Presented at MariaDB Roadshow Paris 2017

1. What's New in
MariaDB Server 10.2
MaxScale 2.1
Max Mether
Field CTO

2. MariaDB Server 10.2

3. Summary - What’s New
Analytics SQL ● Window Functions
● Common Table Expressions (CTE)
JSON ● JSON Functions
● GeoJSON Functions
Replication ● Delayed Replication
● Restrict the speed of reading binlog from Master
● Compressed Binary Log
Database Compatibility ● Multi-Trigger Support
● CHECK Constraint Expression Support
● EXECUTE IMMEDIATE statement
● Support for DEFAULT with expressions
● DECIMAL increased from 30 to 38 digits
Storage Engine
Enhancements
● New Storage Engine MyRocks based on RocksDB from Facebook
● Enhancements from MySQL InnoDB 5.7
● Enable InnoDB NUMA interleave for InnoDB

4. Summary - What’s New
Security ● Per User Server Load Limitations
● Enforced TLS Connections
Administration ● New functions for User Management
● Enhanced Informations from EXPLAIN
● User defined variables added to Information Schema
● Binary Log based Flashback
Performance ● Enhanced Performance for creating Connections
● Indexes for Virtual Columns
● New Option to define a directory for InnoDB temporary files
Server-Internal
Optimisations
● Internal Use of MariaDB Connector/C
● Optimizer Enhancements
● Non-Locking ANALYZE TABLE
Other Enhancements ● Lifted limitations for Virtual Computed Columns
● Subquery-Support for Views
● Multiple Use of Temporary Tables in Query

5. Window Functions
• Window functions were introduced in SQL:2003. The
last expansion was in the latest version of the standard,
SQL:2011.
• A window function looks at “windows” of your data while
processing it, which improves the efficiency of query
execution.
• Identified by the OVER clause
• Window functions
–can help eliminate expensive subqueries
–can help eliminate self-joins
–make queries more readable
–make queries faster
More efficient and readable
queries, especially powerful
when analyzing data

6. Window Functions
Data Series Example
SELECT
time, value
FROM data_points
ORDER BY time;

7. Window Functions
Data Series Example
SELECT
time, value
FROM data_points
ORDER BY time;
SELECT
time, value,
avg(value) over (ORDER BY time
ROWS BETWEEN 6 PRECEDING
AND 6 FOLLOWING)
FROM data_points
ORDER BY time;

8. Window Functions
Data Series Example
SELECT timestamp, transaction_id, customer_id,
amount, (SELECT sum(amount)
FROM transactions AS t2
WHERE t2.customer_id = t1.customer_id AND
t2.timestamp <= t1.timestamp) AS
balance FROM transactions AS t1
ORDER BY customer_id, timestamp;
SELECT timestamp, transaction_id, customer_id, amount,
sum(amount) OVER (PARTITION BY customer_id
ORDER BY timestamp
ROWS BETWEEN UNBOUNDED
PRECEDING AND CURRENT ROW) AS balance
FROM transactions AS t1
ORDER BY customer_id, timestamp;
Query using Window
function
Query without
Window function

9. Window Functions
Example:
Given a set of bank
transactions,
compute the account balance
after each transaction
*Test done in a developer environment
#Rows Regular SQL
(seconds)
Regular SQL +
Index (seconds)
Window
Functions
(seconds)
10 000 0.29 0.01 0.02
100 000 2.91 0.09 0.16
1 000 000 29.1 2.86 3.04
10 000 000 346.3 90.97 43.17
100 000 000 4357.2 813.2 514.24

10. Window Functions
• ROWS and RANGE-type frames
• "Streamable" window functions:
ROW_NUMBER, RANK, DENSE_RANK
• Window functions that can be streamed once the
number of rows in partition is known:
PERCENT_RANK, CUME_DIST, NTILE
• Supported aggregate functions:
COUNT, SUM, AVG, BIT_OR, BIT_AND, BIT_XOR
– Aggregate functions with DISTINCT are not supported as
window functions.
Supported Functions

11. CTE
• A Common Table Expression is basically a temporary and
named result set, derived from a simple query
• Introduced in SQL:1999 and as CTEs
• A CTE
– is identified by a WITH clause
–Similar to derived tables in the FROM clause
–More expressive and provide cleaner code
–Can produce more efficient query plans
–Can be used with SELECT and EXPLAIN
–Can be recursive
Refer to a subquery
expression many times in a
query. A temporary table that
only exists for the duration of
a query.

