Roger Hosto

Author: webgeek

  • MySQL Error: 1062 'Duplicate entry' Error

    The all too common MySQL ‘Duplicate entry’ Error.

    mysql> show slave status\G;
*************************** 1. row ***************************
               Slave_IO_State: Waiting for master to send event
                  Master_Host: master-mysql.local
                  Master_User: repl
                  Master_Port: 3306
                Connect_Retry: 60
              Master_Log_File: mysql-bin.004768
          Read_Master_Log_Pos: 1022786917
               Relay_Log_File: relay-bin.001728
                Relay_Log_Pos: 929659721
        Relay_Master_Log_File: mysql-bin.004768
             Slave_IO_Running: Yes
            Slave_SQL_Running: No
              Replicate_Do_DB: 
          Replicate_Ignore_DB: information_schema,mysql
           Replicate_Do_Table: 
       Replicate_Ignore_Table:
      Replicate_Wild_Do_Table: 
  Replicate_Wild_Ignore_Table: 
                   Last_Errno: 1062
                   Last_Error: Error 'Duplicate entry 'xyz' for key 'PRIMARY'' on query. Default database: 'db'. Query: 'INSERT INTO  data  (   id,   version ) VALUES  (279598012, 5)'
                 Skip_Counter: 0
          Exec_Master_Log_Pos: 929659575
              Relay_Log_Space: 1022787256
              Until_Condition: None
               Until_Log_File: 
                Until_Log_Pos: 0
           Master_SSL_Allowed: No
           Master_SSL_CA_File: 
           Master_SSL_CA_Path: 
              Master_SSL_Cert: 
            Master_SSL_Cipher: 
               Master_SSL_Key: 
        Seconds_Behind_Master: NULL
Master_SSL_Verify_Server_Cert: No
                Last_IO_Errno: 0
                Last_IO_Error: 
               Last_SQL_Errno: 1062
               Last_SQL_Error:Error 'Duplicate entry 'xyz' for key 'PRIMARY'' on query. Default database: 'db'. Query: 'INSERT INTO  data  (   id,   version ) VALUES  (279598012, 5)'
  Replicate_Ignore_Server_Ids: 
             Master_Server_Id: 10147115
1 row in set (0.00 sec)

    The easy way to deal with this is to verify this it is a replication entry and running the following command.

    mysql> SET GLOBAL SQL_SLAVE_SKIP_COUNTER=1; START SLAVE;

    However, if you have what seems to be a large number of duplicate entry error and don’t feel like skipping the entries one by one or you just don’t want replication to stop for this error. You can add the following to the /etc/my.cnf

    slave-skip-errors = 1062

    And restart the MySQL services. This will skip all the Duplicate entry errors until it’s removed and the MySQL is restarted.

    Keep in mind, that this error can indicate other issues with the MySQL service or system, before skipping the error completely.

  • Installing MariaDB 10.1 on CentOS 6.8

    MariaDB is a fork of the MySQL; it is notable for being led by the original developers of MySQL and is community-developed. The original developers forked it due to concerns over its acquisition by Oracle.

    MariaDB intends to be a “drop-in” replacement for MySQL, ensuring capability with library binary and matching with MySQL APIs and commands. Making it extremely easy for current MySQL User/Administrator to switch over with little to no difference in how they use it.

    It includes the XtraDB storage engine an enhanced version of the InnoDB storage engine. XtraDB is designed to better scale on modern hardware and includes a variety of other features useful in high-performance environments. To top it off XtraDB is backwards compatible with the standard InnoDB, make it a good “drop-in” replacement.

    Installational is pretty straight forward and very similar to installing MySQL. I prefer to install package with yum. So the first thing it to add the MariaDB yum repo.

    Pick your favorite editor and added the following file.
    /etc/yum.repos.d/MariaDB.repo

    # MariaDB 10.1 CentOS repository list - created 2017-03-03 18:33 UTC
# http://downloads.mariadb.org/mariadb/repositories/
[mariadb]
name = MariaDB
baseurl = http://yum.mariadb.org/10.1/centos6-amd64
gpgkey=https://yum.mariadb.org/RPM-GPG-KEY-MariaDB
gpgcheck=1

    Now run the following.

    [rhosto@localhost ~]$ sudo yum clean all

[rhosto@localhost ~]$ sudo yum install MariaDB-server MariaDB-client

    Now we can start the service.

    [rhosto@localhost ~]$ sudo service mysql start

    Next I strongly recommend running ‘/usr/bin/mysql_secure_installation’. Which will set the MariaDB root user password and give you the option of removing the test databases and anonymous user created by default.

    [rhosto@localhost ~]$ sudo /usr/bin/mysql_secure_installation

NOTE: RUNNING ALL PARTS OF THIS SCRIPT IS RECOMMENDED FOR ALL MariaDB
SERVERS IN PRODUCTION USE! PLEASE READ EACH STEP CAREFULLY!

