Before data can be imported into the tables, create a matching schema.

On the primary server, create the schema:

1. For each database that you are importing, create the database with the statement:

1. For each table that you are importing, create the table with the statement:

Import the Data

Enterprise ColumnStore supports multiple methods to import data into ColumnStore tables.

cpimport

MariaDB Enterprise ColumnStore includes cpimport, which is a command-line utility designed to efficiently load data in bulk. Alternative methods are available.

To import your data from a TSV (tab-separated values) file, on the primary server run cpimport:

LOAD DATA INFILE

When data is loaded with the statement, MariaDB Enterprise ColumnStore loads the data using cpimport, which is a command-line utility designed to efficiently load data in bulk. Alternative methods are available.

To import your data from a TSV (tab-separated values) file, on the primary server use statement:

Import from Remote Database

MariaDB Enterprise ColumnStore can also import data directly from a remote database. A simple method is to query the table using the statement, and then pipe the results into cpimport, which is a command-line utility that is designed to efficiently load data in bulk. Alternative methods are available.

To import your data from a remote MariaDB database:

Next Step

Navigation in the Single-Node Enterprise ColumnStore topology with Local storage deployment procedure:

This page was step 5 of 5.

This procedure is complete.

MariaDB Corporation provides package repositories for CentOS / RHEL (YUM) and Debian / Ubuntu (APT). A download token is required to access the MariaDB Enterprise Repository.

Customer Download Tokens are customer-specific and are available through the MariaDB Customer Portal.

To retrieve the token for your account:

1. Navigate to

2. Log in.

3. Copy the Customer Download Token.

Substitute your token for CUSTOMER_DOWNLOAD_TOKEN when configuring the package repositories.

Set Up Repository

1. On the MaxScale node, install the prerequisites for downloading the software from the Web. Install on CentOS / RHEL (YUM):

Install on Debian / Ubuntu (APT):

1. On the MaxScale node, configure package repositories and specify MariaDB MaxScale 22.08:

Checksums of the various releases of the mariadb_es_repo_setup script can be found in the section at the bottom of the page. Substitute ${checksum} in the example above with the latest checksum.

Install MaxScale

On the MaxScale node, install MariaDB MaxScale.

Install on CentOS / RHEL (YUM):

Install on Debian / Ubuntu (APT):

Next Step

Navigation in the procedure "Deploy ColumnStore Shared Local Storage Topology".

This page was step 6 of 9.

Next: Step 7: Start and Configure MariaDB MaxScale.

MariaDB Corporation provides package repositories for CentOS / RHEL (YUM) and Debian / Ubuntu (APT). A download token is required to access the MariaDB Enterprise Repository.

Customer Download Tokens are customer-specific and are available through the MariaDB Customer Portal.

To retrieve the token for your account:

1. Navigate to https://customers.mariadb.com/downloads/token/

2. Log in.

3. Copy the Customer Download Token.

Substitute your token for CUSTOMER_DOWNLOAD_TOKEN when configuring the package repositories.

Set Up Repository

1. On each Enterprise ColumnStore node, install the prerequisites for downloading the software from the Web. Install on CentOS / RHEL (YUM):

Install on Debian / Ubuntu (APT):

1. On each Enterprise ColumnStore node, configure package repositories and specify Enterprise Server:

Checksums of the various releases of the mariadb_es_repo_setup script can be found in the section at the bottom of the page. Substitute ${checksum} in the example above with the latest checksum.

Install Enterprise Server and Enterprise ColumnStore

1. On each Enterprise ColumnStore node, install additional dependencies:

Install on CentOS and RHEL (YUM):

Install on Debian 9 and Ubuntu 18.04 (APT)

Install on Debian 10 and Ubuntu 20.04 (APT):

1. On each Enterprise ColumnStore node, install MariaDB Enterprise Server and MariaDB Enterprise ColumnStore:

Install on CentOS / RHEL (YUM):

Install on Debian / Ubuntu (APT):

Next Step

Navigation in the procedure "Deploy ColumnStore Object Storage Topology".

This page was step 3 of 9.

Next: Step 4: Start and Configure MariaDB Enterprise Server.

To solve this problem, MariaDB Enterprise ColumnStore provides two utilities to view and clear the table locks:

• cleartablelock

• viewtablelock

Viewing Table Locks

The viewtablelock utility shows table locks currently held by MariaDB Enterprise ColumnStore:

To view all table locks:

To view table locks for a specific table, specify the database and table:

Clearing Table Locks

The cleartablelock utility clears table locks currently held by MariaDB Enterprise ColumnStore.

To clear a table lock, specify the lock ID shown by the viewtablelock utility:

Passwords are encrypted using the cspasswd utility

Compatibility

• MariaDB Enterprise ColumnStore 6

• MariaDB Enterprise ColumnStore 22.08

• MariaDB Enterprise ColumnStore 23.02

Encryption Keys

MariaDB Enterprise ColumnStore stores its password encryption keys in the plain-text file /var/lib/columnstore/.secrets.

