MariaDB ColumnStore is a columnar storage engine that utilizes a massively parallel distributed data architecture. It's a columnar storage system built by porting InfiniDB 4.6.7 to MariaDB and released under the GPL license.
is available as a storage engine for MariaDB Server. Before then, it is available as a separate download.
It is designed for big data scaling to process petabytes of data, linear scalability, and exceptional performance with real-time response to analytical queries. It leverages the I/O benefits of columnar storage, compression, just-in-time projection, and horizontal and vertical partitioning to deliver tremendous performance when analyzing large data sets.
Links:
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A Google Group exists for MariaDB ColumnStore that can be used to discuss ideas and issues and communicate with the community: Send email to mariadb-columnstore@googlegroups.com or use the
Bugs can be reported in MariaDB Jira: (see ). Please file bugs under the MCOL project and include the output from the if possible.
MariaDB ColumnStore is released under the GPL license.
R is a language and environment for statistical computing and graphics.
R provides a wide variety of statistical (linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, …), graphical techniques, machine learning packages and is highly extensible.
One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.
R is an integrated suite of software facilities for data manipulation, calculation, and graphical display.
It includes:
• an effective data handling and storage facility,
• a suite of operators for calculations on arrays, in particular matrices,
• a large, coherent, integrated collection of intermediate tools for data analysis,
• graphical facilities for data analysis and display either on-screen or on hardcopy, and
• a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.
Some basic notions / tips on how to use R along with MariaDB are the following:
A. The recommended R distribution is “Base R”:
B. The recommended R GUIs are RStudio Desktop, or RStudio Server:
Alternative GUIs would be:
RCode (PGM Solutions): .
“R” and “MariaDB Server” can be installed either in the same server, or in different servers, as an ODBC communication protocol will be used for the exchange of data between the two environments.
For the transfer of data between MariaDB Server and R Environment, it is recommended R's "odbc" Package:
“odbc" is a new R package available on CRAN (Since 2017-02-05), and maintained by RStudio, which is designed to comply with the DBI specification.
Tutorials on how to use R's "odbc" package can be found here:
Setting up ODBC Drivers:
"odbc" R Package:
The "odbc" package requires to have previously installed the MariaDB or MySQL ODBC connector:
For installing the "odbc" package from CRAN, execute in R:
“RMariaDB” R library, is a modern 'MariaDB' client based on 'Rcpp'.
For installing RMariaDB package through CRAN, execute the following R statement:
And for connecting to MariaDB:
There are other alternatives for data transfer between R and MariaDB:
“readr” R package, for writing / reading CSV files. To be used in MariaDB along with “LOAD DATA INFILE”.
"RODBC" R package: Robust and well-tested (Since 2000-05-24) package which enables data transfer between R and MariaDB by means of an ODBC connector:
It is slightly slower than RStudio's new "odbc" package (See benchmarks):
For bug report to the RODBC package maintainer, use the following R statement: bug.report(package = "RODBC")
Recommended resources for learning how to program in R are the following:
A recommended book for understanding the underlying statistics in the R packages is:
Rstudio Cheatsheets are a recommended and valuable resource:
Along with the following Base R reference card:
Search Engines:
Information on new R packages is regularly published in the following websites:
H2O.AI
The R Programming language has support for the H2O.ai library (), which enables to create in-memory multi-cluster GPU powered machine learning models.
For installing H2O.ai through CRAN, execute:
The following R Statements can be used for importing a MariaDB table to H2O.ai using the R Front End:
import_sql_table: "This function imports a SQL table to H2OFrame in memory".
import_sql_select: "This function imports the SQL table that is the result of the specified SQL query to H2OFrame in memory".
NOTE: Be sure to start the h2o.jar in the terminal with your downloaded JDBC driver in the classpath:
KERAS
offers an interface to , a high-level neural networks 'API'.
'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.
R LIBRARIES: CARET
A book which introduces core Machine Learning concepts:
Documentation on how to perform Text Mining in R can be found in the book "Text Mining With R":
SHINY WEB APPS
R Package makes it incredibly easy to build interactive web applications with R.
Automatic "reactive" binding between inputs and outputs and extensive prebuilt widgets make it possible to build beautiful, responsive, and powerful applications with minimal effort.
