PowerDNS with MySQL

Apr 30, 20170 likes1,111 views

I Goo Lee

MySQL PowerGroup Tech Seminar (2017.1) - 2.PowerDNS with MySQL (by Dong Chan. Sung) - URL : cafe.naver.com/mysqlpg

MHA MONITORING
MHA MANAGER
SLAVE
MASTER
SLAVE
MASTER
SLAVE
MASTER
Check Check
Check

THROUGH.. WHAT?
SLAVE
MASTER
App servers DB servers
?
VIP vs Domain

VIP FAILOVER (1/3)
SLAVEMASTER
10.5.5.11 10.5.5.12
10.5.5.11
App servers
I’M DIE..

VIP FAILOVER (2/3)
SLAVEMASTER
10.5.5.11 10.5.5.12
10.5.5.11
App servers
TAKE
OVER!

VIP FAILOVER (3/3)
PROS Very Very Stable, Very Very Easy
CONS Multi IDC, DR

DOMAIN FAILOVER (1/4)
SLAVEMASTER
10.5.5.11 10.5.5.12
App serversDNS
where svc.db.io?
go to 10.5.5.11
Q
A
1)
2)
3)

DOMAIN FAILOVER (2/4)
SLAVEMASTER
10.5.5.11 10.5.5.12
App serversDNS
I’M DIE..
Change svc.db.io 
from 10.5.5.11 
to 10.5.5.12

DOMAIN FAILOVER (3/4)
SLAVEMASTER
10.5.5.11 10.5.5.12
App serversDNS
where svc.db.io?
go to 10.5.5.12
Q
A

PROS No limitation IDC
CONS Difﬁcult, TTL, Socket Timeout.. etc
DOMAIN FAILOVER (4/4)

Power DNS!!https://www.powerdns.com

Open Source, Powerful function, Several Backend..

PDNS
PDNS
PDNS
SELECT * FROM RECORDS
>> A1.COM : 1.1.1.1 
>> A2.COM : 1.1.1.2 
>> A3.COM : 1.1.1.3

Domain infomation “Stored in Database”, not in File

Database? MySQL
(supports also Oracle, Postgres.. etc)

Replication, Replication, Replication
MySQL?

PDns? DNS Processing
MySQL? Data Processing

PowerDNS Daemon
Localhost MySQL Database
Synced Through Replication

PDNS
PDNS
PDNS
PDNS
R/W
RO
RO
RO
Like this..

PDNS never knows.. Changed Data from SQL thread.

REPLICATOR
PDNS
DNS
CACHE
listen binlog event
DNS BOX
a.com?
1.1.1.1
a.com: 1.1.1.1
RO
a.com -> 1.1.1.2

REPLICATOR
PDNS
DNS
CACHE
listen binlog event
DNS BOX
a.com -> 1.1.1.2
a.com: 1.1.1.1
pdns_control purge “a.com”
RO

REPLICATOR
PDNS
DNS
CACHE
listen binlog event
DNS BOX
a.com?
1.1.1.2
a.com: 1.1.1.2
RO

PowerDNS only caches queries with “no answer”, or with “exactly one”

select content,ttl,prio,type,domain_id,name  
from records where type='SOA'  
and name='chan04.mytest.io';
select content,ttl,prio,type,domain_id,name  
from records where type='SOA'  
and name='mytest.io';
select content,ttl,prio,type,domain_id,name  
from records where type='NS'  
and name='chan04.mytest.io'  
and domain_id=2;
select content,ttl,prio,type,domain_id,name  
from records  
where name='chan04.mytest.io'  
and domain_id=2;
CACHED
NOT CACHED

DNS
Cache
Query
Cache
QPS Lookup Time
ON ON 35,457 queries/sec 0.281 ms
ON OFF 23,750 queries/sec 0. 420 ms
OFF ON 23,178 queries/sec 0.430 ms
OFF OFF 8,319 queries/sec 1.201 ms
#1
#2
#3