The encryption keys are not created by default, but can be generated by executing the cskeys utility:

In a multi-node Enterprise ColumnStore cluster, every ColumnStore node should have the same encryption keys. Therefore, it is recommended to execute cskeys on the primary server and then copy /var/lib/columnstore/.secrets to every other ColumnStore node and fix the file's permissions:

Encrypt a Password

To encrypt a password:

Generate an encrypted password using the cspasswd utility:

• If the --interactive command-line option is specified, cspasswd prompts for the password.

Set the encrypted password in Columnstore.xml using the mcsSetConfig utility:

Decrypt a Password

To decrypt a password, execute the cspasswd utility and specify the --decrypt command-line option:

For example:

Checking Replication Status with MaxScale

MaxScale is capable of checking the status of using :

• List the servers using the list servers command, like this:

If the node properly rejoined, the State column of the node shows Slave, Running.

Locking for Writes

MariaDB Enterprise ColumnStore requires a table lock for write operations.

Locking for Data Loading

MariaDB Enterprise ColumnStore requires a write metadata lock (MDL) on the table when a bulk data load is performed with cpimport.

When a bulk data load is running:

• Read queries will not be blocked.

• Write queries and concurrent bulk data loads on the same table will be blocked until the bulk data load operation is complete, and the write metadata lock on the table has been released.

• The write metadata lock (MDL) can be monitored with the .

For additional information, see "MariaDB Enterprise ColumnStore Data Loading".

Online Schema Changes

MariaDB Enterprise ColumnStore supports online schema changes, so that supported DDL operations can be performed without blocking reads. The supported DDL operations only require a write metadata lock (MDL) on the target table.

A system of record is the authoritative data source for a given piece of information. Organizations often store duplicate information in several systems, but only a single system can be the authoritative data source.

Enterprise ColumnStore is designed to handle analytical processing for OLAP, data warehousing, DSS, and hybrid workloads on very large data sets. Analytical processing does not generally happen on the system of record. Instead, analytical processing generally occurs on a specialized database that is loaded with data from the separate system of record. Additionally, very large data sets can be difficult to back up. Therefore, it may be beneficial to only backup the system of record.

If Enterprise ColumnStore is not acting as the system of record for your data, you should determine how the system of record affects your backup plan:

• If your system of record is another database server, you should ensure that the other database server is properly backed up and that your organization has procedures to reload Enterprise ColumnStore from the other database server.

• If your system of record is a set of data files, you should ensure that the set of data files is properly backed up and that your organization has procedures to reload Enterprise ColumnStore from the set of data files.

Full Backup and Restore

MariaDB Enterprise ColumnStore supports full backup and restore for all storage types. A full backup includes:

• Enterprise ColumnStore's data and metadata

With S3: an S3 snapshot of the S3-compatible object storage and a file system snapshot or copy of the Storage Manager directory Without S3: a file system snapshot or copy of the DB Root directories.

• The MariaDB data directory from the primary node

To see the procedure to perform a full backup and restore, choose the storage type:

Edit your Storage Manager configuration file located at /etc/columnstore/storagemanager.cnf in order to look similar to the example below (replacing those in the [S3] section with your own custom variables):

Cloud (IaaS)

• AWS S3

• Google GCS

Software-Based

A thumbnail diagram provides a small-scale summary of the topology's architecture.

Although multiple topologies are listed on this page, the listed topologies are not the only options. MariaDB products are flexible, configurable, and extensible, so it is possible to deploy different topologies that combine the capabilities of multiple topologies listed on this page. The topologies listed on this page are primarily intended to be representative of the most commonly requested use cases.

Transactional (OLTP)

Primary/Replica Topology

Galera Cluster Topology

Analytical (OLAP, Data Warehousing, DSS)

ColumnStore Shared Local Storage Topology

ColumnStore Object Storage Topology

Hybrid Workloads

HTAP Topology

Examples

Creating a ColumnStore Table

To create a ColumnStore table, use the statement with the ENGINE=ColumnStore option:

Multi-Node Configuration

To deploy a multi-node Enterprise ColumnStore deployment, a configuration similar to below is required:

Configure the Mandatory Utility User Account

To configure the mandatory utility user account, use the mcsSetConfig command:

MariaDB Enterprise ColumnStore performs best with Linux kernel optimizations.

On each server to host an Enterprise ColumnStore node, optimize the kernel:

1. Set the relevant kernel parameters in a sysctl configuration file. To ensure proper change management, use an Enterprise ColumnStore-specific configuration file.

Create a /etc/sysctl.d/90-mariadb-enterprise-columnstore.conf file:

1. Use the sysctl command to set the kernel parameters at runtime

Temporarily Configure Linux Security Modules (LSM)

The Linux Security Modules (LSM) should be temporarily disabled on each Enterprise ColumnStore node during installation.