For deploy Shiny Web Applications using Open Source Alternatives, you can either use:
RMARKDOWN DOCUMENTS
Some of the most advanced R resources for fully understanding the internals and nuances of the R Programming Language are the following:
MariaDB ColumnStore is ideal for real-time analytics and complex queries on large datasets across industries.
A vignette on how to use the RODBC package can be found here: RODBC CRAN Vignette
install.packages("odbc")install.packages("RMariaDB")library(RMariaDB)
con <- dbConnect(
drv = RMariaDB::MariaDB(),
username = NULL,
password = NULL,
host = NULL,
port = 3306
)install.packages("h2o")connection_url <- "jdbc:mariadb://172.16.2.178:3306/ingestSQL?&useSSL=false"
username <- "root"
password <- "abc123"
# Whole Table:
table <- "citibike20k"
my_citibike_data <- h2o.import_sql_table(connection_url, table, username, password)
# SELECT Query:
select_query <- "SELECT bikeid FROM citibike20k"
my_citibike_data <- h2o.import_sql_select(connection_url, select_query, username, password)java -cp <path_to_h2o_jar>:<path_to_jdbc_driver_jar> water.H2OAppMariaDB Query Accelerator is an Alpha release. Do not use it in production environments. Query Accelerator works only in ColumnStore 25.10.0 and with MariaDB Enterprise Server 11.8.3+.
Query Accelerator allows MariaDB to use ColumnStore to execute queries that are otherwise executed by InnoDB. Under the hood, Columnstore:
receives a query;
searches for applicable Engine Independent statistics for InnoDB table index column;
applies RBO rule to transform its InnoDB tables into a number of UNION queries over non-overlapping ranges of a suitable InnoDB table index;
Query Accelerator improves the performance of queries that use aggregation functions, for instance SUM, AVG, MIN, MAX, and GROUP BY, where the performance overhead of pulling the data out of InnoDB can be overcome by the performance optimization of running in the ColumnStore engine.
This avoids the bottleneck/pipeline of having to move data out of InnoDB and into ColumnStore. Query Accelerator strives to parallelize data out of InnoDB, by utilizing table statistics to optimize multiple threads to data ranges on disk. If the InnoDB table in question uses an index, Query Accelerator is able to get the data much faster.
Example of a query benefitting from Query Accelerator (assuming column_a is indexed):
The effectiveness of Query Accelerator can vary depending on the type of queries you run and the specific characteristics of your database schema. Certain types of queries or configurations may not benefit from Query Accelerator, or could potentially experience decreased performance. It's essential to understand when Query Accelerator is most advantageous and when traditional InnoDB operations might be more efficient. Consider the following points to optimize query performance with Query Accelerator:
Make sure your query uses tables that are indexed, and the index key has the first integer column.
Also, run ANALYZE TABLE before running Query Accelerator.
Performance issues occur for queries like this:
InnoDB handles such comparison much better than ColumnStore in general, and in Query Accelerator, that would be even worse.
Generally, if your query takes longer than a minute in InnoDB, try Query Accelerator.
Query Accelerator has the same limitations as ColumnStore in general, in that it has a limited set of and it can handle. Therefore, be aware of
syntax or functions that Columnstore does not support;
data types ColumnStore does not support.
Edit the MariaDB configuration file (my.cnf or my.ini)
Locate (or create) the mariadb section, and add a line enabling Query Accelerator, like this:
Restart MariaDB Server for the change to take effect.
Run queries to turn on Query Accelerator
Set these parameters in a client session:
There must be engine-independent statistics for an InnoDB table index column so that it can be used for Query Accelerator.
columnstore_unstable_optimizer
enables unstable optimizer that is required for Query Accelerator RBO rule.
columnstore_select_handler
enables/disables ColumnStore processing for InnoDB tables.
columnstore_query_accel_parallel_factor
controls the number of parallel ranges to be used for Query Accelerator.
Watch out for max_connections. If you set columnstore_query_accel_parallel_factor to a high value, you may need to increase max_connections to avoid connection pool exhaustion.