In order to log into MariaDB to secure it, we'll need the current
password for the root user. If you've just installed MariaDB, and
you haven't set the root password yet, the password will be blank,
so you should just press enter here.

Enter current password for root (enter for none):
OK, successfully used password, moving on...

Setting the root password ensures that nobody can log into the MariaDB
root user without the proper authorisation.

Set root password? [Y/n] y
New password:
Re-enter new password:
Password updated successfully!
Reloading privilege tables..
... Success!


By default, a MariaDB installation has an anonymous user, allowing anyone
to log into MariaDB without having to have a user account created for
them. This is intended only for testing, and to make the installation
go a bit smoother. You should remove them before moving into a
production environment.

Remove anonymous users? [Y/n] Y
... Success!

Normally, root should only be allowed to connect from 'localhost'. This
ensures that someone cannot guess at the root password from the network.

Disallow root login remotely? [Y/n] Y
... Success!

By default, MariaDB comes with a database named 'test' that anyone can
access. This is also intended only for testing, and should be removed
before moving into a production environment.

Remove test database and access to it? [Y/n] Y
- Dropping test database...
... Success!
- Removing privileges on test database...
... Success!

Reloading the privilege tables will ensure that all changes made so far
will take effect immediately.

Reload privilege tables now? [Y/n] Y
... Success!

Cleaning up...

All done! If you've completed all of the above steps, your MariaDB
installation should now be secure.

Thanks for using MariaDB!

    Now verify that it will startup on reboot.

    [rhosto@localhost ~]$ sudo chkconfig --list mysql
mysql 0:off 1:off 2:on 3:on 4:on 5:on 6:off

    And you are good to go.

    [rhosto@localhost ~]$ mysql -u root -p
Enter password:
Welcome to the MariaDB monitor. Commands end with ; or \g.
Your MariaDB connection id is 11
Server version: 10.1.21-MariaDB MariaDB Server

Copyright (c) 2000, 2016, Oracle, MariaDB Corporation Ab and others.

Type 'help;' or '\h' for help. Type '\c' to clear the current input statement.

MariaDB [(none)]>

  • Linux Mint

    Linux Mint - From freedom came elegance
    Linux Mint

    Linux Mint is a community-driven Linux distribution based on the popular Ubuntu and Debian. Initially released on August 10, 2006, and with ten years to grow, it has become one top desktop Linux distribution.

    Linux Mint Desktop Screenshot

    To do to this Linux Mint provides some proprietary software and comes bundled with a variety of free and open-source applications. Release, however, they have back off a little with some of the proprietary software being installed by default. It

    Some of the reasons for the success of Linux Mint are:

    • It works out of the box, with full multimedia support and is extremely easy to use.
    • It’s both free of cost and open source.
    • It’s community-driven. Users are encouraged to send feedback to the project so that their ideas can be used to improve Linux Mint.
    • Based on Debian and Ubuntu, it provides about 30,000 packages and one of the best software managers.
    • It’s safe and reliable. Thanks to a conservative approach to software updates, a unique Update Manager and the robustness of its Linux architecture, Linux Mint requires little maintenance.
    Linux Mint 18.1 VirtualBox Motherboard settings.

    To test Linux Mint out, I installed it on a VirtualBox VM with 1 CPU with 4G RAM and 20G of storage. I enabled 3D Acceleration and bumped the Video memory up to 128M.

    Linux Mint 18.1 VirtualBox Display Settings.

    Installation is pretty straight forward and easy to do. Linux Mint can be run from a Live-DVD or booted it from a USB flash drive on any PC capable of booting from a USB drive, with the option of saving settings to the flash drive. I just download the iso and attached it as an optical drive on my VM.

    The look and feel are immaculate and easy to use, most of the desktop applications that you would need are already installed. You have the LibreOffice Suite, Firefox, Thunderbird Mail, and GIMP. Out of the box, I would say it’s a very functional desktop.

  • Querying Apache Hadoop Resource Manager with Python.

    Querying Apache Hadoop Resource Manager with Python.

    I was recently asked to write a script that would monitor the running application on the Apache Hadoop Resource Manager.

    I wonder over to the Apache Hadoop Cluster Application Statistics API. The API allows to query most of the information that you see in the WEB UI. Information such as status on the cluster, metrics on the cluster, scheduler information, information about nodes in the cluster, and information about applications on the cluster.