The LSM will be configured and re-enabled later in this deployment procedure.

The steps to disable the LSM depend on the specific LSM used by the operating system.

CentOS / RHEL Stop SELinux

SELinux must be set to permissive mode before installing MariaDB Enterprise ColumnStore.

To set SELinux to permissive mode:

1. Set SELinux to permissive mode:

1. Set SELinux to permissive mode by setting SELINUX=permissive in /etc/selinux/config.

For example, the file will usually look like this after the change:

1. Confirm that SELinux is in permissive mode:

SELinux will be configured and re-enabled later in this deployment procedure. This configuration is not persistent. If you restart the server before configuring and re-enabling SELinux later in the deployment procedure, you must reset the enforcement to permissive mode.

Debian / Ubuntu AppArmor

AppArmor must be disabled before installing MariaDB Enterprise ColumnStore.

1. Disable AppArmor:

1. Reboot the system.

2. Confirm that no AppArmor profiles are loaded using aa-status:

AppArmor will be configured and re-enabled later in this deployment procedure.

Configure Character Encoding

When using MariaDB Enterprise ColumnStore, it is recommended to set the system's locale to UTF-8.

1. On RHEL 8, install additional dependencies:

1. Set the system's locale to en_US.UTF-8 by executing localedef:

Create an S3 Bucket

If you want to use S3-compatible storage, it is important to create the S3 bucket before you start ColumnStore. If you already have an S3 bucket, confirm that the bucket is empty.

S3 bucket configuration will be performed later in this procedure.

Next Step

Navigation in the Single-Node Enterprise ColumnStore topology with Object storage deployment procedure:

This page was step 1 of 5.

Next: Step 2: Install MariaDB Enterprise ColumnStore.

MariaDB Enterprise ColumnStore 23.10 includes a testS3Connection command to test the S3 configuration, permissions, and connectivity.

On each Enterprise ColumnStore node, test the S3 configuration:

If the testS3Connection command does not return OK, investigate the S3 configuration.

Test Local Connection

Connect to the server using using the root@localhost user account:

Test ColumnStore Plugin Status

Query and confirm that the ColumnStore storage engine plugin is ACTIVE:

Test ColumnStore Table Creation

1. Create a test database, if it does not exist:

1. Create a ColumnStore table:

1. Add sample data into the table:

1. Read data from table:

Test Cross Engine Join

1. Create an InnoDB table:

1. Add data to the table:

1. Perform a cross-engine join:

Next Step

Navigation in the Single-Node Enterprise ColumnStore topology with Object storage deployment procedure:

This page was step 4 of 5.

Next: Step 5: Bulk Import of Data.

Connect to the server using MariaDB Client using the root@localhost user account:

Test ColumnStore Plugin Status

Query and confirm that the ColumnStore storage engine plugin is ACTIVE:

Test ColumnStore Table Creation

1. Create a test database, if it does not exist:

1. Create a ColumnStore table:

1. Add sample data into the table:

1. Read data from table:

Test Cross Engine Join

1. Create an InnoDB table:

1. Add data to the table:

1. Perform a cross-engine join:

Next Step

Navigation in the Single-Node Enterprise ColumnStore topology with Local storage deployment procedure:

This page was step 4 of 5.

Next: Step 5: Bulk Import of Data.

MariaDB ColumnStore is a columnar storage engine that integrates with MariaDB Server. It employs a massively parallel distributed data architecture, making it ideal for processing petabytes of data with linear scalability. It was originally ported from InfiniDB and is released under the GPL license.

Key Benefits

• Exceptional Analytical Performance: Delivers superior performance for complex analytical queries (OLAP) due to its columnar nature, which minimizes disk I/O by reading only necessary columns.

• High Data Compression: Columnar storage allows for much higher compression ratios compared to row-based storage, reducing disk space usage and improving query speed.

• Massive Scalability: Designed to scale horizontally across multiple nodes, processing petabytes of data with ease.

• Just-in-Time Projection: Only the required columns are processed and returned, further optimizing query execution.

• Real-time Analytics: Capable of handling real-time analytical queries efficiently.

Architecture Concepts (Simplified)

MariaDB ColumnStore utilizes a distributed architecture with different components working together:

• User Module (UM): Handles incoming SQL queries, optimizes them for columnar processing, and distributes tasks.

• Performance Module (PM): Manages data storage, compression, and execution of query fragments on the data segments.

• Data Files: Data is stored in column-segments across the nodes, highly compressed.

Installation Overview

MariaDB ColumnStore is installed as a separate package that integrates with MariaDB Server. The exact installation steps vary depending on your operating system and desired deployment type (single server or distributed cluster).

General Steps (conceptual):

1. Install MariaDB Server: Ensure you have a compatible MariaDB Server version installed (e.g., MariaDB 10.5.4 or later).

2. Install ColumnStore Package: Download and install the specific MariaDB ColumnStore package for your OS. This package includes the ColumnStore storage engine and its associated tools.

