Pythian Group

Subscribe to Pythian Group feed
Love Your Data
Updated: 37 min 8 sec ago

Oracle Service Secrets: quiesce tactically

Fri, 2016-09-02 10:18

In the last post of this series about Oracle net services, I talked about how services can help you identify performance issues faster and easier by tagging connections with service names. Today I am introducing you to the idea of temporarily disabling connections during maintenance with the help of services.

During deployments, testing or reorganizations it might be necessary to prevent clients from connecting to the database while still allowing access for DBAs to do their work. Some methods to do this include temporarily locking application user accounts or putting the database in quiesce mode. But with services, you now also have a more tactical approach to this issue.

My example assumes a single instance with two services DEMO_BATCH and DEMO_OLTP. And let’s assume that we need to temporarily disable batch services, maybe just to reduce system load due to those activities or maybe because we are reorganizing the objects used by the batch processes.

To disable a service in a single instance we can either remove it from the SERVICE_NAMES instance parameter or use the DBMS_SERVICE package.




PL/SQL procedure successfully completed.

New sessions using the service name will receive an ORA-12514 error when trying to connect:

brbook:~ brost$ ./sqlcl/bin/sql brost/******@

SQLcl: Release RC on Thu Aug 18 13:12:27 2016

Copyright (c) 1982, 2016, Oracle.  All rights reserved.

  USER          = brost
  URL           = jdbc:oracle:thin:@
  Error Message = Listener refused the connection with the following error:
ORA-12514, TNS:listener does not currently know of service requested in connect descriptor
Existing sessions are allowed to continue

Note that stopping will only affect new connections. Existing sessions that used the DEMO_BATCH service are allowed to continue until they disconnect or you kill them. This gives you the flexibility of a grace period where you just wait for existing sessions to finish their work and disconnect by themselves.

no rows selected


-------------------- ------------------------------
Grid Infrastructure has option to force disconnects

If you are using grid infrastructure and manage services through srvctl this behaviour is basically the same but you get an extra “force” switch to also disconnect existing sessions while stopping a service.

[oracle@ractrial1 ~]$ srvctl stop service -db orcl42 -service racdemo_batch [-force]

[oracle@ractrial1 ~]$ srvctl stop service -h

Stops the service.

Usage: srvctl stop service -db <db_unique_name> [-service  "<service_name_list>"] [-serverpool <pool_name>] [-node <node_name> | -instance <inst_name>] [-pq] [-global_override] [-force [-noreplay]] [-eval] [-verbose]
    -db <db_unique_name>           Unique name for the database
    -service "<serv,...>"          Comma separated service names
    -serverpool <pool_name>        Server pool name
    -node <node_name>              Node name
    -instance <inst_name>          Instance name
    -pq                            To perform the action on parallel query service
    -global_override               Override value to operate on a global service.Ignored for a non-global service
    -force                         Disconnect all sessions during stop or relocate service operations
    -noreplay                      Disable session replay during disconnection
    -eval                          Evaluates the effects of event without making any changes to the system
    -verbose                       Verbose output
    -help                          Print usage

Creating extra services on a database allows you to stop and start them for maintenance which can be used as a convenient way to lock out only certain parts of an application while leaving user accounts unlocked to connect via different services.

Categories: DBA Blogs

The rlwrap utility for DBA.

Thu, 2016-08-18 08:36

I spend most of my time as a DBA in linux terminal and sqlplus. Everybody who works with oracle sqlplus knows about its power, but also about its limitations. For many years I have used the rlwrap utility developed by Hans Lub. It gives me command history, and the ability to edit my SQL Plus commands, and use auto completion if I set it up. In this post I will share some tips about installation and basic usage.

First we need to install the utility, and there are several options for that. We will check a few of them below. Please keep in mind that all of my examples are tested on Oracle Linux 6.

For the first one we need git, yum and automake packages. We will use the latest and greatest source code from the project site on GitHub: and your standard Yum repository. Assuming you have connection to GitHub and your Yum repo.
Let’s run it step-by-step:

[root@sandbox ~]# yum install readline-devel
[root@sandbox ~]# yum install automake
[root@sandbox ~]# yum install git
[root@ovmcloud01 ~]# git clone
Initialized empty Git repository in /root/rlwrap/.git/
remote: Counting objects: 1250, done.
remote: Total 1250 (delta 0), reused 0 (delta 0), pack-reused 1250
Receiving objects: 100% (1250/1250), 565.53 KiB, done.
Resolving deltas: 100% (867/867), done.
[root@ovmcloud01 ~]# cd rlwrap
[root@ovmcloud01 rlwrap]# autoreconf --install installing `tools/config.guess' installing `tools/config.sub' installing `tools/install-sh' installing `tools/missing'
src/ installing `tools/depcomp'
[root@ovmcloud01 rlwrap]# automake  --add-missing
[root@ovmcloud01 rlwrap]# ./configure
[root@ovmcloud01 rlwrap]# make install

That’s it. You have it installed in your system.

