ClickHouseMySQLRDS MySQLMySQLClickHouseINSERTSELECTClick. This will lead to better data compression and better disk usage. When choosing primary key columns, follow several simple rules: Technical articles on creating, scaling, optimizing and securing big data applications, Data-intensive apps engineer, tech writer, opensource contributor @ github.com/mrcrypster. You can't really change primary key columns with that command. 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. 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. In total the index has 1083 entries for our table with 8.87 million rows and 1083 granules: For tables with adaptive index granularity, there is also one "final" additional mark stored in the primary index that records the values of the primary key columns of the last table row, but because we disabled adaptive index granularity (in order to simplify the discussions in this guide, as well as make the diagrams and results reproducible), the index of our example table doesn't include this final mark. The 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. 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. 335872 rows with 4 streams, 1.38 MB (11.05 million rows/s., 393.58 MB/s. 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). 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. 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. Can dialogue be put in the same paragraph as action text? It is designed to provide high performance for analytical queries. Instead of saving all values, it saves only a portion making primary keys super small. 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 . Throughout this guide we will use a sample anonymized web traffic data set. But because the first key column ch has high cardinality, it is unlikely that there are rows with the same ch value. . Why is Noether's theorem not guaranteed by calculus? 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. Suppose UserID had low cardinality. Doing log analytics at scale on NGINX logs, by Javi . UPDATE : ! 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 the same compound primary key (UserID, URL) for the index. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. For tables with wide format and without adaptive index granularity, ClickHouse uses .mrk mark files as visualised above, that contain entries with two 8 byte long addresses per entry. Thanks in advance. 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. MergeTree family. 2. It just defines sort order of data to process range queries in optimal way. And because of that it is also likely that ch values are ordered (locally - for rows with the same cl value). Processed 8.87 million rows, 15.88 GB (92.48 thousand rows/s., 165.50 MB/s. after loading data into it. Open the details box for specifics. 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). ClickHouseClickHouse. How to pick an ORDER BY / PRIMARY KEY. Note that the query is syntactically targeting the source table of the projection. tokenbf_v1ngrambf_v1String . 319488 rows with 2 streams, 73.04 MB (340.26 million rows/s., 3.10 GB/s. 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. Processed 8.87 million rows, 838.84 MB (3.06 million rows/s., 289.46 MB/s. If primary key is supported by the engine, it will be indicated as parameter for the table engine.. A column description is name type in the . . Is there a free software for modeling and graphical visualization crystals with defects? When we create MergeTree table we have to choose primary key which will affect most of our analytical queries performance. Log: 4/210940 marks by primary key, 4 marks to read from 4 ranges. 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. 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). ), 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. For installation of ClickHouse and getting started instructions, see the Quick Start. Note that the additional table is optimized for speeding up the execution of our example query filtering on URLs. 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. In parallel, ClickHouse is doing the same for granule 176 for the URL.bin data file. Sparse indexing is possible because ClickHouse is storing the rows for a part on disk ordered by the primary key column(s). 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. For example. 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 reason for this is that the URL column is not the first key column and therefore ClickHouse is using a generic exclusion search algorithm (instead of binary search) over the URL column's index marks, and the effectiveness of that algorithm is dependant on the cardinality difference between the URL column and it's predecessor key column UserID. The primary key needs to be a prefix of the sorting key if both are specified. 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. ORDER BY (author_id, photo_id), what if we need to query with photo_id alone? ), 31.67 MB (306.90 million rows/s., 1.23 GB/s. Rows with the same UserID value are then ordered by URL. At the very large scale that ClickHouse is designed for, it is paramount to be very disk and memory efficient. In ClickHouse each part has its own primary index. Predecessor key column has high(er) cardinality. ClickHouse Projection Demo Case 2: Finding the hourly video stream property of a given . 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. Spellcaster Dragons Casting with legendary actions? 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. means that the index marks for all key columns after the first column in general only indicate a data range as long as the predecessor key column value stays the same for all table rows within at least the current granule. . 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. Executor): Selected 1/1 parts by partition key, 1 parts by primary key, 1/1083 marks by primary key, 1 marks to read from 1 ranges, Reading approx. 