There are two ways to verify Query Accelerator is being used:
Use select mcs_get_plan('rules') to get a list of the rules that were applied to the query.
Look for patterns like derived table - $added_sub_#db_name_#table_name_X in the optimized plan using select mcs_get_plan('optimized').
This example shows a SUM(x) GROUP BY y query which runs ~2.6s in InnoDB with indexes, and 3x faster via ColumnStore query acceleration ( ~0.7s ), provided there's enough CPU and a high enough parallel_factor.
In mariadb (MariaDB command-line client), run these statements:
Turn on Query Accelerator - On CLI:
In mariadb (MariaDB command-line client), run these statements:
Tail the ColumnStore log debug.log, and confirm parallel access to InnoDB:
Increase or decrease parallelism with columnstore_ces_optimization_parallel_factor. Keep in mind you need enough max_connections in MariaDB server:
Check the execution plan via EXPLAIN FORMAT=JSON. It should say Pushed select:
In future versions of Query Accelerator, those SET statements will be in stored procedures, allowing to turn Query Accelerator on and off with simpler commands.
To use Query Accelerator just for one query, you have to run those SET statements per query, not per session. Setting them per session effectively disables the MariaDB Optimizer for subsequent queries that ColumnStore cannot execute.
Log out of mariadb (MariaDB command-line client), and log in again.
In mariadb (MariaDB command-line client), run these statements:
Turn off Query Accelerator - On CLI:
Verify that mcs_get_plan shows parallel_ces, and that the detailed ColumnStore execution plan shows derived table:
SET columnstore_unstable_optimizer=ON;
SET optimizer_switch='index_merge=off,index_merge_union=off,index_merge_sort_union=off,index_merge_intersection=off,index_merge_sort_intersection=off,index_condition_pushdown=off,derived_merge=off,derived_with_keys=off,firstmatch=off,loosescan=off,materialization=on,in_to_exists=off,semijoin=off,partial_match_rowid_merge=off,partial_match_table_scan=off,subquery_cache=off,mrr=off,mrr_cost_based=off,mrr_sort_keys=off,outer_join_with_cache=off,semijoin_with_cache=off,join_cache_incremental=off,join_cache_hashed=off,join_cache_bka=off,optimize_join_buffer_size=off,table_elimination=off,extended_keys=off,exists_to_in=off,orderby_uses_equalities=off,condition_pushdown_for_derived=on,split_materialized=off,condition_pushdown_for_subquery=off,rowid_filter=off,condition_pushdown_from_having=on,not_null_range_scan=off,hash_join_cardinality=off,cset_narrowing=off,sargable_casefold=off';
SELECT c_zip, sum(c_payment_cnt) FROM test.customer_indexed GROUP BY c_zip ORDER BY c_zip ; -- 0.7ssed -i 's/^columnstore_innodb_queries_use_mcs = on/#columnstore_innodb_queries_use_mcs = on/' /etc/my.cnf.d/columnstore.cnf
systemctl restart mariadbSELECT mcs_get_plan('rules');
+-----------------------+
| mcs_get_plan('rules') |
+-----------------------+
| parallel_ces |
+-----------------------+
SELECT mcs_get_plan('optimized');
+-----------------------+
| mcs_get_plan('rules') |
+-----------------------+
...
