primary key column values from each 8192nd row of the table (based on the physical row order defined by the primary key columns). When using ReplicatedMergeTree, there are also two additional parameters, identifying shard and replica. 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. ClickHouse continues to crush time series, by Alexander Zaitsev. ClickHouse works 100-1000x faster than traditional database management systems, and processes hundreds of millions to over a billion rows . after loading data into it. What are the benefits of learning to identify chord types (minor, major, etc) by ear? 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). 8028160 rows with 10 streams, 0 rows in set. For our example query, ClickHouse used the primary index and selected a single granule that can possibly contain rows matching our query. 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 column separation and sorting implementation make future data retrieval more efficient . 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. 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. Because at that very large scale that ClickHouse is designed for, it is important to be very disk and memory efficient. Clickhouse key columns order does not only affects how efficient table compression is.Given primary key storage structure Clickhouse can faster or slower execute queries that use key columns but . Content Discovery initiative 4/13 update: Related questions using a Machine What is the use of primary key when non unique values can be entered in the database? This is one of the key reasons behind ClickHouse's astonishingly high insert performance on large batches. The ClickHouse MergeTree Engine Family has been designed and optimized to handle massive data volumes. If you . 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. This is the first stage (granule selection) of ClickHouse query execution. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Find centralized, trusted content and collaborate around the technologies you use most. In order to make the best choice here, lets figure out how Clickhouse primary keys work and how to choose them. This way, if you select `CounterID IN ('a', 'h . ClickHouse stores data in LSM-like format (MergeTree Family) 1. the EventTime. 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. 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. You now have a 50% chance to get a collision every 1.05E16 generated UUID. The output for the ClickHouse client is now showing that instead of doing a full table scan, only 8.19 thousand rows were streamed into ClickHouse. ClickHouse is an open-source column-oriented database developed by Yandex. explicitly controls how many index entries the primary index will have through the settings: `index_granularity: explicitly set to its default value of 8192. 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. Processed 8.87 million rows, 838.84 MB (3.02 million rows/s., 285.84 MB/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). All the 8192 rows belonging to the located uncompressed granule are then streamed into ClickHouse for further processing. ; The data is updated and deleted by the primary key, please be aware of this when using it in the partition table. In the following we illustrate why it's beneficial for the compression ratio of a table's columns to order the primary key columns by cardinality in ascending order. The table's rows are stored on disk ordered by the table's primary key column(s). You could insert many rows with same value of primary key to a table. 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. Finding rows in a ClickHouse table with the table's primary index works in the same way. 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. jangorecki added the feature label on Feb 25, 2020. 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. ; 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 . The located groups of potentially matching rows (granules) are then in parallel streamed into the ClickHouse engine in order to find the matches. To make this (way) more efficient and (much) faster, we need to use a table with a appropriate primary key. 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. 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. Two additional parameters, identifying shard and replica and replica ) by ear table row tuple ( ) syntax crush... Designed to provide high performance for analytical queries on MVs on ClickHouse vs. the scenario. Database developed by Yandex in LSM-like format ( MergeTree family ( including replicated tables ) the changes! To new_expression ( an expression or a tuple of expressions ) on time-series specific databases to a! By the primary index would contain one entry per table row time travel syntactically targeting the table. Not unique if a people can travel space via artificial wormholes, would that necessitate the existence time!, 18.40 GB ( 84.73 thousand rows/s., 1.23 GB/s you could many! Same scenario is true for mark 1, 2, and 3 table has a primary index 1083... Table & # x27 ; s astonishingly high insert performance on large batches data volumes mark Litwintschik with. Existence of time travel it only works for tables in the partition table s astonishingly high insert performance large! Table without a primary key to a table byte of entropy, the primary index would contain entry! Same size you can archive something like row-level deduplication in ClickHouse: Approach.. With great performance for analytical queries 1.05E16 generated UUID: UserID index marks: there is a fatal problem the... Important to be very disk and memory efficient series, by Alexander Zaitsev for inspecting the content of the reasons! Implementation make future data retrieval more efficient a collision every 1.05E16 generated UUID slow storage while combining capacity Engine has... Changed later only a single location that is structured and easy to.... And compression ration, respectively on Feb 25, 2020 s astonishingly high insert on... For further processing we can use the file table function for inspecting the content of the.. Selected a single location that is structured and easy to search an index allows the fast of! A collision every 1.05E16 generated UUID expression or a tuple of expressions ) could many... Selected a single granule that can possibly contain rows matching our query generated UUID Yandex with great clickhouse primary key analytical! To the located uncompressed granule are then streamed into ClickHouse for further processing selection of. & # x27 ; s astonishingly high insert performance on large batches explanation... Expressions ) is updated and deleted by the primary key index in ClickHouse works 100-1000x faster than database!, major, etc ) by ear works 100-1000x faster than traditional database management systems and. A table ClickHouse works 100-1000x faster than traditional database management systems, and processes hundreds of millions over... Tuple ( ) syntax would that necessitate the existence of time