The second way is to compile it from source you have downloaded from . It may be useful if you don’t have connection to Yum and GitHub.
Keep in mind you will need GNU readline and ncurses libraries and headers installed in your system. So, we download the binaries, unpack it, compile and install.

[root@sandbox]$tar xfz rlwrap-0.42.tar.gz
[root@sandbox]$cd rlwrap-0.42
[root@sandbox]$make install

The third way is to copy previously compiled rlwrap execution file and use it on a new system adding it to */bin directory in standard path.
It works if you have several similar, binary compatible systems and don’t want to spend time compiling the same binaries on each one.

[root@sandbox]$cd rlwrap-0.42
[root@sandbox]$ls -l src/rlwrap
-rwxr-xr-x. 1 root root 225023 Aug 16 12:49 src/rlwrap
[root@sandbox]$cp src/rlwrap /usr/local/bin/
[root@sandbox]$rlwrap --help
Usage: rlwrap [options] command ...

  -a[password prompt]        --always-readline[=password prompt]
  -A                         --ansi-colour-aware

Of course you may consider to make your own rpm or use EPEL (Extra Packages for Enterprise Linux) yum repository and install it from there. Just keep in mind the version you get from EPEL may be slightly outdated.

[root@sandbox]$yum install
[root@sandbox]$yum install rlwrap

Having the rlwrap installed you may find use for it.
Here some basic examples how you can use the utility:
Create an alias in your bashrc for sqlplus :

vi ~/.bashrc

and add

alias sqlp='rlwrap sqlplus'

The same you can do for rman :

alias rman='rlwrap rman'

For Oracle GoldenGate command line utility

alias ggsci='rlwrap ./ggsci' 

In rlwrap you can use ! and TAB to call list of commands or use prefix and CTRL+R to search for certain command in command history. Also you can create your own dictionary and use it for auto completion.
Let’s try to build some dictionary for auto-completion
I created a file “lsql.lst” with the following contents:

[oracle@sandbox ~]$ vi lsql.lst

[oracle@sandbox ~]$alias sqlp="rlwrap -f lsql.lst sqlplus / as sysdba"
[oracle@sandbox ~]$ sqlp

SQL*Plus: Release Production on Wed Aug 17 15:36:04 2016

Copyright (c) 1982, 2014, Oracle.  All rights reserved.

Connected to:
Oracle Database 12c Enterprise Edition Release - 64bit Production
cdb> desc v$t 

— here we are pressing TAB and getting list of suggestions:

cdb> desc v$t 
table       tablespace
cdb> desc v$tablespace
 Name														   Null?    Type
 ----------------------------------------------------------------------------------------------------------------- -------- ----------------------------------------------------------------------------
 TS#															    NUMBER

We can make it even better. Let’s upload name for all dba_views
In our sqlplus session we run :

cdb> spool lsql.lst append
cdb> select name from V$FIXED_TABLE;
cdb>spool off

logoff and logon again to reload the file and try :

cdb> select * from V$PA -- TAB
cdb> select * from V$B -- TAB
BACKUP                       BACKUP_CONTROLFILE_SUMMARY   BACKUP_DATAFILE_SUMMARY      BACKUP_SET                   BACKUP_SYNC_IO               BSP                          BUFFERED_PUBLISHERS
BACKUP_ASYNC_IO              BACKUP_CORRUPTION            BACKUP_PIECE                 BACKUP_SPFILE                BLOCKING_QUIESCE             BT_SCAN_OBJ_TEMPS            BUFFER_POOL
cdb> select * from V$B

You can see we have all “V$” views and it can be extremely handy when you don’t really have any time to search for a view name and only vaguely remember that you have a view to look up for certain information.

The rlwrap may not be most sophisticated program but it can make your life much easier. There may be a more advanced tool for sqlplus like SQLcl that provides a lot more options. But—the beauty of rlwrap is in its “lightness” and ability to work not only with sqlplus, but with practically any command line tool.

