vimium – remap marks key

On holiday I on may notebook with the “siwss german” key layout. This lead to the issue with Chrome + vimium extentions and the marks key. The default key to jump to a mark is “`” which need shift and two strokes to the key with the “`”.

On vimium readme it is not explained how to modify this key to “‘” . But the solution is:

map ' Marks.activateGotoMode

Just add this in the vimium options page under the remapping section.

The Perfrect Build Script

A build script should be simple, extensible and compile native.

Generally shell scripts do great job as build scripts. But if your development environment consist of Linux, Windows and Docker environments, shell script do not run out of the box on every platform. As well scripting languages like python need dependencies and are difficult to maintain for different platforms.

The solution would be a script which can run without dependencies on every platform. The solution I picked is “golang”. Runs as script, compiles native, easy to cross compile and does not need any dependencies.

Since all artifacts produces are docker images, the script is called “captain”

Add Jenkins Tool Location with ansible

Resently I hat do setup a jenkins slave with ansible. I used some ansible roles from ansible-galaxy, but all did miss to set the tool location.

So I read some java doc and added the tool location config to the ruby-jinja2 template.

import hudson.model.AbstractBuild
import hudson.model.Node
import hudson.plugins.sshslaves.SSHLauncher
import hudson.plugins.sshslaves.verifiers.NonVerifyingKeyVerificationStrategy
import hudson.plugins.sshslaves.verifiers.SshHostKeyVerificationStrategy
import hudson.slaves.ComputerLauncher
import hudson.slaves.DumbSlave
import hudson.util.VariableResolver
import jenkins.model.Jenkins
import hudson.slaves.*
import hudson.slaves.EnvironmentVariablesNodeProperty.Entry
import hudson.plugins.git.GitTool
import hudson.plugins.git.GitTool$DescriptorImpl

List env = new ArrayList();
{% for item in (slave_environments | dict3items) %}
env.add(new Entry("{{ item.key }}", "{{ item.value }}"))
{% endfor %}

List toolLoc = new ArrayList();
{% for item in (slave_tool_locations ) %}
toolLoc.add(new ToolLocationNodeProperty.ToolLocation(ToolInstallation.all().get(hudson.plugins.git.GitTool$DescriptorImpl.class), "{{ }}", "{{ item.home }}"))
{% endfor %}

EnvironmentVariablesNodeProperty envPro = new EnvironmentVariablesNodeProperty(env);
ToolLocationNodeProperty envToolLoc = new ToolLocationNodeProperty(toolLoc);

Jenkins jenkins = Jenkins.getInstance()

String nodeHostname = "{{ slave_linux_host }}"
String nodeCredentialID = "{{ slave_linux_jenkins_cred_id }}"
int nodePort = {{ slave_linux_ssh_port }}
SshHostKeyVerificationStrategy strategy = new NonVerifyingKeyVerificationStrategy()

ComputerLauncher nodeLauncher = new SSHLauncher( nodeHostname, nodePort,
nodeCredentialID, null, null, null, null, 31, 20, 10, strategy )

String nodeName = "{{ slave_agent_name }}"
String nodeRemoteFS = "{{ slave_linux_home }}"

Node node = new DumbSlave(nodeName, nodeRemoteFS, nodeLauncher)
node.setNumExecutors({{ slave_executors_num }})
node.setLabelString("{{ slave_linux_labels | join(' ') }}")
{% if slave_mode_exclusive %}
{% else %}
{% endif %}

If this did help you, consider to send me flower or a donut.

docker volume performance (portworx, nfs, bind mount)

I consider using NFS for shared volume on Docker Swarm. So I wonder how the performance of Docker NFS volumes may be.

Docker cache or delegated volumes have a huge effect on read an mostly outperform native read by far. On direct io this options do not have any effect.

The test is done on local host all to the same physical disk. Network latency is no considered. The Portworx is a standalone setup and might not represent the performance of a large cluster with replication.

docker run -it --rm -v jenkins_vol:/demo -v nfstest:/nfs ubuntu 

root@686890cca159:/# set -o vi                                                                                                                                                                                                                                                                                                                                                                                                                                                   
root@686890cca159:/# dd if=/dev/zero of=/bindmount/test bs=1M count=1000 oflag=direct                                                                                                                                                                     
1000+0 records in                                                                                                                                                                                                                                        
1000+0 records out
1048576000 bytes (1.0 GB, 1000 MiB) copied, 4.80277 s, 218 MB/s
root@686890cca159:/# dd if=/dev/zero of=/portworxvolume/test bs=1M count=1000 oflag=direct                                                                                                                                                               
1000+0 records in                                                                                                                                                                                                                                        
1000+0 records out
1048576000 bytes (1.0 GB, 1000 MiB) copied, 6.08722 s, 172 MB/s
root@686890cca159:/# dd if=/dev/zero of=/nfs/test bs=1M count=1000 oflag=direct                                                                                                                                                                          
1000+0 records in                                                                                                                                                                                                                                        
1000+0 records out
1048576000 bytes (1.0 GB, 1000 MiB) copied, 15.3807 s, 68.2 MB/s

But still the NFS seem to be significantly slower. NFS tuning did not change write performance.