12. Common Table Expression
Example
WITH sales_product_year AS (
SELECT
product,
year(ship_date) as year,
SUM(price) as total_amt
FROM
item_sales
GROUP BY
product, year
)
SELECT *
FROM
sales_product_year CUR,
sales_product_year PREV,
WHERE
CUR.product = PREV.product AND
CUR.year = PREV.year + 1 AND
CUR.total_amt > PREV.total_amt;
Use CTE
Define CTE

13. Common Table Expression
Recursive Example
WITH recursive sales_managers AS (
SELECT name, manager, roleName
FROM sales_hierarchy
WHERE manager IS NULL
UNION ALL
SELECT e.name, e.manager, e.roleName
FROM sales_hierarchy e
JOIN sales_managers m ON m.name = e.manager
)
SELECT * FROM sales_managers;
Use CTE
Define CTE

14. JSON
• MariaDB Server provides
– JSON functions which gives users the highest flexibility to
work with JSON based data
–stored in string based data types like VARCHAR.
• Storing JSON data in MariaDB Server is combining
relational data with the world of NoSQL
•MariaDB Server currently supports 24 JSON functions to
evaluate, query and modify JSON formatted strings
JSON- and GeoJSON
Functions for MariaDB

15. JSON Example
Working with JSON Data
CREATE TABLE jsontable (
id INTEGER NOT NULL PRIMARY KEY AUTO_INCREMENT,
jsonfield VARCHAR(1024),
CHECK (JSON_VALID(jsonfield)));
Field for JSON Data
Constraint to
validate JSON
format

16. JSON Example
Insert JSON Data
INSERT INTO jsontable (id,jsonfield) VALUES(NULL,
'{"category": "Software","product": "MaxScale",
"version": "2.0.0"}');
INSERT INTO jsontable (id,jsonfield) VALUES(NULL,
'{"category": "Service","service_type": "Training",
"class": "DBA"}');
SELECT * FROM jsontableG
******************* 1. row ***********************
id: 1
jsonfield: {"category": "Software","product":
"MaxScale", "version": "2.0.0"}
Insert like a string

17. JSON Example
Validating JSON Format
INSERT INTO jsontable (id,jsonfield) VALUES(NULL,
'{"category" - "Software","product": "MaxScale",
"version": "2.0.0"}');
ERROR 4025 (23000): CONSTRAINT `CONSTRAINT_1`
failed for `test`.`jsontable`
Insert non JSON
format
Validation
JSON_VALID fails

18. JSON Example
Working with JSON Data
SELECT * FROM jsontable
WHERE JSON_VALUE(jsonfield, '$.category') =
'Software';
+----+-------------------------------------------+
| id | jsonfield |
+----+-------------------------------------------+
| 1 | {"category": "Software","product":
"MaxScale", "version": "2.0.0"} |
+----+-------------------------------------------+
1 row in set (0.02 sec)
look for specific
value

19. Are JSON Queries efficient?
EXPLAIN SELECT * FROM jsontable WHERE
JSON_VALUE(jsonfield, '$.category') = 'Software' G
******************** 1. row **********************
id: 1
select_type: SIMPLE
table: jsontable
type: ALL
possible_keys: NULL
key: NULL
key_len: NULL
ref: NULL
rows: 2
Extra: Using where
1 row in set (0.10 sec)
All Queries are full
table scans

20. Indexing through a Virtual Column
ALTER TABLE jsontable
ADD category VARCHAR(20) AS
(JSON_VALUE(jsonfield, '$.category'));
ALTER TABLE jsontable ADD INDEX(category);
Add a virtual column
Create index on
virtual column

21. Are the indexed Queries efficient?
EXPLAIN SELECT * FROM jsontable WHERE
JSON_VALUE(jsonfield, '$.category') = 'Software' G
******************** 1. row **********************
id: 1
select_type: SIMPLE
table: jsontable
type: ref
possible_keys: category
key: category
key_len: 23
ref: const
rows: 1
Extra: Using where
1 row in set (0.10 sec)
The index is being
used!!!

22. GeoJSON
• With MariaDB Server 10.2 GeoJSON functions have been
added to the existing functions used to work with spatial
data types, like POINT, LINESTRING, POLYGON
• ST_AsGeoJSON is used to convert a geometric data type
into a GeoJSON format
• ST_GeomFromGeoJSON is used to convert a GeoJSON
based description into a geometric format
• The GeoJSON format follows the open standard for
encoding geometric features - http://geojson.org
GeoJSON functions for
converting a geometry to a
GeoJSON element or vise
versa

23. New Replication
Features
• Delayed Replication
–allows specifying a slave to lag behind the master
–CHANGE MASTER TO master_delay=3600;
• Restrict the speed of reading binlog from Master
–The option read_binlog_speed_limit can be used to
restrict the speed to read the binlog from the master
–Reduces the load on the master when slaves need to
catch up
• Compressed Binary Log
–Reduced Binary Log size and network traffic
–Binary Log events are compressed on the master
–The slave IO thread is uncompressing them before writing
them into the Relay Log
–Compression is controlled by log_bin_compress and
log_bin_compress_min_len
New Replication Features
help to reduce load on Master,
disk space, network
bandwidth usage