   • Linux (e.g., Debian/Ubuntu): You would typically add the MariaDB repository configured for ColumnStore and then install mariadb-plugin-columnstore.

   • Single Server vs. Distributed: For a single-server setup, you install all ColumnStore components on one machine. For a distributed setup, you install and configure components across multiple machines.

3. Configure MariaDB: After installation, you might need to adjust your MariaDB server configuration (my.cnf or equivalent) to properly load and manage the ColumnStore engine.

4. Initialize ColumnStore: Run a specific columnstore-setup or post-install script to initialize the ColumnStore environment.

Basic Usage

Once MariaDB ColumnStore is installed and configured, you can create and interact with ColumnStore tables using standard SQL.

Creating a ColumnStore Table

Specify ENGINE=ColumnStore when creating your table. Note that ColumnStore tables do not support primary keys in the same way as InnoDB, as their primary focus is analytical processing.

Inserting Data

You can insert data using standard INSERT statements. For large datasets, bulk loading utilities (for instance, LOAD DATA INFILE) are highly recommended for performance.

Querying Data

Perform analytical queries. ColumnStore will efficiently process these, often leveraging its columnar nature and parallelism.

See Also

• MariaDB ColumnStore Overview

• DigitalOcean: How to Install MariaDB ColumnStore on Ubuntu 20.04

MariaDB Corporation provides package repositories for CentOS / RHEL (YUM) and Debian / Ubuntu (APT). A download token is required to access the MariaDB Enterprise Repository.

Customer Download Tokens are customer-specific and are available through the MariaDB Customer Portal.

To retrieve the token for your account:

1. Navigate to https://customers.mariadb.com/downloads/token/

2. Log in.

3. Copy the Customer Download Token.

Substitute your token for CUSTOMER_DOWNLOAD_TOKEN when configuring the package repositories.

Set Up Repository

1. On each Enterprise ColumnStore node, install the prerequisites for downloading the software from the Web. Install on CentOS / RHEL (YUM):

Install on Debian / Ubuntu (APT):

1. On each Enterprise ColumnStore node, configure package repositories and specify Enterprise Server:

Checksums of the various releases of the mariadb_es_repo_setup script can be found in the section at the bottom of the page. Substitute ${checksum} in the example above with the latest checksum.

Install Enterprise ColumnStore

Install additional dependencies:

Install on CentOS / RHEL (YUM)

Install of Debian 10 and Ubuntu 20.04 (APT):

Install on Debian 9 and Ubuntu 18.04 (APT):

Install MariaDB Enterprise Server and MariaDB Enterprise ColumnStore:

Install on CentOS / RHEL (YUM):

Install on Debian / Ubuntu (APT):

Next Step

Navigation in the Single-Node Enterprise ColumnStore topology with Object storage deployment procedure:

This page was step 2 of 5.

Next: Step 3: Start and Configure MariaDB Enterprise ColumnStore.

Before data can be imported into the tables, create a matching schema.

On the primary server, create the schema:

1. For each database that you are importing, create the database with the statement:

1. For each table that you are importing, create the table with the statement:

Import the Data

Enterprise ColumnStore supports multiple methods to import data into ColumnStore tables.

cpimport

MariaDB Enterprise ColumnStore includes cpimport, which is a command-line utility designed to efficiently load data in bulk. Alternative methods are available.

To import your data from a TSV (tab-separated values) file, on the primary server run cpimport:

LOAD DATA INFILE

When data is loaded with the LOAD DATA INFILE statement, MariaDB Enterprise ColumnStore loads the data using cpimport, which is a command-line utility designed to efficiently load data in bulk. Alternative methods are available.

To import your data from a TSV (tab-separated values) file, on the primary server use LOAD DATA INFILE statement:

Import from Remote Database

MariaDB Enterprise ColumnStore can also import data directly from a remote database. A simple method is to query the table using the statement, and then pipe the results into cpimport, which is a command-line utility that is designed to efficiently load data in bulk. Alternative methods are available.

To import your data from a remote MariaDB database:

Next Step

Navigation in the Single-Node Enterprise ColumnStore topology with Object storage deployment procedure:

This page was step 5 of 5.

This procedure is complete.

ColumnStore's extent map is a smart structure that underpins its performance. By providing a logical partitioning scheme, it avoids the overhead associated with indexing and other common row-based database optimizations.

The primary node in a ColumnStore cluster holds the master copy of the extent map. Upon system startup, this master copy is read into memory and then replicated to all other participating nodes for high availability and disaster recovery. Nodes keep the extent map in memory for rapid access during query processing. As data within extents is modified, these updates are broadcast to all participating nodes to maintain consistency.

If the master copy of the extent map becomes corrupted, the entire system could become unusable, potentially leading to data loss. Having a recent backup of the extent map allows for a much faster recovery compared to reloading the entire database in such a scenario.