    I first start by querying the cluster info.

    import urllib2
import json

resource_manager = 'http://resourcemanager:8088'

info_url = resource_manager+"/ws/v1/cluster/info"

request = urllib2.Request(info_url)

'''
If you prefer to work with xml replace json below with xml
'''
request.add_header('Accept', 'application/json')

response = urllib2.urlopen(request)
data = json.loads(response.read())

print json.dumps(data, sort_keys=True, indent=4, separators=(',', ': '))

    returns the following:

    {
"clusterInfo": {
"haState": "ACTIVE",
"hadoopBuildVersion": "2.6.0-cdh5.7.0 from c00978c67b0d3fe9f3b896b5030741bd40bf541a by jenkins source checksum b2eabfa328e763c88cb14168f9b372",
"hadoopVersion": "2.6.0-cdh5.7.0",
"hadoopVersionBuiltOn": "2016-03-23T18:36Z",
"id": 1478120586043,
"resourceManagerBuildVersion": "2.6.0-cdh5.7.0 from c00978c67b0d3fe9f3b896b5030741bd40bf541a by jenkins source checksum deb0fdfede32bbbb9cfbda6aa7e380",
"resourceManagerVersion": "2.6.0-cdh5.7.0",
"resourceManagerVersionBuiltOn": "2016-03-23T18:43Z",
"rmStateStoreName": "org.apache.hadoop.yarn.server.resourcemanager.recovery.NullRMStateStore",
"startedOn": 1478120586043,
"state": "STARTED"
}
}

    Now onto what I need to do, querying the Resource Manager about running applications. The Cluster Applications API allow you to collect information on resources, which represents an application. There are multiple parameters that can be specified to retrieve data. For a list of parameters go to Cluster_Applications_API

    I however just need the information on running applications. Which looks something like.

    import urllib2
import json

resource_manager = 'http://dvcdhnn02:8088'

info_url = resource_manager+"/ws/v1/cluster/apps?states=running"

request = urllib2.Request(info_url)

'''
If you prefer to work with xml replace json below with xml
'''
request.add_header('Accept', 'application/json')

response = urllib2.urlopen(request)
data = json.loads(response.read())

print json.dumps(data, sort_keys=True, indent=4, separators=(',', ': '))

    which returns something like:

    {
"apps": {
"app": [
{
"allocatedMB": 24576,
"allocatedVCores": 3,
"amContainerLogs": "http://resourcemanager:8042/node/containerlogs/container_1478120586043_15232_01_000001/hdfs",
"amHostHttpAddress": "resourcemanager:8042",
"applicationTags": "",
"applicationType": "MAPREDUCE",
"clusterId": 1478120586043,
"diagnostics": "",
"elapsedTime": 18009,
"finalStatus": "UNDEFINED",
"finishedTime": 0,
"id": "application_1478120586043_15232",
"logAggregationStatus": "NOT_START",
"memorySeconds": 431865,
"name": "SELECT 1 AS `number_of_records...TIMESTAMP))(Stage-1)",
"numAMContainerPreempted": 0,
"numNonAMContainerPreempted": 0,
"preemptedResourceMB": 0,
"preemptedResourceVCores": 0,
"progress": 54.07485,
"queue": "root.hdfs",
"runningContainers": 3,
"startedTime": 1479156085020,
"state": "RUNNING",
"trackingUI": "ApplicationMaster",
"trackingUrl": "http://resourcemanager:8088/proxy/application_1478120586043_15232/",
"user": "hdfs",
"vcoreSeconds": 51
}
]
}
}

    straight forward and simple to use.

  • Resizing InnoDB Logs

    If you have already created your database and you change the setting for “innodb_log_file_size=###M” and restart here you database and get an error that looks something like

    InnoDB: Error: log file ./ib_logfile0 is of different size 0 5242880 bytes

    Here what you need to do.
    1.) Make sure your database shutdown clean.
    2.) Move(not delete) any existing ib_logfile[#] to a safe place.
    3.) edit the “innodb_log_file_size=###M” setting in your my.cnf.
    4.) Restart your database and check your log file to make sure there were no errors.
    5.) Check to make sure the new ib_logfile[#] are the right size.

  • MongoDB Script for counting records in collections in all the databases

    Here is a quick script. I wrote for a co-worker.

    var host = "localhost"
var port = 27000
var dbslist = db.adminCommand('listDatabases');

for( var d = 0; d < dbslist.databases.length; d++) {
     var db = connect(host+":"+port+"/"+dbslist.databases[d].name);
     var collections = db.getCollectionNames();
     for(var i = 0; i < collections.length; i++){
         var name = collections[i];
         if(name.substr(0, 6) != 'system') {
            print("\t"+dbslist.databases[d].name+"."+name + ' = ' + db[name].count() + ' records');
         }
     }
}

     

  • Apache Oozie – Shell Script Example.

    Recently I needed the ability to allow a user to submit jobs that required them to pass arguments to a shell script. While it’s easy enough to submit a job using a Web UI like HUE. I wanted to tie it to a homegrown SaaS solution that we were developing to allow developers to load datasets into a database for testing.

