Showing posts with label SQL Server 2019. Show all posts
Showing posts with label SQL Server 2019. Show all posts

16 February 2025

💠🛠️🗒️SQL Server: Columnstore Indexes [Notes]

Disclaimer: This is work in progress intended to consolidate information from various sources. It considers only on-premise SQL Server, for other platforms please refer to the documentation.

Last updated: 15-Feb-2024

[SQL Server] columnstore indexes (CI)

  • {def} a technology for storing, retrieving and managing data by using a columnar data format (aka columnstore
    • store compressed data on a per-column rather than a per-row basis [5]
  • {benefit} designed for analytics and data warehousing workloads
    • data warehousing
      • {scenario} store fact tables and large dimension tables
        • ⇐ tend to require full table scans rather than table seeks
    • analytics workloads
      • {scenario} [SQL Server 2016 SP1] can be used for real-time analytics on operational databases
        • ⇐ an updatable nonclustered columnstore index can be created on a rowstore table
  • {benefit} performance increase 
    • can achieve up to 100x better performance [4]
    • offers an order of magnitude better performance than a rowstore index
      • {feature} uses batch mode execution
        • improves query performance typically by two to four times
      • have high performance gains for analytic queries that scan large amounts of data, especially on large tables (>1 million rows) 
  • {benefit} reduces significantly the data warehouse storage costs
    • {feature} data compression
      • ⇒ provides high compression rates, typically by 10 times
        • ⇒ reduces total I/O from the physical media
          • ⇐ queries often select only a few columns from a table
          • minimizes or eliminates system I/O bottlenecks
        • reduces significantly the memory footprint
          • ⇒ query performance can improve 
            • because SQL Server can perform more query and data operations in memory
  • {benefit} built in memory
    • ⇒ sufficient memory must be available 
  • {benefit} part of the database engine
    • no special hardware is needed
  • {concept} columnstore 
    • {def} data structure logically organized as a table with rows and columns, and physically stored in a column-wise data format
      • stores values from the same domain which commonly have similar values
        • when a query references a column, then only that column is fetched from disk [3]
          • ⇐ the columns not requested are skipped 
            • ⇒ they are not loaded into memory 
        • when a query is executed, the rows must be reconstructed
          • ⇒ row reconstruction takes some time and uses some CPU and memory resources [3]
        • [SQL Server 2016] columnstore index on rowstore tables
          • columnstore is updated when data changes in the rowstore table
        • both indexes work against the same data
  • {concept}rowstore
    • {def} data that's logically organized as a table with rows and columns, and physically stored in a row-wise data format
      • ⇐ the traditional way to store relational table data
      • refers to a table where the underlying data storage format is either
        • a heap
        • a clustered index
        • a memory-optimized table
  • {concept} rowstore index
    • performs best on queries that seek into the data, when searching for a particular value, or for queries on a small range of values
      • ⇒ appropriate for transactional workloads 
        • because they tend to require mostly table seeks instead of table scans
  • {concept} rowgroup
    • {def} a group of rows that are compressed into columnstore format at the same time
      • {constraint} has a maximum number of rows per rowgroup, which is 1,048,576 =2^20 rows
      • contains one column segment for every column in the table
      • can have more than one delta rowgroup that form the deltastore
        • e.g. when multiple threads create columnstore indexes using parallel execution plans [5]
          • ⇐ each thread will work with its own subset of data, creating separate rowgroups [5]
        • [partitions] each table partition has its own set of row groups [5]
          • ⇐  too many partitions may prevent workloads from benefiting from a CCI [11]
            • ⇐ data aren’t pushed into a compressed columnstore segment until the rowgroup limit is reached
    • {event} rowgroup is compressed
      • marked as read-only [16]
      • a compressed rowgroup is considered as fragmented when either 
        • row number < rowgroup limit but dictionary size reached the maximum
          • nothing can be done to increase the number of rows [15]
          • the trim_reason is other than DICTIONARY_SIZE
        • it has nonzero deleted rows that exceeds a minimum threshold [15]
    • {event} all data from rowgroup deleted 
      • transitions from COMPRESSED into TOMBSTONE state