Index Oracle RDBMS in Solr using Apache Spark

After I wrote multiprocessing python script which can be scaled with multiple docker containers to load billions of records from Oracle RDBMS to Solr Cloud I started to think of simpler solutions.

The basic idea is to handle all multiprocessing and scaling aspects by spark. As Spark is able to partition jdbc datasources out of the box the prove was needed that data can be saved in solr. Spakr-Solr provides the needed functionality.

Environment Preparation:

  • Setup Solr Cloud on Docker (one node in cloud mode is sufficient, create a collection “test”)
  • Run Oracle 18c on Docker
  • Run a Spark Cluster on Docker (I used this Spark images)

Copy the jdbc driver and the spark-solr driver

docker cp ojdbc8.jar spark-master:/
docker cp spark-solr-3.6.4-shaded.jar  spark-master:/
docker cp ojdbc8.jar spark-worker-1:/
docker cp spark-solr-3.6.4-shaded.jar  spark-worker-1:/

Start the Spark Shell (I use the pyspark) and load the date into a data frame

docker exec -it spark-master /spark/bin/pyspark --jars /ojdbc8.jar,/spark-solr-3.6.4-shaded.jar

empDF = \
    .format("jdbc") \
    .option("url", "jdbc:oracle:thin:test/test@//") \
    .option("dbtable", "test.emp") \
    .option("user", "test") \
    .option("password", "****") \
    .option("driver", "oracle.jdbc.driver.OracleDriver") \

Now save the date to Solr Cloud


And the date is in Solr Cloud.

On a Test with more date I had to add the Option .option(“commit_within”, “5000”)
I could not find explicit commit on solr-spark.

This solution should able scale well on a spark cluster by partitioning the data on the jdbc side. Transformation can be added on Spark. And such a solution would much less complex and better maintainable then a python multiprocessing solution (who ever used python multiprocessing may know what I mean).

If you have used similar setup please share your experiences.

Oracle: udpate a XML value in place with sql

Oracle sql provides a simple way to update values in a xml document by sql.
The “updatexml” function can udpate any fields in by xpath.

update SCOTT.DEMO" d
where id=1234;

to update multiple values at once:


Very handy to update large documents.

Oracle on Docker (I): Backup / Restore Oracle RDBMS on Docker

Running Oracle on Docker is a great way for Testing Features or Development environments, specially for offline development on notebooks which require an oracle database.

If you have setup an oracle Image according oracle rdbms docker and choosen to create your db on docker volumes. And mounted backup1 volume at /backup.

docker volume create oracledb1
docker volume create backup1

Create an offline backup:

docker exec -u 0 -it ora122 bash
bash-4.2# chown oracle /backup

docker exec -ti ora122 sqlplus / as sysdba
SQL> alter database close;

Database altered.

docker exec -ti ora122 rman target /
backup database format '/backup/%U' tag=bk1;

Restore Database from Backup

docker exec -ti ora122 rman target /
RMAN> restore database from tag=BK1

RMAN> shutdown immediate

RMAN> startup mount

RMAN> alter database open resetlogs;

Find Oracle SQL Profiles causing dynamic_sampling

Recently I hat memory lock issues with statements using dynamic sampling.
But after disabling dynamic_sampling at the system level there were still some statements causing issues with dynamic_sampling.
The execution plan showed that the problematic statements had SQL Profiles. Thanks to a blog from
Cristian Antognini ( all SQL Profiles with with a dynaimic sampling hint could be found.

Demo: Setup a statment with a sqlprofile

exec dbms_sqltune.drop_sql_profile('SQLPROFILE42');
     l_sql               clob;
     l_sql := q'!select id from t42 where id=:x!';

     dbms_sqltune.import_sql_profile( sql_text => l_sql, 
                                     name => 'SQLPROFILE42',
                                     profile => sqlprof_attr(q'!OPT_PARAM('optimizer_dynamic_sampling' 2)!'
                                                -- ,q'!FULL(@"SEL$1" "T42"@"SEL$1")!'
                                                ,q'!INDEX(@"SEL$1" "T42"@"SEL$1" "I42_3")!'
             force_match => true );

var x number;
exec :x := 2167658022;

explain plan for select id from t42 where id=:x;
select * from table(dbms_xplan.display(null,null,'ADVANCED'));