    Since I was already using Hadoop and Sqoop to store and load the datasets, and I didn’t want to reinvent the wheel, I decided to use Oozie that I had already installed to handle some of the Hadoop ETL jobs.

    I started off by creating a working directory on HDFS.

    hdfs dfs mkdir -p /user/me/oozie-scripts/OozieTest

    Next I created a simple shell script that take two parameters. For testing, I decided to use curl to retrieve a CSV from google.com and then copy it to HDFS. Keep mind that any application that you use in your shell script needs to be install on your data nodes.

    #!/bin/bash
    /usr/bin/curl -i “$1″  -o $2
    /usr/bin/hdfs dfs -copyFromLocal $2 $2

    Now I copied the script to the working directory on HDFS.

    shell> hdfs dfs copyFromLocal GetCSVData.sh /usr/me/oozie-scripts/OozieTest

    Next I created a simple workflow.xml template to handle the Oozie job. Oozie requires it to be name workflow.xml. This defines the actions and parameters for the actions.

    <workflow-app name=”GetCSVData” xmlns=”uri:oozie:workflow:0.4″>
    <start to=”GetCSVData”/>
    <action name=”GetCSVData”>
    <shell xmlns=”uri:oozie:shell-action:0.1″>
    <job-tracker>${jobTracker}</job-tracker>
    <name-node>${nameNode}</name-node>
    <exec>GetCSVData.sh</exec>
    <argument>${url}</argument>
    <argument>${output}</argument>
    <file>/user/root/oozie-scripts/OozieTest/GetCSVData.sh#GetCSVData.sh</file>
    </shell>
    <ok to=”end”/>
    <error to=”kill”/>
    </action>
    <kill name=”kill”>
    <message>Action failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
    </kill>
    <end name=”end”/>
    </workflow-app>

    The important part of this is <shell xmlns=”uri:oozie:shell-action:0.1″>. Which defines the type of action and the requirements for it.

    The sets which job tracker to run the job <job-tracker>${jobTracker}</job-tracker>

    The name-node where everything is stored <name-node>${nameNode}</name-node>

    The name of the Shell script to be executed <exec>GetCSVData.sh</exec>

    The first argument to pass to the shell script <argument>${url}</argument>

    The second argument to pass to the shell script <argument>${output}</argument>

    The location of the shell script on HDFS <file>/user/me/oozie-scripts/OozieTest/GetCSVData.sh#GetCSVData.sh</file>

    Now will we need to create a properties file named “oozietest.properties” for submitting the Oozie job. This will basically fill in the all the variables for the workflow.xml.

    oozie.wf.application.path=hdfs://localhost:8020/user/me/oozie-scripts/OozieTest
    jobTracker=localhost:8032
    nameNode=hdfs://localhost:8020
    url=http://www.google.com/finance/historical?q=NYSE%3ADATA&ei=TH0mVsrWBce7iwLE86_ABw&output=csv
    output=/tmp/DATA.csv

    The oozie.wf.application.path is the working directory on HDFS that has the workflow.xml. Where as the rest are key value pairs to fill in the value.

    Now all we need to do is submit job.

    shell> oozie job -oozie http://localhost:11000/oozie -config oozietest.properties -run

    The should generate a job id which we can use to check the status of the job we submitted.

    shell> oozie job -oozie http://dbz-datarepo-app02:11000/oozie -info [JOB_ID]

    for more information check out Apache Oozie.

  • Installing MonetDB on CentOS/RedHat.

    Hello world!

  • Disable 70-persistent-net.rules generation on CentOS 6 VM

    If you’re like me you probably have an environment that is running on some virtual platform and like everyone else you have built a template to spin Linux systems. One of the things lately we were running into was the “70-persistent-net.rules”, which associated MAC address to Network interfaces.

    The easiest way I have found to disable this was to do the following, it’s not pretty but works.

    rm /etc/udev/rules.d/70-persistent-net.rules

    echo “#” > /lib/udev/rules.d/75-persistent-net-generator.rules

    Happy hacking.

  • Connecting Tableau to DataStax Cassandra with Cassandra CQL ODBC.

    Recently, I did some testing with Tableau Desktop connecting to DataStax Cassandra using their newly released DataStax ODBC driver. Before the release of the DataStax ODBC driver, the only way to connect Tableau Desktop to DataStax was the DataStax Enterprise Connector (a.k.a. Hive Thrift Server).

    While Hive is a great analytic tool, it is somewhat slow. When you are trying to load data for a report, which should be relatively quick, Hive can take a little time to get up to speed. I’m sure everyone that has used Hive knows what I’m talking about.

    I would highly recommend downloading and installing the DataStax ODBC driver.

    There is a nice little how-to on the DataStax blog @ http://www.datastax.com/download-drivers.