      • later removed by the tuple-mover background process
    • {event} rows in the columnstore indexes can be moved to different locations
      • row-id in the nonclustered indexes aren’t updated 
        • ⇐ the mappings between old and new row locations are stored in an internal structure (aka mapping index) 
    • {event} rowgroup build
      • all column data are combined on a per-row group basis, encoded and compressed [5]
        • the rows within a row group can be rearranged if that helps to achieve a better compression rate [5]
  • {feature} data compression
    • the table is sliced into rowgroups, and each rowgroup is compresses in a column-wise manner
      • the number of rows in the rowgroup must be 
        • large enough to improve compression rates
        • small enough to benefit from in-memory operations
          • having too many small rowgroups decreases columnstore index’s quality
    • uses its own compression mechanism 
      • ⇒ row or page compression cannot be used on it [3]
      • [SQL Server 2016] page compression has been removed
        • ⇐ in some cases, page compression disallowed the creation of columnstore indexes with a very large number of columns [5]
  • {feature} compression delay
    • computed when a delta rowgroup is closed [7]
    • keeps the ‘active’ rows in delta rowgroup and only transition these rows to compressed rowgroup after a specified delay [7]
      • ⇐ reduces the overall maintenance overhead of NCCI [7]
      • ⇒ leads to a larger number of delta rowgroups [7]
    • {best practice} if the workload is primarily inserting data and querying it, the default COMPRESSION_DELAY of 0 is the recommended option [7]
    • {best practice} [OLTP workload] if > 10% rows are marked deleted in recently compressed rowgroups, then consider a value that accommodates the behavior [7]
      • via: create nonclustered columnstore index with (compression_delay= 150)
  • {feature} data encoding
    • all values in the data are replaced with 64-bit integers using one of two encoding algorithms
    • {concept} dictionary encoding
      • stores distinct values from the data in a separate structure (aka dictionary} 
        • every value in a dictionary has a unique ID assigned [5]
          • the ID is used for replacement
    • {concept} global dictionary
      • shared across all segments that belong to the same index partition [5]
    • {concept} local dictionary
      • created for individual segments using values that are not present in the global dictionary
    • {concept} value-based encoding
      • mainly used for numeric and integer data types that do not have enough duplicated values [5]
        • dictionary encoding would be inefficient [5]
      • converts integer and numeric values to a smaller range of 64-bit integers in 2 steps
        • {step} [numeric data types] are converted to integers using the minimum positive exponent (aka magnitude that allows this conversion) [5]
          • {goal} convert all numeric values to integers [5]
          • [integer data types] the smallest negative exponent is chosen that can be applied to all values without losing their precision [5]
            • {goal} reduce the interval between the minimum and maximum values stored in the segment [5]
        • {step} the minimum value (aka base value) in the segment is identified and subtracted it from all other values [5]
          • ⇒ makes the minimum value in the segment number 0 [5]
      • after encoding the data are compressed and stored as a LOB allocation unit
  • {concept} column segment 
    • {def} a column of data from within the rowgroup
    • is compressed together and stored on physical media
    • SQL Server loads an entire segment to memory when it needs to access its data
  • {concept} segment metadata 
    • store metadata about each segment 
      • e.g. minimum and maximum values
      • ⇐ segments that do not have the required data are skipped [5]
  • {concept} deltastore
    • {def} all of the delta rowgroups of a columnstore index
    • its operations are handled behind the scenes
      • can be in either states
        • {state} open (aka open delta store) 
          • accepts new rows and allow modifications and deletions of data
        • {state} closed (aka closed data store)
          • a delta store is closed when it reaches its rowgroup limit
  • {concept} delta rowgroup 
    • {def} a clustered B-tree index that's used only with columnstore indexes
    • improves columnstore compression and performance by storing rows until the number of rows reaches the rowgroup limit and are then moved into the columnstore
    • {event} reaches the maximum number of rows
      • it transitions from an ‘open’ to ‘closed’ state
      • a closed rowgroup is compressed by the tuple-mover and stored into the columnstore as COMPRESSED rowgroup
    • {event} compressed
      • the existing delta rowgroup transitions into TOMBSTONE state to be removed later by the tuple-mover when there is no reference to it