24. Database
Compatibility
• Multi-Trigger Support per Type
• CHECK constraints allow data validation per column
• EXECUTE IMMEDIATE for dynamic SQL combines
prepare, execute and deallocate prepare into one
action
• DECIMAL increased from 30 to 38 digits
• Support of expressions for DEFAULT
Increased Database
Compatibility by reducing
limitation based workflows
can now be built based on
triggers

25. CHECK Constraints
Examples
CREATE TABLE t1 (a INT CHECK (a>2), b INT CHECK (b>2),
CONSTRAINT a_greater CHECK (a>b));
CREATE TABLE t2 (name VARCHAR(30) CHECK
(CHAR_LENGTH(name)>2), start_date DATE,
end_date DATE CHECK (start_date IS NULL OR end_date IS
NULL OR start_date<end_date));
CREATE TABLE jsontable (
id INTEGER NOT NULL PRIMARY KEY AUTO_INCREMENT,
jsonfield VARCHAR(1024),
category VARCHAR(20) as
(JSON_VALUE(jsonfield,'$.category')),
KEY jsonkey (category),
CHECK (JSON_VALID(jsonfield)));
Numeric constraints
and comparisons
Date comparisons and
character length
Validation by using
functions

26. Enhancements
from MySQL
InnoDB 5.7
• Indexes for spatial data types
• VARCHAR size can be increased using an in-place ALTER
TABLE, if the length stays between 0 and 255 or higher than
255
• improved DDL performance for InnoDB temporary tables
• InnoDB internal improvements and better control via
parameters
• On Fusion-io Non-Volatile Memory (NVM) file systems
the doublewrite buffer is automatically disabled for system
tablespace files
• Optimized crash recovery (tablespace discovery)
MySQL Server 5.7 has
introduced enhancements to
InnoDB, some selected
changes have been merged to
MariaDB Server

27. MyRocks Storage
Engine
•For workloads requiring higher compression and IO
efficiency
• Higher performance for web scale type applications
• LSM (Log-structured merge) architecture allows very
efficient data ingestion
• Extract of the available features
–Column Families
–Transactions and WriteBatchWithIndex
–Backup and Checkpoints
–Merge Operators
–Manual Compactions Run in Parallel with Automatic
Compactions
–Persistent Cache
–Bulk loading
–Delete files in range
–Pin iterator key/value
MyRocks in MariaDB,
compression and IO efficiency
based on Facebook’s
MyRocks.

28. Performance
Optimizations
• Enhanced Performance for creating Connections
– The way to create new connections has been optimized
– This is especially good for applications, which are
using non-persistent connections
• Indexes for Virtual Columns
– Before MariaDB Server 10.2 indexes could not be
defined for virtual columns
– With supporting indexes, virtual columns also can be
used efficiently for searching
• New Option to define a directory for InnoDB
temporary files
– By using a separate directory for InnoDB temporary
files separate disks and types of disks can be used,
which is reducing IO waits
Several changes have been
added to MariaDB Server to
improve Performance

29. New Security
Options
•Per User Server Load Limitations
–Limit to the number of queries, updates or
connections the user can place or make per hour.
–Limit to the number of simultaneous
connections that the user can hold
•Enforced TLS connections
–SSL/TLS encryption options have been introduced
for users, permitting only encrypted connections for a
user
–CREATE and ALTER USER now include the optional
parameters SSL, X509, ISSUER, SUBJECT, CIPHER
• Binary log file encryption

30. Security Syntax Enhancements
Examples
CREATE USER foo
WITH MAX_QUERIES_PER_HOUR 10
MAX_UPDATES_PER_HOUR 20
MAX_CONNECTIONS_PER_HOUR 30
MAX_USER_CONNECTIONS 40;
CREATE USER 'foo4'@'test'
REQUIRE ISSUER 'foo_issuer'
SUBJECT 'foo_subject'
CIPHER 'text;
Per User Server
Load Limitation
Enforced TLS
Connections

31. New options for
DBAs
• New functions for User Management
– ALTER USER
– SHOW CREATE USER
• Enhanced Informations from EXPLAIN
– EXPLAIN FORMAT=JSON for detailed
information on sort_key an outer_ref_condition
• User defined variables added to Information Schema
– Information schema plugin to report all user defined
variables via the Information Schema
USER_VARIABLES Table
• Binary Log based Flashback
– The MariaDB Flashback feature allows DBAs to roll
back instances, databases or tables to a given
timestamp

