Category: Uncategorized

How to resolve error “’s server DNS address could not be found” while loading Oozie Web UI

Today we will see how we can resolve the error “’s server DNS address could not be found” while loading Oozie Web UI. I got this wired error after reinstalling the HDP sandbox on Windows 10.

This error is happening because we haven’t updated the hosts file in Windows 10 platform. You can find the hosts file under the below listed directory.


Copy the host file to desktop and then edit it, add the following line at the end of hosts file.

Now copy back the hosts file to C:\Windows\System32\drivers\etc, You may need the admin rights if you are not a power user.

Now you will be able to access Oozie UI with the below link.


Load Testing Oracle Stored Procedures Using Apache JMeter

Last week I was asked to load test few oracle stored procedures created by another team. These stored procedures are invoked by java services and the delivery team wanted to test them in the performance environment before pushing them to production. I was asked to use an Open Source tool for this testing since the project doesn’t have budget to buy a commercial tool.

Since Apache JMeter is a free tool, we decided to use it for our testing. The stored procedure takes one INTEGER value as input and returns an INTEGER value and a cursor as out put. The input value came from a CSV file which has 100K records that we mined from the database. I have detailed the steps we used to create a database test plan, our biggest challenge was to receive cursor output from the stored procedure. Details on how we achieved this can be found below. I have also explained how you can download the necessary tools and configure them.

**Please note that I have simulated below tests in my laptop since the actual code belongs to my client. I have no authority to publish the actual code here, but the configurations used in both cases are same. **

First, Lets download the latest version of Apache JMeter.


JMeter needs a JDBC driver to connect to oracle database, You can find the JDBC connect for Oracle at the Oracle Technology Network (OTN) website.


First, download JMeter and unzip to the folder where you want to save it. Go to /bin folder and you can find windows batch executable (jmeter.bat), double-click on it and it will start the JMeter GUI. If you are in Linux there is a script in the bin directory which invokes jmeter UI.

Now, download the JDBC driver from OTN and move the .jar file to /lib folder. It will be automatically detected and used by JMeter.

You can also download a lot of JMeter plugins, which will make your testing easy and will help you present your results in beautiful graphs.

Go to the JMeter UI.


I had created a stored procedures in my local oracle instance, which will be tested by JMeter. This stored procedure accepts number value as input and return a number and a cursor.


Go back to the JMeter and create a new thread group, which will monitor and control our testing.


Change the thread group name, you can give whatever name you want. Here I gave “OracleLoadTest_Sproc” for my thread group. Also we can set the number parallel threads and ramp up period in this screen.


After this we have to configure our database connection. JMeter can connect to Oracle database using a JDBC driver.

Right click on thread group and add a new JDBC connection configuration.


Now configure the connection settings, Here I have specified the server name as localhost since it is installed on my laptop. You can find your DB server details in TNSNAMES.ORA file or can get it from your DBA. You also have to set your database username and password for establishing the connection.


Our input data is coming from a CSV file and we usually test with 100K input fields. Here I have used a small file just to make sure the setup is working.


You have to configure the source file name and column names here. These column names are used later as the input parameter for the stored procedure.


Now we have to add a constant throughput time which helps us to main a constant throughput through out our test window.


Here I had set the timer as 60 threads/minute (1 Thread/Sec).


Let us add the component that calls stored procedure. Add a JDBC request to the thread group and include stored procedure details in it.


We have to set the query type as Callable Statement and use the CALL command to invoke the stored procedure. We can also pass the values from CSV file to the stored procedure. Just to make sure the type for cursor variable is-10 for the stored procedure.


Now add few listeners which monitor the execution and summarize the output. This will help us to present the final results more clearly to the client. You can add as many listeners as you want, We are adding just two here.




We are all set here, lets start the execution and collect the test results.


Once the test is complete, listeners provide test summary and other execution details.


We can see the stored procedure invocation details (input values, output values etc.) also from the result tree screen.



One important factor is JMeter is a Java application and could use a lot of heap memory. By default JMeter 3.1 uses 512 MB of heap memory. You will definitely need more data to run tests in GUI mode. Edit the jemeter.bat using any good text editor like UltraEdit and add more memory based on your hardware configuration.

You can run JMeter from the command prompt if you have a memory constraint and save the results to a jtl file.

Disable Hadoop log messages in the console

If you are a beginner to Hadoop/map-reduce ecosystem, you must have seen the messages that are displayed in the console when you run commands. It could be useful for a beginner and sometimes help you to understand the functionality.  But these log outputs are annoying once you are familiarized with the working of the system or you are using a production system.

These messages can be  suppressed using Ambari. I will just list the steps you have to go through to disable them.

  1. Log into Ambari console and go to Mapreduce2 tab in the left side.


2. In the Mapreduce2 page, click on configs tab and then go to advanced.


3. There you can see “Advanced mapred-site” push down list and click on it.


4. Scroll down and then change the value of below two parameters to ‘OFF’


ambari_75. Once done, save the changes and restart the components required.

Now if you login to Hive/Pig Console you don’t see all those INFO messages when running the queries.