  • {concept} tuple-mover 
    • background process that checks for closed row group
      • if it finds a closed rowgroup, it compresses the delta rowgroup and stores it into the columnstore as a COMPRESSED rowgroup
  • {concept} clustered columnstore index (CCI) 
    • is the primary storage for the entire table
    • {characteristic) updatable
      • has two structures that support data modifications
        • ⇐ both use the B-Tree format to store data [5]
        • ⇐ created on demand [5]
        • delete bitmap
          • indicates which rows were deleted from a table
          • upon deletion the row continues to be stored into the rowgroup
          • during query execution SQL Server checks the delete bitmap and excludes deleted rows from the processing [5]
        • delta store
          • includes newly inserted rows
          • updating a row triggers the deletion of the existing row and insertion of a new version of a row to a delta store
            • ⇒ the update does not change the row data
            • ⇒ the updated row is inserted to a delete bitmap
        • [partitions] each partition can have a single delete bitmap and multiple delta stores
          • ⇐ this makes each partition self-contained and independent from other partitions
            • ⇒ allows performing a partition switch on tables that have clustered columnstore indexes defined [5]
    • {feature} supports minimal logging for batch sizes >= rowgroup’s limit [12]
    • [SQL Server 2017] supports non-persisted computed columns in clustered columnstore indexes [2]
    • store some data temporarily into a clustered index (aka deltastore) and a btree list of IDs for deleted rows
      • ⇐ {benefit} reduces fragmentation of the column segments and improves performance
      • combines query results from both the columnstore and the deltastore to return the correct query results
    • [partitions] too many partitions can hurt the performance of a clustered columnstore index [11]
  • {concept} nonclustered columnstore index (NCCI)
    • {def} a secondary index that's created on a rowstore table
      • is defined as one or more columns of the table and has an optional condition that filters the rows
      • designed to be used for workloads involving a mix of transactional and analytics workload*
      • functions the same as a clustered columnstore index
        • ⇐ has same performance optimizations (incl. batchmode operators)
        • {exception} doesn’t supports persisted computed columns
          • can’t be created on a columnstore index that has a computed column [2]
        • however behave differently between the various versions of SQL Server
          • [SQL Server 2012|2014] {restriction} readonly
      • contains a copy of part or all of the rows and columns in the underlying table
        • include a row-id , which is either the address of
          • a row in a heap table 
          • a clustered index key value
            • includes all columns from the clustered index even when not explicitly defined in the CREATE statement
              • the not specified columns will not be available in the sys.index_columns view
      • [SQL Server 2016] multiple nonclustered rowstore indexes can be created on a columnstore index and perform efficient table seeks on the underlying columnstore
        • ⇒ once created, makes it possible to drop one or more btree nonclustered indexes
      • enables real-time operational analytics where the OLTP workload uses the underlying clustered index while analytics run concurrently on the columnstore index
  • {concept} batch mode execution (aka vector-based execution, vectorized execution
    • {def} query processing method used to process multiple rows together in groups of rows, or batches, rather than one row at a time
      • SQL Server can push a predicate to the columnstore index scan operator, preventing unnecessary rows from being loaded into the batch [5]
      • queries can process up to 900 rows together
        • enables efficient query execution (by a 3-4x factor) [4]
        • ⇐ the size of the batches varies to fit into the CPU cache
        • ⇒ reduces the number of times that the CPU needs to request external data from memory or other components [5]
      • improves the performance of aggregations, which can be calculated on a per-batch rather than a per-row basis [5]
      • tries to minimize the copy of data between operators by creating and maintaining a special bitmap that indicates if a row is still valid in the batch [5]
        • ⇐ subsequent operators will ignore the non-valid rows
        • every operator has a queue of work items (batches) to process [5]
        • worker threads from a shared pool pick items from queues and process them while migrating from operator to operator [5]
    • is closely integrated with, and optimized around, the columnstore storage format.