Backup Procedure

To safeguard against potential Extent Map corruption, regularly back up the master copy:

1. Lock Table:

1. Save BRM:

1. Create Backup Directory:

1. Copy Extent Map:

1. Unlock Tables:

Recovery Procedures

Single-Node System

1. Stop ColumnStore:

1. Rename Corrupted Map:

1. Clear Versioning Files:

1. Restore Backup:

1. Set Ownership:

1. Start ColumnStore:

Clustered System

1. Shutdown Cluster:

1. Rename Corrupted Map:

1. Clear Versioning Files:

1. Restore Backup:

1. Set Ownership:

1. Start Cluster:

Automation Recommendation

Incorporate the save_brm command into your data import scripts (e.g., those using cpimport) to automate Extent Map backups. This practice ensures regular backups without manual intervention.

Refer to the MariaDB ColumnStore Backup Script for an example implementation.​

MariaDB Corporation provides package repositories for CentOS / RHEL (YUM) and Debian / Ubuntu (APT). A download token is required to access the MariaDB Enterprise Repository.

Customer Download Tokens are customer-specific and are available through the MariaDB Customer Portal.

To retrieve the token for your account:

1. Navigate to https://customers.mariadb.com/downloads/token/

2. Log in.

3. Copy the Customer Download Token.

Substitute your token for CUSTOMER_DOWNLOAD_TOKEN when configuring the package repositories.

Set Up Repository

1. On each Enterprise ColumnStore node, install the prerequisites for downloading the software from the Web. Install on CentOS / RHEL (YUM):

Install on Debian / Ubuntu (APT):

1. On each Enterprise ColumnStore node, configure package repositories and specify Enterprise Server:

Checksums of the various releases of the mariadb_es_repo_setup script can be found in the section at the bottom of the page. Substitute ${checksum} in the example above with the latest checksum.

Install Enterprise ColumnStore

Install additional dependencies:

Install on CentOS / RHEL (YUM)

Install of Debian 10 and Ubuntu 20.04 (APT):

Install on Debian 9 and Ubuntu 18.04 (APT):

Install MariaDB Enterprise Server and MariaDB Enterprise ColumnStore:

Install on CentOS / RHEL (YUM):

Install on Debian / Ubuntu (APT):

Next Step

Navigation in the Single-Node Enterprise ColumnStore topology with Local storage deployment procedure:

This page was step 2 of 5.

Next: Step 3: Start and Configure MariaDB Enterprise ColumnStore.

Convenience utility programs getConfig and setConfig are available to safely update the Columnstore.xml without needing to be comfortable with editing XML files. The -h argument will display usage information.

Memory Management

NumBlocksPct

TotalUmMemory

Memory Requirements

In a single server or combined deployment, the sum of NumBlocksPct and TotalUmMemory should typically not exceed 75% of physical memory. With very large memory servers this could be raised but the key point is to leave enough memory for other processes including mariadbd.

Query Concurrency - MaxOutstandingRequests

ColumnStore handles concurrent query execution by managing the rate of concurrent batch primitive steps. This is configured using the MaxOutstandingRequests parameter and has a default value of 20. Each batch primitive step is executed within the context of 1 extent column according to this high level process:

• ColumnStore issues up to MaxOutstandingRequests number of batch primitive steps.

• PrimProc processes the request, using many threads and returns its response. These generally take a fraction of a second up to a low number of seconds depending on the amount of Physical I/O and the performance of that storage.

• ColumnStore issues new requests as prior requests complete maintaining the maximum number of outstanding requests.

This scheme allows for large queries to use all available resources when not otherwise being consumed and for smaller queries to execute with minimal delay. Lower values optimize for higher throughput of smaller queries while a larger value optimizes for response time of that query. The default value should work well under most circumstances however the value should be increased as the number of nodes is increased.

How many Queries are running and how many queries are currently in the queue can be checked with

Join Tuning - PmMaxMemorySmallSide

ColumnStore maintains statistics for table and utilizes this to determine which is the larger table of the two. This is based both on the number of blocks in that table and estimation of the predicate cardinality. The first step is to apply any filters as appropriate to the smaller table and returning this data set to memory. The size of this data set is compared against the configuration parameter PmMaxMemorySmallSide which has a default value of 64 (MB). This value can be set all the way up to 4GB. This default allows for approximately 1M rows on the small table side to be joined against billions (or trillions) on the large table side. If the size of the small data set is less than PmMaxMemorySmallSide the dataset is sent to PrimProc for creation of a distributed hashmap. Thus this setting is important to tuning of joins and whether the operation can be distributed or not. This should be set to support your largest expected small table join size up to available memory:

• Although this will increase the size of data between nodes to support the join, it means that the join and subsequent aggregates are pushed down, scaled out, and a smaller data set is returned back.

• In a multiple server deployment, the sizing should be based from available physical memory on the servers, how much memory to reserve for block caching, and the number of simultaneous join operations that can be expected to run times the average small table join data size.