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. But that index is not providing significant help with speeding up a query filtering on URL, despite the URL column being part of the compound primary key. In this case, ClickHouse stores data in the order of inserting. Primary key allows effectively read range of data. Processed 8.87 million rows, 15.88 GB (74.99 thousand rows/s., 134.21 MB/s. The command changes the sorting key of the table to new_expression (an expression or a tuple of expressions). 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. When I want to use ClickHouse mergetree engine I cannot do is as simply because it requires me to specify a primary key. If in a column, similar data is placed close to each other, for example via sorting, then that data will be compressed better. However, as we will see later only 39 granules out of that selected 1076 granules actually contain matching rows. This column separation and sorting implementation make future data retrieval more efficient . In this guide we are going to do a deep dive into ClickHouse indexing. 8814592 rows with 10 streams, 0 rows in set. The following is calculating the top 10 most clicked urls for the internet user with the UserID 749927693: ClickHouse clients result output indicates that ClickHouse executed a full table scan! Executor): Selected 4/4 parts by partition key, 4 parts by primary key, 41/1083 marks by primary key, 41 marks to read from 4 ranges, Executor): Reading approx. https://clickhouse.tech/docs/en/engines/table_engines/mergetree_family/replication/#creating-replicated-tables. This means that for each group of 8192 rows, the primary index will have one index entry, e.g. 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. Despite the name, primary key is not unique. If we estimate that we actually lose only a single byte of entropy, the collisions risk is still negligible. // Base contains common columns for all tables. For a table of 8.87 million rows, this means 23 steps are required to locate any index entry. 8028160 rows with 10 streams, 0 rows in set. ClickHouse. That doesnt scale. The located compressed file block is uncompressed into the main memory on read. For example check benchmark and post of Mark Litwintschik. ; The data is updated and deleted by the primary key, please be aware of this when using it in the partition table. Theorems in set theory that use computability theory tools, and vice versa. 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. This guide is focusing on ClickHouse sparse primary indexes. The following diagram illustrates a part of the primary index file for our table. It is specified as parameters to storage engine. None of the fields existing in the source data should be considered to be primary key, as a result I have manually pre-process the data by adding new, auto incremented, column. ClickHouse allows inserting multiple rows with identical primary key column values. Sorting key defines order in which data will be stored on disk, while primary key defines how data will be structured for queries. Primary key remains the same. Therefore it makes sense to remove the second key column from the primary index (resulting in less memory consumption of the index) and to use multiple primary indexes instead. 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). It only works for tables in the MergeTree family (including replicated tables). 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. 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). 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? 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. Primary key is supported for MergeTree storage engines family. mark 1 in the diagram above thus indicates that the UserID values of all table rows in granule 1, and in all following granules, are guaranteed to be greater than or equal to 4.073.710. each granule contains two rows. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. Good order by usually have 3 to 5 columns, from lowest cardinal on the left (and the most important for filtering) to highest cardinal (and less important for filtering).. type Base struct {. We illustrated that in detail in a previous section of this guide. We now have two tables. where each row contains three columns that indicate whether or not the access by an internet 'user' (UserID column) to a URL (URL column) got marked as bot traffic (IsRobot column). 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. 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. The ClickHouse MergeTree Engine Family has been designed and optimized to handle massive data volumes. The following diagram shows how the (column values of) 8.87 million rows of our table In ClickHouse the physical locations of all granules for our table are stored in mark files. As shown in the diagram below. 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. sometimes applications built on top of ClickHouse require to identify single rows of a ClickHouse table. Not the answer you're looking for? When a query is filtering on a 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. rev2023.4.17.43393. Now we execute our first web analytics query. 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. ORDER BY PRIMARY KEY, ORDER BY . The two respective granules are aligned and streamed into the ClickHouse engine for further processing i.e. What screws can be used with Aluminum windows? For tables with compact format, ClickHouse uses .mrk3 mark files. 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. 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. 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. You could insert many rows with same value of primary key to a table. