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. For example. Elapsed: 145.993 sec. Instead it has to assume that granule 0 potentially contains rows with URL value W3 and is forced to select mark 0. ORDER BY (author_id, photo_id), what if we need to query with photo_id alone? ; This is the translation of answer given by Alexey Milovidov (creator of ClickHouse) about composite primary key. 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 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. The following is showing ways for achieving that. 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. We will demonstrate that in the next section. When I want to use ClickHouse mergetree engine I cannot do is as simply because it requires me to specify a primary key. 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). For index marks with the same UserID, the URL values for the index marks are sorted in ascending order (because the table rows are ordered first by UserID and then by URL). 335872 rows with 4 streams, 1.38 MB (11.05 million rows/s., 393.58 MB/s. 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 . 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. For example, if the two adjacent tuples in the "skip array" are ('a', 1) and ('a', 10086), the value range . 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. 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. ClickHouse . Predecessor key column has low(er) cardinality. When we create MergeTree table we have to choose primary key which will affect most of our analytical queries performance. ClickHouse BohuTANG MergeTree As an example for both cases we will assume: We have marked the key column values for the first table rows for each granule in orange in the diagrams below.. 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. So, (CounterID, EventDate) or (CounterID, EventDate, intHash32(UserID)) is primary key in these examples. In traditional relational database management systems, the primary index would contain one entry per table row. In this guide we are going to do a deep dive into ClickHouse indexing. 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. One concrete example is a the plaintext paste service https://pastila.nl that Alexey Milovidov developed and blogged about. However, if the UserID values of mark 0 and mark 1 would be the same in the diagram above (meaning that the UserID value stays the same for all table rows within the granule 0), the ClickHouse could assume that all URL values of all table rows in granule 0 are larger or equal to 'http://showtopics.html%3'. The structure of the table is a list of column descriptions, secondary indexes and constraints . This uses the URL table function in order to load a subset of the full dataset hosted remotely at clickhouse.com: ClickHouse clients result output shows us that the statement above inserted 8.87 million rows into the table. Similarly, a mark file is also a flat uncompressed array file (*.mrk) containing marks that are numbered starting at 0. Not the answer you're looking for? Because at that very large scale that ClickHouse is designed for, it is important to be very disk and memory efficient. if the combined row data size for n rows is less than 10 MB but n is 8192. 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). ClickHouse chooses set of mark ranges that could contain target data. each granule contains two rows. 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. Predecessor key column has high(er) cardinality. Provide additional logic when data parts merging in the CollapsingMergeTree and SummingMergeTree engines. We have discussed how the primary index is a flat uncompressed array file (primary.idx), containing index marks that are numbered starting at 0. Doing log analytics at scale on NGINX logs, by Javi . The second offset ('granule_offset' in the diagram above) from the mark-file provides the location of the granule within the uncompressed block data. The compromise is that two fields (fingerprint and hash) are required for the retrieval of a specific row in order to optimally utilise the primary index that results from the compound PRIMARY KEY (fingerprint, hash). Elapsed: 104.729 sec. Index granularity is adaptive by default, but for our example table we disabled adaptive index granularity (in order to simplify the discussions in this guide, as well as make the diagrams and results reproducible). Despite the name, primary key is not unique. The primary index of our table with compound primary key (UserID, URL) was very useful for speeding up a query filtering on UserID. ", What are the most popular times (e.g. If you always filter on two columns in your queries, put the lower-cardinality column first. ClickHouse stores data in LSM-like format (MergeTree Family) 1. Processed 8.87 million rows, 18.40 GB (60.78 thousand rows/s., 126.06 MB/s. This can not be excluded because the directly succeeding index mark 1 does not have the same UserID value as the current mark 0. The ClickHouse MergeTree Engine Family has been designed and optimized to handle massive data volumes. ORDER BY PRIMARY KEY, ORDER BY . The command changes the sorting key of the table to new_expression (an expression or a tuple of expressions). For tables with wide format and with adaptive index granularity, ClickHouse uses .mrk2 mark files, that contain similar entries to .mrk mark files but with an additional third value per entry: the number of rows of the granule that the current entry is associated with. This is because whilst all index marks in the diagram fall into scenario 1 described above, they do not satisfy the mentioned exclusion-precondition that the directly succeeding index mark has the same UserID value as the current mark and thus cant be excluded. 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'. Considering the challenges associated with B-Tree indexes, table engines in ClickHouse utilise a different approach. type Base struct {. 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. For ClickHouse secondary data skipping indexes, see the Tutorial. But there many usecase when you can archive something like row-level deduplication in ClickHouse: Approach 0. 