32. Summary - What’s New
Analytics SQL ● Window Functions
● Common Table Expressions (CTE)
JSON ● JSON Functions
● GeoJSON Functions
Replication ● Delayed Replication
● Restrict the speed of reading binlog from Master
● Compressed Binary Log
Database Compatibility ● Multi-Trigger Support
● CHECK Constraint Expression Support
● EXECUTE IMMEDIATE statement
● Support for DEFAULT with expressions
● DECIMAL increased from 30 to 38 digits
Storage Engine
Enhancements
● New Storage Engine MyRocks based on RocksDB from Facebook
● Enhancements from MySQL InnoDB 5.7
● Enable InnoDB NUMA interleave for InnoDB

33. Summary - What’s New
Security ● Per User Server Load Limitations
● Enforced TLS Connections
Administration ● New functions for User Management
● Enhanced Informations from EXPLAIN
● User defined variables added to Information Schema
● Binary Log based Flashback
Performance ● Enhanced Performance for creating Connections
● Indexes for Virtual Columns
● New Option to define a directory for InnoDB temporary files
Server-Internal
Optimisations
● Internal Use of MariaDB Connector/C
● Optimizer Enhancements
● Non-Locking ANALYZE TABLE
Other Enhancements ● Lifted limitations for Virtual Computed Columns
● Subquery-Support for Views
● Multiple Use of Temporary Tables in Query

34. MariaDB Server 10.3
• Sequences & PL/SQL
• System Versioned Tables
– As Of (Point in Time Querying)
• User-defined Aggregate Functions
• Intersect & Except
• Hidden Columns

35. What’s New in MariaDB
MaxScale 2.1

36. What is new in MariaDB MaxScale 2.1 ?
• Performance
– Up to 2.8x improvement over MaxScale 2.0
• Security
– Encrypted binlog server files
– SSL between binlog server and Master/Slave
– LDAP/GSSAPI Authentication support
– Selective Data Masking
– Maximum rows returned limit
– Prepared Statement filtering by database firewall
• Scalability
– Aurora Cluster support
– Consistent Critical Read with Master Pinning
– Query Cache Filter
– Streaming Insert Plugin
• Ease of use
– Dynamic Configuration of server, monitor, listener and firewall rules

37. Up to 2.8 x Performance
Gain
MaxScale 2.1 Performance Gain
Read Only
Read Write
QPSQPS
Number of Connections

38. Security in MaxScale 2.1
● Existing in MaxScale 2.0
○ End to End SSL for transport layer security between applications, proxy & databases
○ Black and white list with Database Firewall Filter for SQL Injection protection
○ Connection Rate Limitation for DDoS protection
● New in MaxScale 2.1
○ Encrypted binlog server files
○ SSL between binlog server and Master/Slave
○ LDAP/GSSAPI Authentication support
○ Selective Data Masking
○ Maximum rows returned limit
○ Prepared Statement and Functions filtering by database firewall

39. Aurora Cluster Monitoring and Routing
● Aurora Cluster Monitor
○ Detect read replicas and write node in Aurora Cluster
○ Supports launchable scripts on monitored events like
other monitors
○ Read-Write Splitting, Read Connect Routing, Schema
Routing now can be used for Aurora Cluster
Read
Write
Primary Read Replicas
MaxScale

40. ● Master Pinning: Consistent Critical Read Filter
○ Detects a statement that would modify the database and route all subsequent statement
to the master server where data is guaranteed to be in a up-to-date state
Consistent Critical Read

41. ● Query Cache Filter
○ Cache query results in MaxScale for configurable timeout
○ For the configurable timeout, return results from cache before
being refreshed from to server
○ Configured like any other filter. caching rules can be
specified in a separate json-file
Query Cache
In memory LRU cache.
MaxScale
Clients
Database Servers

42. ● Streaming Insert Plugin
○ Convert all INSERT statements done inside an explicit transaction into LOAD DATA LOCAL
INFILE
○ Useful for client applications doing many inserts within a transaction
○ Improves performance of data load
Batch Insert
INSERT INTO test.t1 VALUES
(“John Doe”, 1234, “1234-5678-ABCD”),
(“Jane Doe”, 4321, “8765-4321-DCBA”);
“John Doe”, 1234, “1234-5678-ABCD”
“Jane Doe”, 4321, “8765-4321-DCBA”
LOAD DATA LOCAL INFILE ‘maxscale.data’ INTO TABLE
test.t1 FIELDS TERMINATED BY ‘,’ LINES TERMINATED BY
‘n’;

43. Ease of Use
● Dynamically configure server, listener, monitor
○ Add/Remove new server to configuration
○ Add/Remove new listener to configuration
○ Add/Remove monitor to configuration
○ No MaxScale restart require
● Dynamically configure database firewall rules
○ Modify firewall rules while MaxScale is running
● Support for text wildcards in hostnames
● New global setting log_throttling to throttle flooding of log file

44. Thank you
Max Mether
max@mariadb.com