Multi-Table Join Tuning

The above logic for a single table join extrapolates out to multi table joins where the small table values are precalculated and performed as one single scan against the large table. This works well for the typical star schema case joining multiple dimension tables with a large fact table. For some join scenarios it may be necessary to sequence joins to create the intermediate datasets for joining, this would happen for instance with a snowflake schema structure. In some extreme cases it may be hard for the optimizer to be able to determine the most optimal join path. In this case a hint is available to force a join ordering. The INFINIDB_ORDERED hint will force the first table in the from clause to be considered the largest table and override any statistics based decision, for example:

use set infinidb_ordered_only=ON;

and for 1.4 set columnstore_ordered_only=ON;

Disk-Based Joins - AllowDiskBasedJoin

When a join is very large and exceeds the PmMaxMemorySmallSide setting, it is performed in memory. For very large joins, this could exceed the available memory, in which case this is detected and a query error reported. Several configuration parameters are available to enable and configure usage of disk overflow should this occur:

• AllowDiskBasedJoin – Controls the option to use disk Based joins or not. Valid values are Y (enabled) or N (disabled). By default, this option is disabled.

• TempFileCompression – Controls whether the disk join files are compressed or noncompressed. Valid values are Y (use compressed files) or N (use non-compressed files).

• TempFilePath – The directory path used for the disk joins. By default, this path is the tmp directory for your installation (i.e., /tmp/columnstore_tmp_files/). Files (named infinidb-join-data*) in this directory will be created and cleaned on an as-needed basis. The entire directory is removed and recreated by ExeMgr at startup. It is strongly recommended that this directory be stored on a dedicated partition.

A MariaDB global or session variable is available to specify a memory limit at which point the query is switched over to disk-based joins:

• infinidb_um_mem_limit - Memory limit in MB per user (i.e., switch to disk-based join if this limit is exceeded). By default, this limit is not set (value of 0).

For example:

If the specified server is a primary server, then MaxScale will allow open transactions to complete before closing any connections.

If you would like MaxScale to immediately close all connections, the --force option can be provided as a third argument:

Confirming Maintenance Mode is Set with MaxScale

Confirm the state of the server object in MaxScale using :

• List the servers using the list servers command, like this:

If the node is properly in maintenance mode, then the State column will show Maintenance as one of the states.

Performing Maintenance

Now that the server is in maintenance mode in MaxScale, you can perform your maintenance.

While the server is in maintenance mode:

• MaxScale doesn't route traffic to the node.

• MaxScale doesn't select the node to be primary during failover.

• The node can be rebooted.

• The node's services can be restarted.

Clear the Server State in MaxScale

Maintenance mode for the server object for the node can be cleared in MaxScale using :

• Use or another supported REST client.

• Clear the server object's state using the clear server command.

• As the first argument, provide the name for the server.

• As the second argument, provide maintenance as the state.

For example:

Confirming Maintenance Mode is Cleared with MaxScale

Confirm the state of the server object in MaxScale using :

• List the servers using the list servers command, like this:

If the node is no longer in maintenance mode, the State column no longer shows Maintenance as one of the states.

Prerequisites

This procedure assumes that the new Enterprise ColumnStore version will be installed onto new servers.

To reuse existing servers for the new Enterprise ColumnStore version, you must adapt the procedure detailed below. After step 1, confirm all data has been backed-up and verify backups. The old version of Enterprise ColumnStore should then be uninstalled, and all Enterprise ColumnStore files should be deleted before continuing with step 2.

Step 1: Backup/Export Schemas and Data

On the old ColumnStore cluster, perform a full backup.

MariaDB recommends backing up the table schemas to a single SQL file and backing up the table data to table-specific CSV files.

1. For each table, obtain the table's schema by executing the SHOW CREATE TABLE :

   Backup the table schemas by copying the output to an SQL file. This procedure assumes that the SQL file is named schema-backup.sql.

2. For each table, backup the table data to a CSV file using the SELECT .. INTO OUTFILE :

3. Copy the SQL file containing the table schemas and the CSV files containing the table data to the primary node of the new ColumnStore cluster.

Step 2: Install New Major Release

On the new ColumnStore cluster, follow the deployment instructions of the desired topology for the new ColumnStore version.

For deployment instructions, see "".

Step 3: Restore/Import Data

On the new ColumnStore cluster, restore the table schemas and data.

1. Restore the schema backup using :

   • HOST and PORT should refer to the following:

     • If you are connecting with MaxScale as a proxy, they should refer to the host and port of the MaxScale listener

     • If you are connecting directly to a multi-node ColumnStore cluster, they should refer to the host and port of the primary ColumnStore node

     • If you are connecting directly to single-node ColumnStore, they should refer to the host and port of the ColumnStore node

   • When the command is executed, mariadb client prompts for the user password

2. For each table, restore the data from the table's CSV file by executing the on the primary ColumnStore node:

Step 4: Test

On the new ColumnStore cluster, verify that the table schemas and data have been restored.