>>From Tables
derived table - $added_sub_test_customer_indexed_0SELECT column_a, SUM(column_b) FROM innodb_table GROUP BY column_a SELECT column_a FROM tbl WHERE column_a = column_b [mariadb]
columnstore_innodb_queries_use_mcs = onSET columnstore_unstable_optimizer=ON;
SET optimizer_switch="index_merge=off,index_merge_union=off,index_merge_sort_union=off,index_merge_intersection=off,index_merge_sort_intersection=off,index_condition_pushdown=off,derived_merge=off,derived_with_keys=off,firstmatch=off,loosescan=off,materialization=on,in_to_exists=off,semijoin=off,partial_match_rowid_merge=off,partial_match_table_scan=off,subquery_cache=off,mrr=off,mrr_cost_based=off,mrr_sort_keys=off,outer_join_with_cache=off,semijoin_with_cache=off,join_cache_incremental=off,join_cache_hashed=off,join_cache_bka=off,optimize_join_buffer_size=off,table_elimination=off,extended_keys=off,exists_to_in=off,orderby_uses_equalities=off,condition_pushdown_for_derived=on,split_materialized=off,condition_pushdown_for_subquery=off,rowid_filter=off,condition_pushdown_from_having=on,not_null_range_scan=off,hash_join_cardinality=off,cset_narrowing=off,sargable_casefold=off";ANALYZE TABLE table_name PERSISTENT FOR COLUMNS (column_name) indexes();CREATE DATABASE IF NOT EXISTS test; USE test;
CREATE TABLE IF NOT EXISTS test.customer_indexed ( `c_d_id` int(2) NOT NULL, `c_w_id` int(6) NOT NULL, `c_first` varchar(16) , `c_middle` char(2) , `c_last` varchar(16) , `c_street_1` varchar(20) , `c_street_2` varchar(20) , `c_city` varchar(20) , `c_state` char(2) , `c_zip` int(5) , `c_phone` char(16) , `c_since` datetime DEFAULT NULL, `c_credit` char(2) , `c_credit_lim` decimal(12,2) DEFAULT NULL, `c_discount` decimal(4,4) DEFAULT NULL, `c_balance` decimal(12,2) DEFAULT NULL, `c_ytd_payment` decimal(12,2) DEFAULT NULL, `c_payment_cnt` int(8) DEFAULT NULL, `c_delivery_cnt` int(8) DEFAULT NULL, `c_data` varchar(500)) ENGINE=InnoDB DEFAULT CHARSET=latin1;
INSERT INTO test.customer_indexed SELECT ROUND(RAND() * 42000, 0), ROUND(RAND() * 42000, 0), substring(MD5(RAND()*1000000000),1,16), substring(MD5(RAND()),1,2), substring(MD5(RAND()*1000000000),1,16), substring(MD5(RAND()*1000000000),1,20), substring(MD5(RAND()*1000000000),1,20), substring(MD5(RAND()*1000000000),1,20), substring(MD5(RAND()),1,2), ROUND(RAND() * 42000, 0), substring(MD5(RAND()),1,16), CURRENT_TIMESTAMP - INTERVAL FLOOR(RAND() * 365 * 24 * 60 *60) SECOND, substring(MD5(RAND()),1,2), ROUND(RAND() * 9999999999, 2), ROUND(RAND() * 0, 4), ROUND(RAND() * 9999999999, 2), ROUND(RAND() * 9999999999, 2), ROUND(RAND() * 42000, 0), ROUND(RAND() * 42000, 0), substring(MD5(RAND()*1000000000),1,500) FROM seq_1_to_8000000; -- 3.5 min
ALTER TABLE test.customer_indexed ADD INDEX idx_fast (`c_zip`, `c_payment_cnt`); -- ~1.5 min
-- baseline
SELECT c_zip, sum(c_payment_cnt) FROM test.customer_indexed GROUP BY c_zip ORDER BY c_zip ; --2.6s sed -i 's/^#columnstore_innodb_queries_use_mcs = on/columnstore_innodb_queries_use_mcs = on/' /etc/my.cnf.d/columnstore.cnf
systemctl restart mariadb# In mariadb (MariaDB command-line client)
USE test;
ANALYZE table test.customer_indexed PERSISTENT FOR COLUMNS (c_zip,c_payment_cnt) indexes(); --8s
SELECT table_name, column_name, hist_type FROM mysql.column_stats WHERE table_name="customer_indexed";
SHOW VARIABLES LIKE "%columnstore_innodb_queries_use_mcs%";tail -f /var/log/mariadb/columnstore/debug.logSET columnstore_ces_optimization_parallel_factor=100;EXPLAIN FORMAT=JSON SELECT c_zip, SUM(c_payment_cnt) FROM test.customer_indexed GROUP BY c_zip ORDER BY c_zip ;
...
| {
"query_block": {
"select_id": 1,
"table": {
"message": "Pushed select"
}
}
} |
...This page is: Copyright © 2025 MariaDB. All rights reserved.
This page is: Copyright © 2025 MariaDB. All rights reserved.
This page is: Copyright © 2025 MariaDB. All rights reserved.