      • columnstore indexes use batch mode execution
        • ⇐ improves query performance typically by two to four times
  • {concept} tuple mover
    • single-threaded process that works in the background, preserving system resources
      • runs every five minutes
    • converts closed delta stores to row groups that store data in a column-based storage format [5]
      • can be disabled via trace flag T-634 
      • ⇐ the conversion of closed delta stores to row groups can be forced by reorganizing an index [5]
        • runs in parallel using multiple threads
          • decreases significantly conversion time at a cost of extra CPU load and memory usage [5]
        • via: ALTER INDEX REORGANIZE command
    • it doesn’t prevent other sessions from inserting new data into a table [5]
    • deletions and data modifications would be blocked for the duration of the operation [5]
      • {recommendation} consider forcing index reorganization manually to reduce execution, and therefore locking, time [5]
    • considered fragmented if it has
      • multiple delta rowgroups
      • deleted rows
    • require maintenance like that of regular B-Tree indexes [5]
      • {issue] partially populated row groups
      • {issue} overhead of delta store and delete bitmap scans during query execution
      • rebuilding the columnstore index addresses the issues
      • the strategy depends on the volatility of the data and the ETL processes implemented in the system [5]
        • {recommendation} rebuild indexes when a table has a considerable volme of deleted rows and/or a large number of partially populated rowgroups [5]
        • {recommendation} rebuild partition(s) that still have a large number of rows in open delta stores after the ETL process has completed, especially if the ETL process does not use a bulk insert API [5]
      • creating/dropping/disabling/rebuilding functions like any other index
    • columnstore statistics 
      • a statistics object is created at the time of columnstore index creation; however, it is neither populated nor updated afterward [5]
        • ⇐ SQL Server relies on segment information, B-Tree indexes (when available), and column-level statistics when deciding if a columnstore index needs to be used [5]
        • it is beneficial to create missing column-level statistics on the columns that participate in a columnstore index and are used in query predicates and as join keys [5]
          • ⇐ statistics rarely update automatically on very large tables [5]
            • ⇒ statistics must be updated ‘manually’
      • [SQL Server 2019] included into the schema-only clone of a database functionality [8]
        • enable performance troubleshooting without the need to manual capture the statistics information
      • columnstore indexes has been added to sp_estimate_data_compression_savings. In SQL Server 2019 both 
      • COLUMNSTORE and COLUMNSTORE_ARCHIVE have been added to allow you to estimate the space savings if 
      • either of these indexes are used on a table.
        • via DBCC CLONEDATABASE
    • [in-memory tables] 
      • {limitation} a columnstore index must include all the columns and can’t have a filtered condition [2]
      • {limitation} queries on columnstore indexes run only in InterOP mode, and not in the in-memory native mode [2]
    • {operation} designing columnstore indexes
      • {best practice} understand as much as possible data’s characteristics
      • {best practice} identify workload’s characteristics
    • {operation} create a clustered columnstore index
      • via CREATE CLUSTERED COLUMNSTORE INDEX command
      • not needed to specify any columns in the statement
        • ⇐ the index will include all table columns
    • {operation} index rebuilding 
      • forces SQL Server to remove deleted rows physically from the index and to merge the delta stores’ and row groups’ data [5]
        • all column segments are recreated with row groups fully populated [5]
      • [<SQL Server 2019] offline operation
      • [SQL Server 2019 Enterprise] online operation
        • ⇒ higher availability 
        • ⇐ pausing and resuming create and rebuild operations are not supported [11]
      • very resource intensive process
      • holds a schema modification (Sch-M) lock on the table
        • ⇒ prevents other sessions from accessing it [5]
        • ⇐ the overhead can be mitigated by using table/index partitioning
          • ⇒ indexes will be rebuild on a partition basis for those partition with volatile data [5]
    • {operation} index reorganization 
      • [<SQL Server 2019] a reorganize operation is required to merge smaller COMPRESSED rowgroups, following an internal threshold policy that determines how to remove deleted rows and combine the compressed rowgroups
      • [SQL Server 2019] a background merge task also works to merge COMPRESSED rowgroups from where a large number of rows has been deleted
        • ⇐ after merging smaller rowgroups, the index quality should be improved.