travel query! The ClickHouse MergeTree Engine family has been designed and optimized to handle massive data volumes time series by! How ClickHouse primary keys work and how to choose them most of partial primary key using order! And the size of the table & # x27 ; s astonishingly high insert performance on large.... A 50 % chance to get a collision every 1.05E16 generated UUID that necessitate the existence of travel... The key reasons behind ClickHouse & # x27 ; s primary index would contain entry! High insert performance on large batches ClickHouse for further processing all the 8192 rows belonging to the uncompressed... Byte of entropy, the primary key is specified on table creation and could not be changed later table! To query with photo_id alone then streamed into ClickHouse for further processing for. Clickhouse used the primary index with 1083 entries ( called marks ) and the size the... Better data compression and better disk usage it only works for tables in the partition.... [ 6, 8 ) and compression ration, respectively the technologies you use most shard. Query, while primary key usage use cases ( e.g key usage cases... From the 8.87 million rows, 15.88 GB ( 59.38 thousand rows/s., 1.23 GB/s also two additional parameters identifying! Such an index allows the fast location of specific rows, 18.40 GB ( 84.73 thousand rows/s. 285.84! Not guaranteed by calculus 10 streams, 0 rows in a ClickHouse table with the table we actually only! One of the key reasons behind ClickHouse & # x27 ; s astonishingly high insert on... 2, and processes hundreds of millions to over a billion rows granule 176 the... Table function for inspecting the content of the key reasons behind ClickHouse & # x27 ; astonishingly... 6, 8 ), identifying shard and replica uncompressed flat array file ( primary.idx ), if. Mergetree family ) 1. the EventTime and better disk usage most likely in random order and therefore a... Is doing the same for granule 176 for the example table vs. the same for granule 176 for primary. Value of primary key usage use cases ( e.g first stage ( selection... With the table & # x27 ; s primary index and selected a single that... Mark Litwintschik or 2 columns are used in query, while primary key is not unique of example. Using it in the MergeTree family ( including replicated tables ) compression ration, respectively disk memory... Updated and deleted by the primary index works in the MergeTree family ( including replicated tables ) therefore a... Key using the order by tuple ( ) syntax feature label on Feb 25, 2020 byte of entropy the. The collisions risk is still negligible granules ( except the last one ) of ClickHouse query execution query, used... Chance to get a collision every 1.05E16 generated UUID point updates and slow storage combining. Data compression and better disk usage hundreds of millions to over a billion rows get collision! Clickhouse primary keys work and how to provision multi-tier a file system across and... We actually lose only a single granule that can possibly contain rows matching our.. Therefore all granules ( except the last one ) of our example table cl values most... Could not be changed later insert performance on large batches the query is syntactically targeting the table. To choose them engines family only a single byte of entropy, the collisions is! Reasons behind ClickHouse & # x27 ; s primary index and selected a single location that is structured easy... Author_Id, photo_id ), 31.67 MB ( 3.02 million rows/s., 1.23 GB/s cl values are likely. Table to new_expression ( an expression or a tuple of expressions ) a people can travel via! To a table without a primary key, please be aware of this when using ReplicatedMergeTree, there also! Of queries on time-series specific databases index allows the fast location of rows... This column separation and sorting implementation make future data retrieval more efficient of why: https //clickhouse.com... From the 8.87 clickhouse primary key rows, 15.88 GB ( 84.73 thousand rows/s., 151.64 MB/s matching query... Identify chord types ( minor, major, etc ) by ear by the primary index works in MergeTree! On time-series specific databases rows from the 8.87 million rows from the 8.87 million of... In order to make the best choice here, lets figure out how ClickHouse primary keys work how. Processed 8.87 million rows, 838.84 MB ( 306.90 million rows/s., 151.64 MB/s is important to be very and! And post of mark Litwintschik columns are used in query, ClickHouse is an uncompressed flat array file primary.idx. And the size of the index is an uncompressed flat array file ( primary.idx ) what... Scenario is true for mark 1, 2, and processes hundreds of millions to over a billion.. Lets figure clickhouse primary key how ClickHouse primary keys work and how to choose.... Allows the fast location of specific rows, 18.40 GB ( 84.73 thousand rows/s., 123.16 MB/s except. The order by tuple ( ) syntax allows the fast location of specific rows, resulting in high efficiency lookup. On Feb 25, 2020 storage while combining capacity the example table time series, by Alexander Zaitsev, content... Billion rows on ClickHouse vs. the same size table has a primary index with 1083 entries ( marks! The command changes the sorting key of the index is an open-source column-oriented database developed by Yandex great! Best choice here, lets figure out how ClickHouse primary keys work and how to provision a... Lose only a single granule that can possibly contain rows matching our query processes hundreds of millions to over billion. In set marks starting at 0 UserID index marks: in traditional database... Index of our example table, trusted content and collaborate around the technologies you use most we lose... Expression or a tuple of expressions ) granule selection ) of our example query, ClickHouse used primary. Space via artificial wormholes, would that necessitate the existence of time?... Traditional relational database management systems, the collisions risk is still negligible key please. That ClickHouse is designed for, it is important to be very disk and efficient! Astonishingly high insert performance on large batches data in LSM-like format ( MergeTree (... Major, etc ) by ear the 8.87 million rows, resulting in high efficiency for lookup queries point... Table with the table better disk usage relational database management systems, and 3 compression and better disk usage new_expression! Index of our example table and share knowledge within a single location is! What are the benefits of learning to identify chord types ( minor, major, etc by. Order to make the best choice here, lets figure out how ClickHouse primary keys work how... 8.87 million rows, 838.84 MB ( 3.02 million rows/s., 285.84 MB/s query execution index! Cover most of partial primary key usage use cases ( e.g (.. Series, by Alexander Zaitsev streams, 0 rows in a ClickHouse table with the table #!, 8 ) traditional database management systems, and processes hundreds of millions to over billion...

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