Automatic Big Table Caching in Oracle 12C

Oracle uses its buffer cache for caching or storing recently accessed table blocks. This helps Oracle to access the same blocks faster if they are required again. Since the buffer cache resides in memory and memory is expensive this cache size is always limited. Oracle uses a LRU (Least Recently Used) algorithm to keep the most recently access blocks in memory. It is a complex algorithm for buffer cache management, but lets simplify it by saying oracle keeps a simple queue which holds the most recently used data at the hot end. As we query more and more data, existing data in the queue is pushed backwards and finally moves out of the queue.

When you query some data which had already been moved out of cache, oracle finds this data is no longer in memory and it goes for physical reads (from disks) and this is an expensive and time consuming operation.

One of the big issue with this kind of a cache is, if you query a big table most of the queue can be replaced by the data from that table alone and all subsequent queries may go for physical reads. Oracle can’t allow this and such reads bypass the buffer cache to maintain its balance. Oracle usually avoids moving the blocks of huge tables into buffer cache by using direct path reads/writes which uses the PGA (Program Global Area) which is not a shared memory area. Since PGA is not shared among the users, such caching of data is not useful for other users of the database. And this may lead to extensive physical read operations.

Recent versions of oracle (12c) is trying to overcome this issue by identifying the big tables in the database and caching data from those tables effectively. This is done by reserving a part of buffer cache for storing big tables.

Let’s test this feature by creating couple of big tables (> 1 Million rows).

SQL> create table my_test_tbl
 2 (Name varchar2(100),
 3 Emp_No integer,
 4 Dept_no integer);

Table created.

SQL> insert into my_test_tbl
 2 select 'John Doe', level, mod(level,10)
 3 from dual
 4 connect by level <= 1000000; SQL> commit;

Commit complete.

SQL> select count(*) from my_test_tbl;


We need to analyze the table, so that the metadata will be updated.

SQL> analyze table my_test_tbl compute statistics;

We will have to set a parameter at system level so that a part of the buffer cache (40% in our case) will be allocated for caching big tables. First , lets check the size of buffer cache allocated for this database.

SQL> select component, current_size/power(1024,3) current_size_GB from v$memory_dynamic_components
 2 where component = 'DEFAULT buffer cache'

 -------------------- ---------------
 DEFAULT buffer cache 1.546875

We have 1.5GB of buffer cache, let’s allocate 40% of this for caching big tables.

SQL> show parameter big_table

 ------------------------------------ ----------- ------------------------------
 db_big_table_cache_percent_target string 0

SQL> alter system set db_big_table_cache_percent_target = 40;

System altered.

SQL> show parameter big_table

 ------------------------------------ ----------- ------------------------------
 db_big_table_cache_percent_target string 40

Now, if we query the table it will be cached in to the big table cache.

SQL> select count(*) from my_test_tbl;


Please make note that we don’t have to restart the DB for modifying this parameter. Lets check the caching of the table and how much of it is cached,

SQL> select * from V$BT_SCAN_CACHE;

bt_pic_1Clearly shows 40% is reserved for big tables.


We have already queried the table once and oracle had identified that the table is indeed a big one. Now we have table in cache, we can check the size of the table on disk and how much of it is cached. Since the V$BT_SCAN_OBJ_TEMPS table contains the object id we can join it with DBA_OBJECTS and find out the table name. Once we have the table name DBA_TABLES will give us the size of the table on disk (blocks).

SQL> select object_name from dba_objects where object_id = 92742


SQL> column table_name format a20
 select table_name, blocks from dba_tables
 where table_name = 'MY_TEST_TBL';

 -------------------- ----------

The whole table is cached now and the temperature is set to 1000, if we use this table more and more the temperature of this table will go up making it hot. Below code snippet will query my_test_tbl 10,000 times and this will help us to increase the temperature of the table.

SQL> declare
 2 l_count integer;
 3 begin
 4 for i in 1..10000
 5 loop
 6 select count(*) into l_count from my_test_tbl;
 7 end loop;
 8 end;
 9 /

PL/SQL procedure successfully completed.

Check the V$BT_SCAN_OBJ_TEMPS table again to see if the temperature value has gone up.


We can see the temperature of the table has gone up because of the frequent querying, now we are creating another table and see if that is also getting cached. We will have 2 million records in this table.

SQL> create table my_test_tbl2
 2 as select * from MY_TEST_TBL;

SQL> insert into my_test_tbl2 select * from my_test_tbl;

1000000 rows created.

SQL> analyze table my_test_tbl2 compute statistics;

Table analyzed.

SQL> select table_name, blocks from dba_tables
 where table_name = 'MY_TEST_TBL2';

 -------------------- ----------
 MY_TEST_TBL2 6224

SQL> select count(*) from MY_TEST_TBL2;


We can see the new table in cache with initial temperature value of 1000.


Lets run the snippet again to query the new table, this time we will query only 100 times.

Query V$BT_SCAN_OBJ_TEMPS again to see the new temperature value of second table.


This temperature value helps oracle to prioritize tables in memory and identify which table is frequently queried. Based on this information oracle decides which table stays in memory and which table has to move out.