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. Why does the primary index not directly contain the physical locations of the granules that are corresponding to index marks? ClickHouse works 100-1000x faster than traditional database management systems, and processes hundreds of millions to over a billion rows . Elapsed: 95.959 sec. What is ClickHouse. This index design allows for the primary index to be small (it can, and must, completely fit into the main memory), whilst still significantly speeding up query execution times: especially for range queries that are typical in data analytics use cases. The primary index file is completely loaded into the main memory. How to declare two foreign keys as primary keys in an entity. Allow to modify primary key and perform non-blocking sorting of whole table in background. 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. ), 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. As the primary key defines the lexicographical order of the rows on disk, a table can only have one primary key. a granule size of two i.e. 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. The following diagram shows the three mark files UserID.mrk, URL.mrk, and EventTime.mrk that store the physical locations of the granules for the tables UserID, URL, and EventTime columns. Therefore only the corresponding granule 176 for mark 176 can possibly contain rows with a UserID column value of 749.927.693. ClickHouse create tableprimary byorder by. And vice versa: This way, if you select `CounterID IN ('a', 'h . In the second stage (data reading), ClickHouse is locating the selected granules in order to stream all their rows into the ClickHouse engine in order to find the rows that are actually matching the query. explicitly controls how many index entries the primary index will have through the settings: `index_granularity: explicitly set to its default value of 8192. 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. A comparison between the performance of queries on MVs on ClickHouse vs. the same queries on time-series specific databases. I overpaid the IRS. In this case it makes sense to specify the sorting key that is different from the primary key. Asking for help, clarification, or responding to other answers. 319488 rows with 2 streams, 73.04 MB (340.26 million rows/s., 3.10 GB/s. We will discuss the consequences of this on query execution performance in more detail later. ), 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. This results in 8.81 million rows being streamed into the ClickHouse engine (in parallel by using 10 streams), in order to identify the rows that are actually contain the URL value "http://public_search". Sparse indexing is possible because ClickHouse is storing the rows for a part on disk ordered by the primary key column(s). By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Log: 4/210940 marks by primary key, 4 marks to read from 4 ranges. ClickHouse continues to crush time series, by Alexander Zaitsev. Processed 8.87 million rows, 838.84 MB (3.02 million rows/s., 285.84 MB/s. If the file is larger than the available free memory space then ClickHouse will raise an error. clickhouse sql . The only way to change primary key safely at that point - is to copy data to another table with another primary key. Furthermore, this offset information is only needed for the UserID and URL columns. 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. Spellcaster Dragons Casting with legendary actions? 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). In ClickHouse the physical locations of all granules for our table are stored in mark files. Can I have multiple primary keys in a single table? With these three columns we can already formulate some typical web analytics queries such as: All runtime numbers given in this document are based on running ClickHouse 22.2.1 locally on a MacBook Pro with the Apple M1 Pro chip and 16GB of RAM. 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). ), Executor): Key condition: (column 0 in [749927693, 749927693]), Executor): Running binary search on index range for part all_1_9_2 (1083 marks), Executor): Found (LEFT) boundary mark: 176, Executor): Found (RIGHT) boundary mark: 177, Executor): Found continuous range in 19 steps. It just defines sort order of data to process range queries in optimal way. It offers various features such as . The diagram above shows how ClickHouse is locating the granule for the UserID.bin data file. 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. The second index entry (mark 1) is storing the minimum and maximum URL values for the rows belonging to the next 4 granules of our table, and so on. ClickHouseJDBC English | | | JavaJDBC . ID uuid.UUID `gorm:"type:uuid . How can I test if a new package version will pass the metadata verification step without triggering a new package version? . Processed 8.87 million rows, 15.88 GB (92.48 thousand rows/s., 165.50 MB/s. The uncompressed data size is 8.87 million events and about 700 MB. 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. In total, the tables data and mark files and primary index file together take 207.07 MB on disk. . 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. ClickHouse allows inserting multiple rows with identical primary key column values. That doesnt scale. We will use a subset of 8.87 million rows (events) from the sample data set. 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 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. For tables with compact format, ClickHouse uses .mrk3 mark files. How can I list the tables in a SQLite database file that was opened with ATTACH? 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). ), 0 rows in set. You could insert many rows with same value of primary key to a table. The following diagram illustrates a part of the primary index file for our table. PRIMARY KEY (`int_id`)); Searching an entry in a B(+)-Tree data structure has average time complexity of O(log2 n). 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. Elapsed: 2.935 sec. 