1. For each table, verify the table's definition by executing the SHOW CREATE TABLE statement:

2. For each table, verify the number of rows in the table by executing SELECT COUNT(*):

3. For each table, verify the data in the table executing the statement.

   If the table is very large, you can limit the number of rows in the result set by adding a LIMIT clause:

As you increase the number of ColumnStore nodes or the number of cores on each node, Enterprise ColumnStore can use those resources to more efficiently execute job steps.

For additional information, see "MariaDB Enterprise ColumnStore Query Evaluation.".

Batch Primitive Step (BPS)

Enterprise ColumnStore defines a batch primitive step to handle many types of tasks, such as scanning/filtering columns, JOIN operations, aggregation, functional filtering, and projecting (putting values into a SELECT list).

In calGetTrace() output, a batch primitive step is abbreviated BPS.

Batch primitive steps are evaluated on multiple nodes in parallel. The PrimProc process on each node evaluates the batch primitive step to one extent at a time. The PrimProc process uses a thread pool to operate on individual blocks within the extent in parallel.

Cross Engine Step (CES)

Enterprise ColumnStore defines a cross-engine step to perform cross-engine joins, in which a ColumnStore table is joined with a table that uses a different storage engine.

In calGetTrace() output, a cross-engine step is abbreviated CES.

Cross-engine steps are evaluated locally by the ExeMgr process on the initiator/aggregator node.

Enterprise ColumnStore can perform cross-engine joins when the mandatory utility user is properly configured.

For additional information, refer to the "Mandatory Utility User Account"

Dictionary Structure Step (DSS)

Enterprise ColumnStore defines a dictionary structure step to scan the dictionary extents that ColumnStore uses to store variable-length string values.

In calGetTrace() output, a dictionary structure step is abbreviated DSS.

Dictionary structure steps are evaluated on multiple nodes in parallel. The PrimProc process on each node evaluates the dictionary structure step to one extent at a time. It uses a thread pool to operate on individual blocks within the extent in parallel.

Dictionary structure steps can require a lot of I/O for a couple of reasons:

• Dictionary structure steps do not support extent elimination, so all extents for the column must be scanned.

• Dictionary structure steps must read the column extents to find each pointer and the dictionary extents to find each value, so it doubles the number of extents to scan.

It is generally recommended to avoid queries that will cause dictionary scans.

For additional information, see "Avoid Creating Long String Columns".

Hash Join Step (HJS)

Enterprise ColumnStore defines a hash join step to perform a hash join between two tables.

In calGetTrace() output, a hash join step is abbreviated HJS.

Hash join steps are evaluated locally by the ExeMgr process on the initiator/aggregator node.

Enterprise ColumnStore performs the hash join in memory by default. If you perform large joins, you may be able get better performance by changing some configuration defaults with mcsSetConfig:

• Enterprise ColumnStore can be configured to use more memory for in-memory hash joins.

• Enterprise ColumnStore can be configured to use disk-based joins.

For additional information, see "Configure in-memory joins" and "Configure Disk-Based Joins".

Having Step (HVS)

Enterprise ColumnStore defines a having step to evaluate a HAVING clause on a result set.

In calGetTrace() output, a having step is abbreviated HVS.

Subquery Step (SQS)

Enterprise ColumnStore defines a subquery step to evaluate a subquery.

In calGetTrace() output, a subquery step is abbreviated SQS.

Tuple Aggregation Step (TAS)

Enterprise ColumnStore defines a tuple aggregation step to collect intermediate aggregation prior to the final aggregation and evaluation of the results.

In calGetTrace() output, a tuple aggregation step is abbreviated TAS.

Tuple aggregation steps are primarily evaluated by the ExeMgr process on the initiator/aggregator node. However, the PrimProc process on each node also plays a role, since the PrimProc process on each node provides the intermediate aggregation results to the ExeMgr process on the initiator/aggregator node.

Tuple Annexation Step (TNS)

Enterprise ColumnStore defines a tuple annexation step to perform the final aggregation and evaluation of the results.

In calGetTrace() output, a tuple annexation step is abbreviated TNS.

Tuple annexation steps are evaluated locally by the ExeMgr process on the initiator/aggregator node.

Enterprise ColumnStore 5 performs aggregation operations in memory. As a consequence, more complex aggregation operations require more memory in that version.

In Enterprise ColumnStore 6, disk-based aggregations can be enabled.

For additional information, see "Configure Disk-Based Aggregations".

Tuple Union Step (TUS)

Enterprise ColumnStore defines a tuple union step to perform a union of two subqueries.

In calGetTrace() output, a tuple union step is abbreviated TUS.

Tuple union steps are evaluated locally by the ExeMgr process on the initiator/aggregator node.

Tuple Constant Step (TCS)

Enterprise ColumnStore defines a tuple constant step to evaluate constant values.

In calGetTrace() output, a tuple constant step is abbreviated TCS.