Finding the SQL Profiles with dynamic sampling

SELECT,extractValue(value(h),'.') AS hint
FROM sys.sqlobj$data od, sys.sqlobj$ so,
table(xmlsequence(extract(xmltype(od.comp_data),'/outline_data/hint'))) h
WHERE 1=1 -- = '&sqlprof'
  AND so.signature = od.signature
  AND so.category = od.category
  AND so.obj_type = od.obj_type
  AND so.plan_id = od.plan_id
  and extractValue(value(h),'.') like '%dynamic%'
OPT_PARAM('optimizer_dynamic_sampling' 2)

Clone Oracle Home 12c on the same Host

If you have to test some patches and you need a new Oracle Home on the same host, cloning the existing home to the same host is an easy an quick way to do that.

Here a short step by step guide:

  1. check diskspace
    df -h .
  2. copy the oracle home to it’s new location
    cp -rp /opt/app/oracle/product/12.1.0/rdbms6/ /opt/app/oracle/product/12.1.0/rdbms7
  3. export the new home path and cone. this creates as well all nessesary inventory entries. be carefull to use a free oracle_home_name.
    export ORACLE_HOME=/opt/app/oracle/product/12.1.0/rdbms7

    /opt/app/oracle/product/12.1.0 $/opt/app/oracle/product/12.1.0/rdbms6/oui/bin/runInstaller -clone -silent ORACLE_HOME=/opt/app/oracle/product/12.1.0/rdbms7 ORACLE_HOME_NAME="OraDB12Home4" ORACLE_BASE="/opt/app/oracle"

    Starting Oracle Universal Installer...

    Checking swap space: must be greater than 500 MB. Actual 9835 MB Passed
    Preparing to launch Oracle Universal Installer from /tmp/OraInstall2017-11-15_01-14-43PM. Please wait ...
    Copyright (C) 1999, 2014, Oracle. All rights reserved.

    You can find the log of this install session at:
    .................................................................................................... 100% Done.

    Setup in progress (Wednesday, November 15, 2017 1:16:34 PM CET)
    .......... 100% Done.
    Setup successful

    Saving inventory (Wednesday, November 15, 2017 1:16:34 PM CET)
    Saving inventory complete
    Configuration complete

    End of install phases.(Wednesday, November 15, 2017 1:17:00 PM CET)
    The following configuration scripts need to be executed as the "root" user.
    To execute the configuration scripts:
    1. Open a terminal window
    2. Log in as "root"
    3. Run the scripts

    The cloning of OraDB12Home4 was successful.
    Please check '/opt/app/oraInventory/logs/cloneActions2017-11-15_01-14-43PM.log' for more details.

  4. run the script as instructed.
  5. you can check the inventory if all looks as expected:
    cat /opt/app/oraInventory/ContentsXML/inventory.xml|grep OraDB12Home4

  6. update your oratab and the the OPATCH directories of the database you want to switch to the new home

    sid rdbms7
    $ORACLE_HOME/OPatch/opatch lsinventory

    create or replace directory OPATCH_INST_DIR as '/opt/app/oracle/product/12.1.0/rdbms7/OPatch';
    create or replace directory OPATCH_SCRIPT_DIR as '/opt/app/oracle/product/12.1.0/rdbms7/QOpatch';
    create or replace directory OPATCH_LOG_DIR as '/opt/app/oracle/product/12.1.0/rdbms7/QOpatch';

Indexing Oracle table columns of collection type (nested table)

With Oracle’s Object-Relational you can store collection of user defined datatypes (UDT) in a table column. This column can be stored a IOT or heap table.

If stored as heap table (normal table) you may want to index the nested table so that there are not FTS on every access.

| Id  | Operation                           | Name                          | Rows  | Bytes | Cost (%CPU)| Time     |
|   0 | SELECT STATEMENT                    |                               |     9 |  1818 | 32469   (1)| 00:00:02 |
|*  1 |  TABLE ACCESS FULL                  | TABLE                         |     1 |    81 |   309   (1)| 00:00:01 |

The nested table has a hidden column “NESTED_TABLE_ID” which can be indexed:

create index <my_index>on <my_nested_table> (NESTED_TABLE_ID)
| Id  | Operation                           | Name                          | Rows  | Bytes | Cost (%CPU)| Time     |
|   0 | SELECT STATEMENT                    |                               |     9 |  1818 |    56   (0)| 00:00:01 |
|   1 |  TABLE ACCESS BY INDEX ROWID BATCHED| NESTED_TABXX_TAB2             |     1 |    27 |     2   (0)| 00:00:01 |
|*  2 |   INDEX RANGE SCAN                  | NESTED_TAB_IX1                |     1 |       |     1   (0)| 00:00:01 |

* Don’t forget to gather statistics after create the indexes