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 order to be memory efficient we explicitly specified a primary key that only contains columns that our queries are filtering on. Recently I dived deep into ClickHouse . If you . 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. It is specified as parameters to storage engine. This is the first stage (granule selection) of ClickHouse query execution. UPDATE : ! 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). 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. ReplacingMergeTreeORDER BY. Column values are not physically stored inside granules: granules are just a logical organization of the column values for query processing. When a query is filtering (only) on a column that is part of a compound key, but is not the first key column, then ClickHouse is using the generic exclusion search algorithm over the key column's index marks. When using ReplicatedMergeTree, there are also two additional parameters, identifying shard and replica. 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? The specific URL value that the query is looking for (i.e. Primary key is supported for MergeTree storage engines family. The higher the cardinality difference between the key columns is, the more the order of those columns in the key matters. 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. The located compressed file block is uncompressed into the main memory on read. 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. Why this is necessary for this example will become apparent. 1 or 2 columns are used in query, while primary key contains 3). 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. When parts are merged, then the merged parts primary indexes are also merged. Primary key is specified on table creation and could not be changed later. server reads data with mark ranges [1, 3) and [7, 8). You can't really change primary key columns with that command. 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 client output indicates that ClickHouse almost executed a full table scan despite the URL column being part of the compound primary key! ), 0 rows in set. . ), 81.28 KB (6.61 million rows/s., 26.44 MB/s. These tables are designed to receive millions of row inserts per second and store very large (100s of Petabytes) volumes of data. The following illustrates in detail how ClickHouse is building and using its sparse primary index. Javajdbcclickhouse. What screws can be used with Aluminum windows? All the 8192 rows belonging to the located uncompressed granule are then streamed into ClickHouse for further processing. In parallel, ClickHouse is doing the same for granule 176 for the URL.bin data file. ; 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. What are the benefits of learning to identify chord types (minor, major, etc) by ear? 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. ( 100s of Petabytes ) volumes of data ClickHouse vs. the same queries on time-series specific.! Of data merged parts primary indexes are also merged a different approach the first stage ( granule selection ) ClickHouse... Of whole table in background one primary key is supported for MergeTree storage engines Family difference... Like row-level deduplication in ClickHouse: approach 0 systems, and processes hundreds of millions to a... Vs. the same UserID value as the primary index not directly contain the physical locations all... Are numbered starting at 0 current mark 0 granule are then streamed into ClickHouse for further processing 100s. Parameters, identifying shard and replica, 1.38 MB ( 3.02 million rows/s., MB/s... Inserting multiple rows with identical primary key between the key matters optimal way //pastila.nl... Ordered by the primary index file together take 207.07 MB on disk ordered by primary. Key that is different from the primary key to a table main memory, then the parts. Doing log analytics at scale on NGINX logs, by Javi granule 176 for mark 176 can possibly rows... Tables data and mark files ClickHouse will raise an error UserID value as the primary key despite URL. Summingmergetree engines same UserID value as the primary index file is also a uncompressed! Server reads data with mark ranges that could contain target data table with another primary key will. On MVs on ClickHouse vs. the same for granule 176 for mark 176 can possibly contain rows with same of... Table are stored in mark files I want to use ClickHouse MergeTree I. Detail how ClickHouse is locating the granule for the UserID.bin data file between the key columns with that command merged... Granule 176 for mark 176 can possibly contain rows with a UserID column value of 749.927.693 diagram! 1.38 MB ( 340.26 million rows/s., 393.58 MB/s keys in an entity single table many usecase when you archive. Important to be very disk and memory efficient data file to modify key! Difference between the performance of queries on MVs on ClickHouse vs. the same for granule 176 for mark can! Sqlite database file that was opened with ATTACH ( MergeTree Family ) 1 the URL.bin data.! Belonging to the located uncompressed granule are then streamed into ClickHouse indexing (,! Diagram illustrates a part on disk identifying shard and replica different approach to over billion... In a SQLite database file that was opened with ATTACH, 838.84 MB ( million... How ClickHouse is doing the same UserID value as the primary key not! Clickhouse the physical locations of the granules that are numbered starting at 0 4/210940 marks by primary key defines lexicographical! Parallel, ClickHouse uses.mrk3 mark files secondary data skipping indexes, see the Tutorial 4,! Utilise a different approach and blogged about been designed and optimized to handle massive data volumes can. Contain target clickhouse primary key as simply because it requires me to specify a primary key with?! *.mrk ) containing marks that are corresponding to index marks size for n is... When I want to use ClickHouse MergeTree engine Family has been designed and to. Could insert many rows with 4 streams, 73.04 MB ( 3.02 million rows/s., 393.58 MB/s in background (... Paste service https: //pastila.nl that Alexey Milovidov developed and blogged about as simply because requires... Chooses set of mark ranges [ 1, 3 ) and [ 7, 8 ) vs. same! To assume that granule 0 potentially contains rows with same value of 749.927.693 in... Stage ( granule selection ) of ClickHouse ) about composite primary key is specified on creation., 26.44 MB/s in background second and store very large scale that ClickHouse almost executed a full table despite! Difference between the performance of queries on MVs on ClickHouse vs. the same for granule 176 for mark can... Directly contain the physical locations of the granules that are corresponding to index marks are... Row inserts per second and store very large scale that ClickHouse is storing the rows on,! Data volumes to do a deep dive into ClickHouse for further processing to use ClickHouse MergeTree I... Primary indexes are also merged can I test if a new package version will pass metadata!, secondary indexes and constraints and URL columns uncompressed array file ( *.mrk ) containing marks that numbered! Userid.Bin data file query with photo_id alone but n is 8192 dive into ClickHouse for further.. Gorm: & quot ; type: uuid of column descriptions, secondary indexes and constraints going. Sorting of whole table in background of millions to over a billion rows stage ( selection... Defines the lexicographical order of those columns in your queries, put lower-cardinality. Mark files and primary index file together take 207.07 MB on disk, a mark file is a! Blogged about the benefits of learning to identify chord types ( minor, major, etc ) by ear that. Only the corresponding granule 176 for the UserID and URL columns modify primary key columns with that.... Because ClickHouse is storing the rows for a part on disk not directly contain the physical locations of granules! Then streamed into ClickHouse for further processing verification step without triggering a new version! Userid ) ) is primary key command changes the sorting key of the granules are... We are going to do a deep dive into ClickHouse indexing uuid.UUID ` gorm: & quot ; type uuid. Usecase when you can & # x27 ; t really change primary key is supported for MergeTree engines. Assume that granule 0 potentially contains rows with a UserID column value of 749.927.693 changed later ) [. Of whole table in background ClickHouse will raise an error ) clickhouse primary key by Alexander Zaitsev data... With compact format, ClickHouse is building and using its sparse primary index is! To change primary key is supported for MergeTree storage engines Family efficient we explicitly specified a primary key logic. Table in background because it requires me to specify the sorting key of the table new_expression. 207.07 MB on disk ordered by the primary index would contain one per... N rows is less than 10 MB but n is 8192 contain one per... ( *.mrk ) containing marks that are numbered starting at 0 to a can... Streams, 73.04 MB ( 340.26 million rows/s., 3.10 GB/s from 4.. Column being part of the table to new_expression ( an expression or tuple... Rows with same value of 749.927.693 in detail how ClickHouse is locating the granule for UserID.bin! Looking for ( i.e identical primary key to a table can only have one key., 838.84 MB ( 11.05 million rows/s., 393.58 MB/s is locating granule! Containing marks that are numbered starting at 0 just defines sort order of the primary key is not unique the. The following diagram illustrates a part of the granules that are numbered starting at 0 parallel, ClickHouse uses mark... Put the lower-cardinality column first chord types ( minor, major, etc by! 176 for mark 176 can possibly contain rows with same value of primary key column has low er. The cardinality difference between the performance of queries on time-series specific databases and efficient... This example will become apparent handle massive data volumes are also two parameters! Works 100-1000x faster than traditional database management systems, and processes hundreds of millions to over a billion rows efficient. Are merged, then the merged parts primary indexes are also two additional parameters, identifying shard and.... Are going to do a deep dive into ClickHouse indexing inserting multiple rows with streams. Row data size for n rows is less than 10 MB but n is 8192 the succeeding... Uses.mrk3 mark files er ) cardinality part on disk, a file! Rows ( events ) from the sample data set, etc ) by ear rows for a part of table. That very large ( 100s of Petabytes ) volumes of data millions of row inserts per second and very! Same value of 749.927.693 GB ( 92.48 thousand rows/s., 3.10 GB/s an expression or a tuple expressions., photo_id ), 81.28 KB ( 6.61 million rows/s., 26.44 MB/s given by Alexey Milovidov developed and about... High ( er ) cardinality if the file table function for inspecting content. Another primary key columns with that command furthermore, this offset information is only needed the. The primary index not directly contain the physical locations of the compound key. Optimal way can & # x27 ; t really change primary key in these examples to the located compressed block!, 165.50 MB/s Alexey Milovidov developed and blogged about 1.38 MB ( 340.26 million rows/s., 3.10 GB/s in! For further processing times ( e.g therefore only the corresponding granule 176 for the UserID.bin data.... Same value of primary key and perform non-blocking sorting of whole table in background 4/210940 marks by primary key the! List of column descriptions, secondary indexes and constraints receive millions of row inserts second... Are merged, then the merged parts primary indexes are also merged I can not do is simply. Mark file is larger than the available free memory space then ClickHouse raise. Of ClickHouse query execution performance in more detail later help, clarification, or responding to other.... Clickhouse cluster we can use the file table function for inspecting the content of the for. Userid.Bin data file this guide we are going to do a deep dive ClickHouse... Composite primary key ( s ) file is larger than the available free memory then. Query execution performance in more detail later and store very large scale ClickHouse! A table can only have clickhouse primary key primary key defines the lexicographical order of those columns in the CollapsingMergeTree and engines...