        • the tuple-mover is helped by a background merge task that automatically compresses smaller OPEN delta rowgroups that have existed for some time as determined by an internal threshold, or merges COMPRESSED rowgroups from where a large number of rows has been deleted
        • via: ALTER INDEX REORGANIZE command
      • [SQL Server 2016] performs additional defragmentation
        • removes deleted rows from row groups that have 10 or more percent of the rows logically deleted [5]
        • merges closed row groups together, keeping the total number of rows less than or equal than rowgroup’s limit [5]
        • ⇐ both processes can be done together [5]
      • [SQL Server 2014] the only action performed is compressing and moving the data from closed delta stores to rowgroups [5] 
        • ⇐ delete bitmap and open delta stores stay intact [5]
      • via: ALTER INDEX REORGANIZE
        • uses all available system resources while it is running [5]
          • ⇒ speeds up the execution process 
          • reduce the time during which other sessions cannot modify or delete data in a table [5]
        • close and compress all open row groups
          • via: ALTER INDEX REORGANIZE WITH (COMPRESS_ALL_ROW_GROUPS = ON)
          • row groups aren’t merged during this operation [5]
    • {operation} estimate compression savings
      • [SQL Server 2019] COLUMNSTORE and COLUMNSTORE_ARCHIVE added
        • allows estimating the space savings if either of these indexes are used on a table [8]
        • {limitation} not available in all editions 
      • via: sp_estimate_data_compression_savings 
    • {operation} [bulk loads] when the number of rows is less than deltastore’s limit, all the rows go directly to the deltastore
      • [large bulk load] most of the rows go directly to the columnstore without passing through the deltastore
        • some rows at the end of the bulk load might be too few in number to meet the minimum size of a rowgroup
          • ⇒ the final rows go to the deltastore instead of the columnstore
      • bulk insert operations provide the number of rows in the batch as part of the API call [5]
        • best results are achieved by choosing a batch size that is divisible by rowgroup’s limit [5]
          • ⇐ guarantees that every batch produces one or several fully populated row groups [5]
            • ⇒ reduce the total number of row groups in a table [5]
            • ⇒ improves query performance
          • ⇐ the batch size shouldn’t exceed rowgroup’s limit [5]
            • row groups can be still created on the fly in a manner to similar a bulk insert when the size of the insert batch is close to or exceeds [5]
  • {operation} [non-bulk operations] trickle inserts go directly to a delta store
  • {feature} parallel inserts
    • [SQL Server 2016] requires following conditions for parallel insert on CCI [6]
      • must specify TABLOCK
      • no NCI on the clustered columnstore index
      • no identity column
      • database compatibility is set to 130
  • {recommendation} minimize the use of string columns in facts tables [5]
    • string data use more space
    • their encoding involves additional overhead during batch mode execution [5]
    • queries with predicates on string columns may have less efficient execution plans that also require significantly larger memory grants as compared to their non-string counterparts [5]
  • {recommendation} [SQL Server 2012|2014] do not push string predicates down toward the lowest operators in execution plans.
  • {recommendation} add another dimension table and replace the string value in the facts table with a synthetic, integer-based ID key that references a new table [5]
  • {operation} upgrading to SQL Server 2016
    • make sure that queries against the tables with columnstore indexes can utilize parallelism in case if database compatibility level less than 130 [5]
  • {feature} [SQL Server 2019] automated columnstore index maintenance [8]
  • {improvement} [SQL Server 2019] better columnstore metadata memory management
  • {improvement} [SQL Server 2019] low-memory load path for columnstore tables
  • {improvement} [SQL Server 2019] improved performance for bulk loading to columnstore indexes
  • {improvement} [SQL Server 2019] server startup process has been made faster for databases that use in-memory columnstore tables for HTAP
  • {feature} DMVs
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References:
[1] SQL Docs (2020) Columnstore indexes: Overview [link]
[2] Microsoft Learn (2024) SQL: What's new in columnstore indexes  [link]
[3] Dejan Sarka et al (2012) Exam 70-463: Implementing a Data Warehouse with Microsoft SQL Server 2012 (Training Kit)
[4] SQL Docs (2019) Columnstore indexes - Query performance [link]
[5] Dmitri Korotkevitch (2016) Pro SQL Server Internals 2nd Ed.
[6] Microsoft Learn (2016) Columnstore Index: Parallel load into clustered columnstore index from staging table [link]
[7] Microsoft Learn (2016) Columnstore Index Defragmentation using REORGANIZE Command [link]
[8] Microsoft (2018) Microsoft SQL Server 2019: Technical white paper [link]

Acronyms:
CCI - clustered columnstore index
CI - columnstore index
DBCC - Database Console Commands
DMV - Dynamic Management View
ETL - Extract, Transform, Load
HTAP - Hybrid Transactional/Analytical Processing 
LOB - Line of Business
NCCI - nonclustered columnstore index
OLTP - On-Line Transaction Processing
SP - Service Pack
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