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Displaying posts with tag: ClickHouse (reset)
Advanced Query Analysis in Percona Monitoring and Management with Direct ClickHouse Access

In my Webinar on Using Percona Monitoring and Management (PMM) for MySQL Troubleshooting, I showed how to use direct queries to ClickHouse for advanced query analysis tasks. In the followup Webinar Q&A, I promised to describe it in more detail and share some queries, so here it goes.

PMM uses ClickHouse to store query performance data which gives us great performance and a very high compression ratio. ClickHouse stores data in column-store format so it handles denormalized data very well. As a result, all query performance data is stored in one simple “metrics” table:

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ClickHouse Versus MySQL Handling of Double Quotes

If you’re a MySQL user trying ClickHouse, one thing which is likely to surprise – and annoy you – is the handling of Double Quotes. In MySQL, you can use both double quotes and single quotes to quote strings, and as an example, these two queries are equivalent:

mysql> select * from performance_schema.global_variables where variable_name='max_connections';
+-----------------+----------------+
| VARIABLE_NAME   | VARIABLE_VALUE |
+-----------------+----------------+
| max_connections | 151            |
+-----------------+----------------+
1 row in set (0.01 sec)

mysql> select * from performance_schema.global_variables where variable_name="max_connections";
+-----------------+----------------+
| VARIABLE_NAME   | VARIABLE_VALUE |
+-----------------+----------------+
| max_connections | 151            |
+-----------------+----------------+
1 row in set (0.00 sec)

This means that many of us tend to use single quotes and double quotes …

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3 Step Migration of MySQL data to Clickhouse for faster analytics.

Recently one of our client approach Mydbops with Query slowness on a MySQL environment . They deployed the new code for generate the huge reports for the year end analytics data . After the deployment the queries were extremely slow and they struggled lot , then they approached us for the solution. After the analysis, their OLAP database as expected it was IO bound with 100% disk IOPS utilised during the report generation. So, the queries were starving for the Disk IO slows the process .

Problem statement :

  • Reports are majorly focused on two larger log tables ( emp_Report_model , emp_details ) .
  • The report generator (procedure) is using the count(*) statement to stimulate the aggregated data on each call. It is required for their business purpose .
  • Count(*) is terribly slow in MySQL ( Using MySQL 5.7 ) as it …
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ClickHouse and MySQL – Better Together

It’s been a while since I wrote about ClickHouse, there are a lot of new features that are worth mentioning, and for this, I recommend to follow the Altinity Blog but today I want to look at the improved integration of ClickHouse and MySQL.

There are two features:

  1. Using MySQL protocol and MySQL client to connect to ClickHouse
  2. Use MySQL tables to select and join with ClickHouse tables

Using MySQL Protocol

By enabling MySQL protocol in ClickHouse server, you will allow for the MySQL command line tool or applications that typically connect to MySQL to connect to ClickHouse and execute queries.

With this, keep in mind:

  • It does not automatically allow you to use MySQL SQL syntax, you still need to write queries in ClickHouse-dialect.
  • The same issue with datatypes. ClickHouse uses Int32/Int64 …
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ClickHouse and ProxySQL queries rewrite (Cross-post from ProxySQL)

MySQL query rewrite for ClickHouse using ProxySQL  Introduction

ProxySQL in September 2017 announced support for ClickHouse as backend. ProxySQL is a popular open source, high performance and protocol-aware proxy server for MySQL and its forks. ClickHouse is an open source column-oriented database management system capable of real time generation of analytical data reports using SQL queries. To support ClickHouse as a backend, ProxySQL acts as a data bridge between MySQL protocol and ClickHouse protocol, allowing MySQL clients to execute queries in ClickHouse through it. ClickHouse’s SQL query syntax is different than MySQL’s syntax, and migrating application from MySQL to ClickHouse isn’t just a matter of changing connections endpoint but it also requires modifying some queries. This needs development time, but not always possible. One of ProxySQL most widely used feature is indeed the ability of …

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How to use ProxySQL to work on ClickHouse like MySQL ?

Use ClickHouse like MySQL with ProxySQL Introduction

We have several customers on ClickHouse now for both columnar database analytics and archiving MySQL data, You can access data from ClickHouse with clickhouse-client but this involves some learning  and also limitations technically. Our customers are very comfortable using MySQL so they always preferred a MySQL client for ClickHouse query analysis and reporting, Thankfully ProxySQL works as a optimal bridge between ClickHouse and MySQL client, This indeed was a great news for us and our customers worldwide. This blog post is about how we can use MySQL client with ClickHouse.

Installation

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Database Replication from MySQL to ClickHouse for High Performance WebScale Analytics

MySQL to ClickHouse Replication 

MySQL works great for Online Transaction Processing (OLTP) systems, MySQL performance degrades with analytical queries on very large database infrastructure, I agree you can optimize MySQL query performance with InnoDB compressions but why then combine OLTP and OLAP (Online Analytics Processing Systems) when you have columnar stores which can deliver high performance analytical queries more efficiently? I have seen several companies building dedicated MySQL servers for Analytics but over the period of time they end spending more money in fine tuning MySQL for Analytics with no significant improvements, There is no point in blaming MySQL for what it is not built for, MySQL / MariaDB is any day a bad choice for columnar analytics / big data solutions.  Columnar database systems are best suited for handling large quantities of data: data stored in columns typically is easier to compress, it is …

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2019 Open Source Database Report: Top Databases, Public Cloud vs. On-Premise, Polyglot Persistence

Ready to transition from a commercial database to open source, and want to know which databases are most popular in 2019? Wondering whether an on-premise vs. public cloud vs. hybrid cloud infrastructure is best for your database strategy? Or, considering adding a new database to your application and want to see which combinations are most popular? We found all the answers you need at the Percona Live event last month, and broke down the insights into the following free trends reports:

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MariaDB Track at Percona Live

Less than one month left until Percona Live. This time the Committee work was a bit unusual. Instead of having one big committee for the whole conference we had a few mini-committees, each responsible for a track. Each independent mini-committee, in turn, had a leader who was responsible for the whole process. I led the MariaDB track. In this post, I want to explain how we worked, which topics we have chosen, and why.

For MariaDB, we had seven slots: five for 50-minutes talks, two for 25-minutes talks and 19 submissions. We had to reject two out of three proposals. We also had to decide how many topics the program should cover. My aim here was to use the MariaDB track to demonstrate as many MariaDB unique features as …

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ClickHouse Performance Uint32 vs Uint64 vs Float32 vs Float64

While implementing ClickHouse for query executions statistics storage in Percona Monitoring and Management (PMM),  we were faced with a question of choosing the data type for metrics we store. It came down to this question: what is the difference in performance and space usage between Uint32, Uint64, Float32, and Float64 column types?

To test this, I created a test table with an abbreviated and simplified version of the main table in our ClickHouse Schema.

The “number of queries” is stored four times in four different columns to be able to benchmark queries referencing different columns.  We can do this with ClickHouse because it is a column store and it works only with columns referenced by the query. This method would not be appropriate for testing on …

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