Categories: DBA Blogs

ORA-15418: Appliance Mode Not Supported For Compatible.asm

Wed, 2016-07-20 09:45

The other day, I was upgrading a compatible.asm parameter to 12.1 on Exadata and I faced this error for the first time :

View the code on Gist.

Indeed, a diskgroup can have this parameter set to TRUE or FALSE :

View the code on Gist.

I then found this note on Metalink : EXADATA : What Is Oracle ASM appliance.mode Attribute (Doc ID 1664261.1) which explains that starting from Exadata, “The Oracle ASM appliance.mode attribute improves disk rebalance completion time  when dropping one or more Oracle ASM disks. This means that redundancy is restored faster after a failure.

Wow, that looks like a pretty cool feature! But it seems that (sadly) we cannot set a 12.1 compatible if this feature is enabled.

Let’s give it a try and deactivate it to set my compatible.asm to 12.1 :

View the code on Gist.

It works ! Now that this compatible.asm is set to 12.1, could we enable that appliance.mode feature again ?

View the code on Gist.

Sadly, no. I hit one of the restrictions; indeed, “The Oracle ASM disk group attribute compatible.asm is set to release, and later. (Appliance mode is not valid with” — then I guess that even of the documentation does not say so, Appliance mode is not valid with a compatible.asm set to either.
Even if it is very weird that Oracle dev “forgot” (?) this cool feature when they released 12c on Exadata, they hopefully “released” it again with I’ll follow up on that when moving this compatible.asm to !

Categories: DBA Blogs

Truncates and Sequences Replication in Oracle GoldenGate

Fri, 2016-07-15 11:29

We use the terms DDL and DML in our DBA life with Oracle all the time. The first stands for Data Definition Language and it is about Data Definition changes. The latter is about manipulation with your data itself, and stands for Data Manipulation Language. When we speak about replication of our data by replication tools between Oracle databases, we generally either enable DDL, work only replicating DML, or do it for both together. In general, I would recommend replicating both DML and DDL just in case, to prevent the replication to be broken in case of unexpected structural changes in the replicated schemas. But in some cases you do not want to replicate all DDL or any DDL at all for certain reasons. I will discuss a couple of operations which are handled slightly different from pure DDL/DML changes in GoldenGate.

The first of them is truncate operation. In Oracle it is definitely DDL and you can see that.

orcl&gt; select object_id,data_object_id,last_ddl_time from dba_objects where object_name='EMP_TEST';

---------------- ---------------- -----------------
	  148769	   148769 06/24/16 16:07:04

orcl&gt; truncate table emp_test;

Table truncated.

orcl&gt; select object_id,data_object_id,last_ddl_time from dba_objects where object_name='EMP_TEST';

---------------- ---------------- -----------------
	  148769	   148770 06/24/16 16:15:52


It is clear that the object gets the new data_object_id and last_ddl_time shows new time.

There is a general assumption that you need to enable a DDL support to replicate truncates. But is this true? In reality you can replicate truncates (with some limitations) without full DDL support, and I want to show you how it can be done. What you need to do is setup a parameter GETTRUNCATES.
Let’s setup it on extract and see how it works.

Here is my extract parameter file:

[oracle@bigdatalite oggora]$ cat dirprm/trext.prm
extract trext
RMTHOST bigdatalite, MGRPORT 7849
RMTTRAIL ./dirdat/tr, format release 11.2
TABLE trsource.*;

We don’t have DDL support and if we try to add a column on the source and put a value to that column our replicat on other side will be abended.

orcl&gt; alter table trsource.emp add col1 varchar2(10) ;

Table altered.

orcl&gt; update trsource.emp set col1='Test1' where empno=7499;

1 row updated.

orcl&gt; commit;

And on the target side:

2016-06-27 13:51:47  INFO    OGG-01021  Oracle GoldenGate Delivery for Oracle, trrep.prm:  Command received from GGSCI: STATS.
2016-06-27 13:57:37  ERROR   OGG-01161  Oracle GoldenGate Delivery for Oracle, trrep.prm:  Bad column index (8) specified for table TRSOURCE.EMP, max columns = 8.
2016-06-27 13:57:37  ERROR   OGG-01668  Oracle GoldenGate Delivery for Oracle, trrep.prm:  PROCESS ABENDING.

You are going to get similar errors for other DDL operations but not for truncates.

orcl&gt; truncate table trsource.emp;

Table truncated.

GGSCI (bigdatalite.localdomain) 1&gt; send trext, stats

Sending STATS request to EXTRACT TREXT ...