Tuple constant steps are evaluated locally by the ExeMgr process on the initiator/aggregator node.

Window Function Step (WFS)

Enterprise ColumnStore defines a window function step to evaluate window functions.

In calGetTrace() output, a window function step is abbreviated WFS.

Window function steps are evaluated locally by the ExeMgr process on the initiator/aggregator node.

When Enterprise ColumnStore performs ORDER BY and LIMIT operations, the operations are performed in a single-threaded manner after the rest of the query processing has been completed, and the full unsorted result-set has been retrieved. For large data sets, the performance overhead can be significant.

Avoid Excessive Aggregations

When Enterprise ColumnStore 5 performs aggregations (i.e., DISTINCT, GROUP BY, COUNT(*), etc.), all of the aggregation work happens in-memory by default. As a consequence, more complex aggregation operations require more memory in that version.

For example, the following query could require a lot of memory in Enterprise ColumnStore 5, since it has to calculate many distinct values in memory:

Whereas the following query could require much less memory in Enterprise ColumnStore 5, since it has to calculate fewer distinct values:

In Enterprise ColumnStore 6, disk-based aggregations can be enabled.

For best performance, avoid excessive aggregations or enable disk-based aggregations.

For additional information, see "Configure Disk-Based Aggregations".

Avoid Non-Distributed Functions

When Enterprise ColumnStore evaluates built-in functions and aggregate functions, it can often evaluate the function in a distributed manner. Distributed evaluation of functions can significantly improve performance.

Enterprise ColumnStore supports distributed evaluation for some built-in functions. For other built-in functions, the function must be evaluated serially on the final result set.

Enterprise ColumnStore also supports distributed evaluation for user-defined functions developed with ColumnStore's User-Defined Aggregate Function (UDAF) C++ API. For functions developed with Enterprise Server's standard User-Defined Function (UDF) API, the function must be evaluated serially on the final result set.

For best performance, avoid non-distributed functions.

Optimize Large Joins

By default, Enterprise ColumnStore performs all joins as in-memory hash joins.

If the joined tables are very large, the in-memory hash join can require too much memory for the default configuration. There are a couple options to work around this:

• Enterprise ColumnStore can be configured to use more memory for in-memory hash joins.

• Enterprise ColumnStore can be configured to use disk-based joins.

• Enterprise ColumnStore can use optimizer statistics to better optimize the join order.

For additional information, see "Configure In-Memory Joins", "Configure Disk-Based Joins", and "Optimizer Statistics".

Load Ordered Data in Proper Order

Enterprise ColumnStore uses extent elimination to optimize queries. Extent elimination uses the minimum and maximum values in the extent map to determine which extents can be skipped for a query.

When data is loaded into Enterprise ColumnStore, it appends the data to the latest extent. When an extent reaches the maximum number of column values, Enterprise ColumnStore creates a new extent. As a consequence, if ordered data is loaded in its proper order, then similar values will be clustered together in the same extent. This can improve query performance, because extent elimination performs best when similar values are clustered together.

For example, if you expect to query a table with a filter on a timestamp column, you should sort the data using the timestamp column before loading it into Enterprise ColumnStore. Later, when the table is queried with a filter on the timestamp column, Enterprise ColumnStore would be able to skip many extents using extent elimination.

For best performance, load ordered data in proper order.

Enable Decimal Overflow Checks

When Enterprise ColumnStore performs mathematical operations with very big values using the , , and data types, the operation can sometimes overflow ColumnStore's maximum precision or scale. The maximum precision and scale depend on the version of Enterprise ColumnStore:

• In Enterprise ColumnStore 6, the maximum precision (M) is 38, and the maximum scale (D) is 38.

• In Enterprise ColumnStore 5, the maximum precision (M) is 18, and the maximum scale (D) is 18.

In Enterprise ColumnStore 6, applications can configure Enterprise ColumnStore to check for decimal overflows by setting the columnstore_decimal_overflow_check system variable, but only when the column has a decimal precision that is 18 or more:

When decimal overflow checks are enabled, math operations have extra overhead.

When the decimal overflow check fails, MariaDB Enterprise ColumnStore raises an error with the ER_INTERNAL_ERROR error SQL, and it writes detailed information about the overflow check failure to the ColumnStore system logs.

User-Defined Aggregate Function (UDAF) C++ API

MariaDB Enterprise ColumnStore supports Enterprise Server's standard User-Defined Function (UDF) API. However, UDFs developed using that API cannot be executed in a distributed manner.

To support distributed execution of custom SQL, MariaDB Enterprise ColumnStore supports a Distributed User Defined Aggregate Functions (UDAF) C++ API:

• The Distributed User Defined Aggregate Functions (UDAF) C++ API allows anyone to create aggregate functions of arbitrary complexity for distributed execution in the ColumnStore storage engine.

• These functions can also be used as Analytic (Window) functions just like any built-in aggregate function.