PDNS Cache + MySQL Query Cache
EXTREME PERFORMANCE?
BUT, REPLICATOR NEED TO BE MANAGED

MySQL Query Cache Only
REASONABLE PERFORMANCE?
0.280 ms VS 0.430 ms

PDNS
PDNS PDNS
VIP - RR 
1.1.1.1
nameserver 1.1.1.1 
nameserver 1.1.2.1
IP 
1.1.2.1

CREATE PROCEDURE `SET_DOMAIN`(V_DOMAIN_PREFIX VARCHAR(64), V_IP_ADDRESS VARCHAR(32))
BEGIN
SET @DOMAIN_NAME = 'MY.DB';
SET @TTL = 0;
SET @RECORD_NAME = CONCAT(TRIM(LCASE(V_DOMAIN_PREFIX)),'.',@DOMAIN_NAME);
SET @RECORD_CONTENT = TRIM(V_IP_ADDRESS);
IF INET_ATON(@RECORD_CONTENT) IS NULL THEN
SIGNAL SQLSTATE '59000' SET MESSAGE_TEXT = 'CUSTOM ERROR';
END IF;
DELETE R FROM RECORDS R
INNER JOIN DOMAINS D ON D.ID = R.DOMAIN_ID
WHERE R.NAME = @RECORD_NAME;
INSERT INTO RECORDS (
DOMAIN_ID,
NAME,
TYPE,
CONTENT,
TTL,
PRIO,
CHANGE_DATE,
DISABLED,
ORDERNAME,
AUTH
) VALUES (
(SELECT ID FROM DOMAINS WHERE NAME = @DOMAIN_NAME),
@RECORD_NAME,
'A',
@RECORD_CONTENT,
@TTL,
0,
UNIX_TIMESTAMP(),
0,
NULL,
1
);
END$$
Example..
CHECK IP
DELETE
INSERT

So, we can use MySQL access controll
ID, Password, Host, Database, Privileges.. etc

A: I don’t know. Still working.
(In fact, not be used for internal reasons in Bank)

KAKAOBANK
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This document discusses strategies for automating remote database backups across multiple data centers. It recommends scheduling backups automatically after a queue time to use underutilized backup servers. The backup manager would select the target backup server based on its service zone, data center location, and available quota to balance load. It would also avoid using the same backup server consecutively and start backups at different times each day to improve reliability in case of failures.

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This document discusses MySQL 5.7's JSON datatype. It introduces JSON and why it is useful for integrating relational and schemaless data. It covers creating JSON columns, inserting and selecting JSON data using functions like JSON_EXTRACT. It discusses indexing JSON columns using generated columns. Performance is addressed, showing JSON tables can be 40% larger with slower inserts and selects compared to equivalent relational tables without indexes. Options for stored vs virtual generated columns are presented.

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The document describes the process of opening a TCP connection between a client and MySQL database, including the initial handshake and response packets. It then explains how the MRTE-Collector works by using message queues to capture and parse MySQL packets from the source database, and replay them to the target database using multiple SQL player threads. The MRTE-Collector publishes messages to RabbitMQ queues which routes the messages to the proper queues subscribed by MRTE-Player.

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This document provides instructions for using Filebeat, Logstash, Elasticsearch, and Kibana to monitor MySQL slow query logs. It describes installing and configuring each component, with Filebeat installed on database servers to collect slow query logs, Logstash to parse and index the logs, Elasticsearch for storage, and Kibana for visualization and dashboards. Key steps include configuring Filebeat to ship logs to Logstash, using grok filters in Logstash to parse the log fields, outputting to Elasticsearch, and visualizing slow queries and creating sample dashboards in Kibana.

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This document discusses setting up MySQL auditing using the Percona Audit Plugin and ELK (Elasticsearch, Logstash, Kibana). It describes installing and configuring the Percona Audit Plugin on MySQL servers to generate JSON audit logs. It then covers using Rsyslog or Filebeat to ship the logs to the Logstash server, and configuring Logstash to parse, enrich, and index the logs into Elasticsearch. Finally, it discusses visualizing the audit data with Kibana dashboards containing graphs and searching. The architecture involves MySQL servers generating logs, Logstash collecting and processing them, and Elasticsearch and Kibana providing search and analytics.

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