Start of Statistics at 2016-06-27 14:05:24.

Output to ./dirdat/tr:


*** Total statistics since 2016-06-27 14:05:07 ***
	Total inserts                   	           0.00
	Total updates                   	           0.00
	Total deletes                   	           0.00
	Total truncates                 	           1.00
	Total discards                  	           0.00
	Total operations                	           1.00

You can see that we have captured the truncate by our extract. Even our DDL support is disabled. What we need is to set up the same parameter GETTRUNCATES on replicat side. Why do we need to set it up explicitly? Because the default behaviour and parameter for GoldenGate is “IGNORETRUNCATES” for all processes. As result, the truncates will be applied to the target system.

We are setting our parameter on replicat side and see the result:

[oracle@bigdatalite ogg11ora]$ cat dirprm/trrep.prm
replicat trrep
DISCARDFILE ./dirdsc/discard.out, append
--DDL include all
map trsource.emp, target trdest.emp;
[oracle@bigdatalite ogg11ora]$

GGSCI (bigdatalite.localdomain) 4&gt; send trrep, stats

Sending STATS request to REPLICAT TRREP ...

Start of Statistics at 2016-06-27 14:08:40.

Replicating from TRSOURCE.EMP to TRDEST.EMP:

*** Total statistics since 2016-06-27 14:08:25 ***
	Total inserts                   	           0.00
	Total updates                   	           0.00
	Total deletes                   	           0.00
	Total truncates                 	           1.00
	Total discards                  	           0.00
	Total operations                	           1.00

test&gt; select count(*) from trdest.emp;



We don’t need full DDL support if we want to replicate truncates only. Sometimes it may help us when we have workflow including truncates, but we don’t want to replicate all DDL commands for some reasons. Just keep in mind that it works with some limitations. You cannot replicate by using “truncate partition” for Oracle. It will require full DDL support.

The second thing I want to discuss in this topic is support for sequences values replication. Sometimes people assume that it requires DDL support, but this is not true. As matter of fact replicating of sequences values doesn’t require you to enable DDL support for your replication. Of course, you need full DDL replication support to replicate CREATE, ALTER, DROP, RENAME for sequences, but the values are replicated as DML.

To enable the replication of sequences you need to create a special user on source and target databases, add the user to the GGSCHEMA parameter to your .GLOBALS file, and run one script to create all necessary procedures in the newly created schema.
Let’s have a closer look. I have a user OGG I am using for connection and I plan to use the same user for sequence support.

Here is my .GLOBALS file:

[oracle@bigdatalite oggora]$ cat GLOBALS

The same I have on the target side:

[oracle@bigdatalite ogg11ora]$ cat GLOBALS

I ran the script sequence.sql on both sides.

orcl&gt; @sequence.sql
Please enter the name of a schema for the GoldenGate database objects:
Setting schema name to OGG


Line/pos				 Error
---------------------------------------- -----------------------------------------------------------------
No errors				 No errors


Line/pos				 Error
---------------------------------------- -----------------------------------------------------------------
No errors				 No errors


Line/pos				 Error
---------------------------------------- -----------------------------------------------------------------
No errors				 No errors


Line/pos				 Error
---------------------------------------- -----------------------------------------------------------------
No errors				 No errors

SUCCESSFUL installation of Oracle Sequence Replication support

And on the source side add primary key supplemental logging to the sys.seq$ table:


Table altered.


You may have a look to the procedures created by the scripts:

These procedures enable interface to flush, update and replicate the sequences.

Now we are creating a sequence on the source and target with the same parameters.

orcl&gt; create sequence trsource.empno_seq start with 8100;

Sequence created.


Adding parameter SEQUENCE to our parameter file for extract:

[oracle@bigdatalite oggora]$ cat dirprm/trext.prm
extract trext
RMTHOST bigdatalite, MGRPORT 7849
RMTTRAIL ./dirdat/tr, format release 11.2
--DDL include objname trsource.*
SEQUENCE tsource.*;
TABLE trsource.*;

[oracle@bigdatalite oggora]$

On the target we are creating the same sequence:

test&gt; create sequence trdest.empno_seq start with 8100;

Sequence created.