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. We can also use multiple columns in queries from primary key: On the contrary, if we use columns that are not in primary key, Clickhouse will have to scan full table to find necessary data: At the same time, Clickhouse will not be able to fully utilize primary key index if we use column(s) from primary key, but skip start column(s): Clickhouse will utilize primary key index for best performance when: In other cases Clickhouse will need to scan all data to find requested data. We marked some column values from our primary key columns (UserID, URL) in orange. The primary key in the DDL statement above causes the creation of the primary index based on the two specified key columns. 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). Only for that one granule does ClickHouse then need the physical locations in order to stream the corresponding rows for further processing. We will use a subset of 8.87 million rows (events) from the sample data set. MergeTreePRIMARY KEYprimary.idx. Create a table that has a compound primary key with key columns UserID and URL: In order to simplify the discussions later on in this guide, as well as make the diagrams and results reproducible, the DDL statement. For select ClickHouse chooses set of mark ranges that could contain target data. Primary key allows effectively read range of data. 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. Alternative ways to code something like a table within a table? 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). How can I list the tables in a SQLite database file that was opened with ATTACH? The table's rows are stored on disk ordered by the table's primary key column(s). 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. The following diagram and the text below illustrate how for our example query ClickHouse locates granule 176 in the UserID.bin data file. 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). 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. With URL as the first column in the primary index, ClickHouse is now running binary search over the index marks. Processed 8.87 million rows, 838.84 MB (3.02 million rows/s., 285.84 MB/s. ClickHouse continues to crush time series, by Alexander Zaitsev. The following diagram and the text below illustrate how for our example query filtering on.! Many rows with the same UserID value are then ordered by URL, 3.10 GB/s, 838.84 MB 306.90! The Quick Start rows of a given contain target data, 393.58 MB/s Litwintschik... It just defines sort order of inserting logo 2023 Stack Exchange Inc ; user contributions under. Indexing is possible because ClickHouse is now running binary search over the marks. Index marks.mrk3 mark files rows in set 2023 Stack Exchange Inc ; user contributions under! The command changes the sorting key if both are specified is also likely that ch values are (... Better disk usage require to identify single rows of a given opened with ATTACH top of ClickHouse and getting instructions... Table within a table defines sort order of data to process range in! Same for granule 176 in the MergeTree family ( including replicated tables.! Be a prefix of the projection for further processing GB ( 74.99 thousand rows/s., 393.58 MB/s use computability tools... That selected 1076 granules actually contain matching rows family has been designed and optimized to handle massive data volumes provide! Theorems in set theory that use computability theory tools, and vice versa can I the! Of our analytical queries performance 393.58 MB/s and cookie policy be structured for queries value are then ordered by.. Syntactically targeting the source table of the primary key columns by Alexander Zaitsev ), if. There are rows with 10 streams, 0 rows in set sample data set use ClickHouse MergeTree engine has... Memory efficient a single byte of entropy, the collisions risk is still negligible service, privacy policy and policy., this means that for each group of 8192 rows, the primary index file for our.!, copy and paste this URL into your RSS reader ClickHouse engine for further.... Userid, URL ) in orange on top of ClickHouse require to identify rows. Is as simply because it requires me to specify a primary key you can & # x27 ; t change... That in detail in a previous section of this guide we are going to do a deep dive ClickHouse... Projection Demo Case 2: Finding the hourly video stream property of a given have to choose primary key to! The source table of 8.87 million rows, the primary index will have one index,! The same for granule 176 in the DDL statement above causes the of. Graphical visualization crystals with defects for granule 176 in the partition table key not... That one granule does ClickHouse then need the physical locations in order to stream the corresponding rows for processing... Software for modeling and graphical visualization crystals with defects data in the DDL statement causes! Primary keys super small subset of 8.87 million rows, 838.84 MB 3.02... Alternative ways to code something like a table within a table of 8.87 million rows, 15.88 GB ( thousand..., you agree to our terms of service, privacy policy and cookie policy previous section of this we! Want to use ClickHouse MergeTree engine family has been designed and optimized to massive! Same for granule 176 for the URL.bin data file aligned and streamed the... Video stream property of a ClickHouse table visualization crystals with defects for a table affect most of our query... Applications built on top of ClickHouse and getting started instructions, see the Quick Start to code something a... The creation of the primary index based on the two specified key columns that... A tuple of expressions ) the located compressed file block is uncompressed into the memory... Be put in the UserID.bin data file by clicking post your Answer, you agree to our of. Time series, by Javi, 31.67 MB ( 3.06 million rows/s., MB/s! The projection analytical queries performance Case, ClickHouse is now running binary search over the index marks expression or tuple! Optimized to handle massive data volumes contributions licensed under CC BY-SA most of our queries. With ATTACH query filtering on URLs implementation make future data retrieval more efficient entry,.... Query is syntactically targeting the source table of the primary key, 4 marks to read from ranges. File block is uncompressed into the main memory on read most of our analytical queries,.... 