A few days ago one of our intern @mydbops reached me with a SQL query. The query scans only a row according to the execution plan. But query does not seems optimally performing.

Below is the SQL query and its explain plan. ( MySQL 5.7 )

select username, role from user_roles where username= '9977223389' ORDER BY role_id DESC LIMIT 1;

Execution plan and table structure

*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: user_roles
   partitions: NULL
         type: index
possible_keys: NULL
          key: PRIMARY
      key_len: 4
          ref: NULL
         rows: 1
     filtered: 10.00
        Extra: Using where
1 row in set, 1 warning (0.00 sec)

show create table user_roles\G
*************************** 1. row ***************************
       Table: …

When building network libraries for Node.js, as we do at work, one quite quickly comes by Node's Buffer type. A Buffer gives access to a memory region in a quite raw form, allowing to handle raw data and allowing to interpret binary streams. The Buffer interface predates ES6 TypedArrays and has some optimizations.

Two optimisations are notable:

For one the slice() method does not copy data, but returns a view on the underlying data. This makes it quite efficient to work on a window of the data, but when writing one has to be careful. Simple example:

const buffer = Buffer.from("hello"); …

Hope everyone aware about known about LVM(Logical Volume Manager) an extremely useful tool for handling the storage at various levels. LVM basically functions by layering abstractions on top of physical storage devices as mentioned below in the illustration.

Below is a simple diagrammatic expression of LVM

         sda1  sdb1   (PV:s on partitions or whole disks)
           \    /
            \  /
          Vgmysql      (VG)
           / | \
         /   |   \
      data  log  tmp  (LV:s)
       |     |    |
      xfs  ext4  xfs  (filesystems)

IOPS is an extremely important resource, when it comes to storage it defines the performance of disk. Let’s not forget PIOPS(Provisioned IOPS) one of the major selling points for AWS and other cloud vendors for production machines …

We are well aware that MySQL Group Replication is one of the faster evolving clustering Technology for MySQL. Flow Control plays a key factor in Group Replication performance and data integrity . In this blog I am going to explain about the Flow Control mechanism and How it has evolved in MySQL 8 ?

What is Flow Control ?

MySQL Group Replication / Native Async replication needs binary logs to get the data flow across the servers.

What makes the difference ?

In the MySQL Group Replication we are trying to achieve the Synchronous replication with the help of a Flow Control mechanism and transaction acknowledgments ( certification ).

Without Flow Control, the MySQL Group Replication is asynchronous replication ? Yes, consistency is lost.

Lets us consider

We have three nodes ( GR1, GR2, GR3 ) . Gr1 is the master and and other two servers ( GR2, GR3 ) are the …

MySQL has introduced the concept of functional index in MySQL 8.0.13. It is one of the much needed feature for query optimisation , we have seen about histogram in my last blog. Let us explore the functional index and its use cases.

For the below explanation, I have used a production scenario which has 16 core cpu, 32GB RAM and with MySQL version 8.0.16(Latest at the time of writing).

MySQL do support indexing on columns or prefixes of column values (length).

Example: 

mysql>show create table app_user\G
*************************** 1. row ***************************
Table: app_user
Create Table: CREATE TABLE `app_user` (
`id` int(11) NOT NULL AUTO_INCREMENT,
`ad_id` int(11) DEFAULT NULL,
`source` varchar(32) DEFAULT NULL,
`medium` varchar(32) DEFAULT NULL,
`campaign` varchar(32) DEFAULT NULL,
`timestamp` …

In MySQL 8.0.16 the optimizer has improved again! Comparisons of columns of numeric types with constant values are checked and folded or removed for invalid or out-of-rage values. The goal is to speed up query execution.

Since MySQL 5.7 one can put indexes on expressions, aka functional indexes, using generated columns. Basically you first need to use the generated column to define the functional expression, then indexed this column.

Quite useful when dealing with JSON functions, you can find an example here and the documentation there.

Starting with MySQL 8.0.13 we have now an easiest way to create functional indexes (or functional key parts as mentioned in the documentation) \o/

Let’s see how with a quick practical example.

MySQL User camp is one of the prestigious MySQL meetup happening in India for the past 6 years. Mydbops DBA’s PonSuresh and Vignesh has presented about the “JSON functions and their improvements in MySQL 8.0” at MySQL User Camp Bangalore on 13-02-2019.

JSON has been more improved a lot in MySQL 8.0 and improvements in LOB storage of MySQL boost it performance further. This presentation covers the JSON performance enhancements in MySQL 8.0 with its basic functions.

JSON improvements in MySQL 8.0 from Mydbops

Partitioning is a process in which a single larger table is split into several smaller tables (physically) and still considered as a single table.It is generally a good idea for the tables whose size is in a few 100 GB’s.

While performing select,update,delete operations in a partitioned tables, we can notice a better performance in queries .This simple process of optimization is called partition pruning in which we can avoid scanning the certain partitions which does not have any matching values based on partition key.

The following example will explain you in detail about the partition pruning

Here I have used an Amazon linux 2 machine

Hardware Info

1 core CPU
1 GB RAM 
20 GB Disk (SSD) 
maximum of 3000 IOPS

MySQL 5.7 is installed in this machine and two tables (one is not partitioned and other is partitioned) with 1.7 million records of  same data is loaded in using …

During our recent consulting with one of our client, We came across an interesting issue on RDS. The baseline is that “Low IO size on your RDS instance can affect your DB performance”.  Yes, It’s IO size, Not IOPS.

We had our production systems running on RDS MySQL with a single master, 3 replicas. All instances are of same type db.m4.4xlarge with same parameter group configuration and the disk size is 1.5 TB. According to the AWS user guide, each of these instances can support up to 4500 (sustained IOPS) guaranteed IOPS.

Find below the Write IOPS graph for all the instances.

It’s understood that Write IOPS / pattern on Master can vary when compared with Slave, due to a lot of factors like binlog row format, log writing etc. But it has to be almost similar for all the slaves given that it …