We have to remember currently we don’t have any option to move individual tables to the cache. It is completely automated and done by Oracle’s discretion. Our table may  or may not be  moved to this cache, but if you have big tables which you think may get benefited from this option then you can check this option.

Import data from MySQL to hadoop using SQOOP

As a part of our job we import/move a lot of data from relational databases (Mainly from Oracle and MySQL) to Hadoop. Most of our data stores are in Oracle with a few internal data stores running on MySQL.

SQOOP (SQL for Hadoop) is an Apache tool to import data from relational databases (There are separate drivers for each database) to hadoop. Here in this blog we will try to import data from a MySQL table to Hadoop file system.

Here, I have a MySQL instance running on the local machine on which my Hadoop cluster also running. You will have to download and place the driver in appropriate directory for SQOOP to connect to that database. Drivers are already present in my machine as SQOOP offers a very extensive support for MySQL.

Below link will give you a list of available drivers and their locations if you are using a different database.

First let me login to the primary node in my 3 node cluster (Virtual/Created by Vagrant and VirtualBox).

vagrant ssh node1

Let us check the connection and data in the MySQL database.

mysql -u root -h localhost -p
Enter password: ********
MariaDB [(none)]> show databases;
| Database |
| information_schema |
| my_test |
| mysql |
| performance_schema |
| test |
5 rows in set (0.04 sec)

use my_test;

MariaDB [my_test]> show tables;
| Tables_in_my_test |
| name_data |
| name_data2 |
2 rows in set (0.02 sec)

Now, lets check the data.

select count(*) from name_data;

MariaDB [my_test]> select count(*) from name_data;
| count(*) |
| 1858689 |

MariaDB [my_test]> select * from name_data limit 3;
| Name | Gender | count |
| Mary | F | 7065 |
| Anna | F | 2604 |
| Emma | F | 2003 |

Now we are sure that we have data in MySQL table, lets check our HADOOP home directory.

hadoop fs -ls /user/vagrant/

[vagrant@node1 ~]$ hadoop fs -ls /user/vagrant
Found 5 items
drwx------ - vagrant hdfs 0 2016-12-20 02:56 /user/vagrant/.Trash
drwxr-xr-x - vagrant hdfs 0 2016-10-26 04:46 /user/vagrant/.hiveJars
drwx------ - vagrant hdfs 0 2016-11-13 23:44 /user/vagrant/.staging
drwxr-xr-x - vagrant hdfs 0 2016-12-06 04:13 /user/vagrant/test_files

Now we wants to move the data from MySQL to the /users/vagrant/name_data directory. Below is th sqoop command to move import data.

[vagrant@node1 ~]$ sqoop import –connect jdbc:mysql://localhost/my_test –username root –password ******* –table name_data –m 1 –target-dir /user/vagrant/my_data

Once this command is completed, data will be present in /user/vagrant/my_data.

[vagrant@node1 ~]$ hadoop fs -ls /user/vagrant/my_data
Found 2 items
-rw-r--r-- 3 vagrant hdfs 0 2016-12-20 03:20 /user/vagrant/my_data/_SUCCESS
-rw-r--r-- 3 vagrant hdfs 22125615 2016-12-20 03:20 /user/vagrant/my_data/part-m-00000

[vagrant@node1 ~]$ hadoop fs -cat /user/vagrant/my_data/part-m-00000| wc -l
[vagrant@node1 ~]$

[vagrant@node1 ~]$ hadoop fs -cat /user/vagrant/my_data/part-m-00000| head -3

We can also create a config file and store the commands in it for re-usability.

[vagrant@node1 ~]$ cat sqoop_test_config.cnf

[vagrant@node1 ~]$ sqoop --options-file ./sqoop_test_config.cnf --password ***** --m 1 --table name_data --target-dir /user/vagrant/my_data

This also does the same job, but now we have the flexibility to save, edit and reuse the commands.

Delete lines from multiple files (recursively) in Linux

We had a requirement to delete a line which matches a particular pattern from multiple ksh files. These lines of code was used to log execution status and we no longer needed it after an architecture change.

Opening hundreds of files and deleting the lines manually was a painful task, We achieved this by combing find and sed commands.

find . -name “*.ksh” -type f | xargs sed -i -e ‘/Search String/d’

Find command searches for ksh files recursively in the current directory and lists them. The second part, xargs and sed commands searches for the pattern in each file and delete it.

You can refer the manual pages if you need more information on these commands.

Deleting thousands of files from Linux directory

Our project team uses a script which creates thousands of files in a folder and we sometime has to manually clean up all those files.

rm command failed saying the list is too long and we had to find another method to do this. rather than writing s shell script and delete the files one by one we used the find command.

Here how we had done it.

>>>ls -lrt| wc -l

We have 250K files in this directory and we tried removing them using the rm command.

>>> rm *.env
 ksh: rm: /bin/rm: cannot execute [Argument list too long]

This issue can be easily solved by the find command and we noticed find runs faster in such situations.

>>>find . -name "*.env" -delete

Above listed find command deleted all .env files in the current directory.