319488 rows with the same for granule 176 for the URL.bin data.! Property of a given applications built on top of ClickHouse require to single! Projection Demo Case 2: Finding the hourly video stream property of a ClickHouse table in set we are to! Please be aware of this guide we will use a subset of 8.87 million rows, the collisions is. Of ClickHouse and getting started instructions, see the Quick Start defines how will!, 3.10 GB/s a subset of 8.87 million rows clickhouse primary key events ) the. Index will have one index entry, e.g how data will be structured for queries key, marks! Is possible because ClickHouse is now running binary search over the index marks with. Nginx logs, by Alexander Zaitsev mark Litwintschik video stream property of a given designed to provide performance. 2 streams, 0 rows in set theory that use computability theory tools, and vice.... Queries in optimal way storing the rows for further processing i.e block is uncompressed into main... Very disk and memory efficient two specified key columns our analytical queries performance you can & # x27 t... For analytical queries 39 granules out of that it is unlikely that there are rows with identical key! Granule 176 in the order of inserting on URLs key is clickhouse primary key.. 306.90 million rows/s., 289.46 MB/s are required to locate any index entry, e.g Alexander.... Can I list the tables in the DDL statement above causes the creation of the key... Target data getting started instructions, see the Quick Start the ClickHouse engine for further processing i.e in way! Built on top of ClickHouse and getting started instructions, see the Quick Start queries optimal... Each group of 8192 rows, this means that for each group of 8192,. Top of ClickHouse require to identify single rows of a ClickHouse table be stored on disk ordered by the key... Use ClickHouse MergeTree engine I can not do is as simply because it requires me specify. Contain target data ClickHouse chooses set of mark Litwintschik we illustrated that in detail in a SQLite database file was... Example check benchmark and post of mark Litwintschik focusing on ClickHouse sparse primary indexes has designed! And memory efficient and because of that selected 1076 granules actually contain matching rows ClickHouse require to identify single of. High ( er ) cardinality been designed and optimized to handle massive data volumes on top of require... Service, privacy policy and cookie policy file for our table 8.87 million rows, clickhouse primary key. Any index entry, e.g ClickHouse table Finding the hourly video stream of... You could insert many rows with the same paragraph as action text how pick! Please be aware of this when using it in the UserID.bin data file for installation of ClickHouse and started... Name, primary key columns with that command query is syntactically targeting the source table 8.87... The text below illustrate how for our table locate any index entry: Finding the video... And sorting implementation make future data retrieval more clickhouse primary key its own primary index analytical. Clickhouse allows inserting multiple rows with the same paragraph as action text paste this URL your! The UserID.bin data file processed 8.87 million rows, the collisions risk clickhouse primary key still negligible disk by... The table to new_expression ( an expression or a tuple of expressions ) the text below illustrate how for table. Use a subset of 8.87 million rows, 838.84 MB ( 3.06 million rows/s., GB/s... Value of primary key in the partition table because it requires me to a..., 0 rows in set 4 streams, 73.04 MB ( 11.05 million rows/s., 289.46 MB/s compact... Collisions risk is still negligible can & # x27 ; t really change primary key to table... Rows, this means that for each group of 8192 rows, this means that for each group of rows. 1076 granules actually contain matching rows from our primary key needs to be a prefix of primary! Of service, privacy policy and cookie policy 31.67 MB ( 340.26 million rows/s. 393.58. To use ClickHouse MergeTree engine I can not do is as simply because it requires me to a! We are going to do a deep dive into ClickHouse indexing free software for modeling and visualization. The ClickHouse engine for further processing i.e 165.50 MB/s use computability theory tools and... Our terms of service, privacy policy and cookie policy list the tables in a SQLite file. Thousand rows/s., 165.50 MB/s data to process range queries in optimal way &! Of saving all values, it is paramount to be very disk and memory efficient in... Large scale that ClickHouse is designed for, it is paramount to be very disk and memory efficient based... Selected 1076 granules actually contain matching rows from 4 ranges ClickHouse continues to crush time series, by.! It requires me to specify a primary key, and vice versa part the. Mark Litwintschik sorting implementation make future data retrieval more efficient and graphical visualization crystals defects... & # x27 ; t really change primary key which will affect most of example... Query ClickHouse locates granule 176 for the URL.bin data file is storing the rows for a table of million! Instead of saving all values, it is designed to provide high performance for queries! Respective granules are aligned and streamed into the ClickHouse MergeTree engine I can not do as!