[oracle@bigdatalite ogg11ora]$ cat dirprm/trrep.prm
replicat trrep
DISCARDFILE ./dirdsc/discard.out, append
--DDL include all
map trsource., target trdest.;

I made our sequences a bit different on purpose. Our source sequence had a slightly bigger current value than target:

orcl&gt; select trsource.empno_seq.currval from dual;



test&gt; select trdest.empno_seq.currval from dual;



What we need is to run command FLUSH SEQUENCE on our extract side:

GGSCI (bigdatalite.localdomain) 9&gt; dblogin userid ogg@orcl,password AACAAAAAAAAAAAIARIXFKCQBMFIGFARA, BLOWFISH, ENCRYPTKEY DEFAULT
Successfully logged into database.

GGSCI (bigdatalite.localdomain as ogg@orcl) 3&gt; flush sequence trsource.empno_seq
Successfully flushed 1 sequence(s) trsource.empno_seq.

GGSCI (bigdatalite.localdomain as ogg@orcl) 4&gt;

And on target we can see:

test&gt; select * from dba_sequences where SEQUENCE_NAME='EMPNO_SEQ';

------------------------------ ------------------------------ ---------------- ---------------- ---------------- - - ---------------- ----------------
TRDEST			       EMPNO_SEQ				     1 9999999999999999 	       1 N N		   20		  8143

test&gt; select last_number from dba_sequences where SEQUENCE_NAME='EMPNO_SEQ';



The last number for the sequence on the target has been increased to 8143 when on the source we have only cache was flushed and we got 8123 as a last number for the sequence:

orcl&gt; select last_number from dba_sequences where SEQUENCE_NAME='EMPNO_SEQ';



Let’s try to get new values for the sequence.

orcl&gt; select trsource.empno_seq.nextval from dual;


orcl&gt; select trsource.empno_seq.nextval from dual;


We continue to increase values on the source, and as soon as we crossed max number for the source (8123) we got new value on the target:

orcl&gt; select trsource.empno_seq.nextval from dual;




test&gt; select last_number from dba_sequences where SEQUENCE_NAME='EMPNO_SEQ';



And the statistics on the target will be shown as updates:

GGSCI (bigdatalite.localdomain) 1&gt; send trrep, stats

Sending STATS request to REPLICAT TRREP ...

Start of Statistics at 2016-06-29 13:20:36.


*** Total statistics since 2016-06-29 13:10:52 ***
	Total updates                   	           4.00
	Total discards                  	           0.00
	Total operations                	           4.00

We can see that the two operations are a bit different from all other standard DDL and DML in Oracle GoldenGate. I hope this small piece of information may help you in your implementation, or help to support your GoldenGate environment.

Stay tuned and keep your eyes on Pythian blog.

Categories: DBA Blogs

Log Buffer #482: A Carnival of the Vanities for DBAs

Wed, 2016-07-13 13:29

This Week’s log buffer edition covers some of the useful blog posts from Oracle, SQL Server and MySQL.


ASM disks – lsdg compared with the v$asm_diskgroup view

Can a query on the standby update the primary ?

What should I know about SQL?

Setting Environment Variables in Application Server/Process Scheduler Tuxedo Domains

Oracle HEXTORAW Function with Examples

SQL Server:

Query Store is a new feature in SQL Server 2016 which, once enabled, automatically captures a history of queries, execution plans, and runtime statistics, retaining them for your troubleshooting performance problems caused by query plan changes.

Finding and Eliminating Duplicate or Overlapping Indexes

Changing Linked Server Properties

Windows Containers and Docker

Stretch Database in SQL Server 2016 RTM


Why Adaptive Fault Detection is Powerful and Unique

Develop By Example – Document Store Connections using Node.js cannot open shared object file with MariaDB Galera

How to make sure that ‘password’ is not a valid MySQL password

MySQL 5.7, utf8mb4 and the load data infile

Categories: DBA Blogs

Eight Ways To Ensure Your Applications Are Enterprise-Ready

Tue, 2016-07-12 10:00

When it comes to building database applications and solutions, developers, DBAs, engineers and architects have a lot of new and exciting tools and technologies to play with, especially with the Hadoop and NoSQL environments growing so rapidly.

While it’s easy to geek out about these cool and revolutionary new technologies, at some point in the development cycle you’ll need to stop to consider the real-world business implications of the application you’re proposing. After all, you’re bound to face some tough questions, like:

Why did you choose that particular database for our mission-critical application? Can your team provide 24/7 support for the app? Do you have a plan to train people on this new technology? Do we have the right hardware infrastructure to support the app’s deployment? How are you going to ensure there won’t be any bugs or security vulnerabilities?

If you don’t have a plan for navigating and anticipating these kinds of questions in advance, you’re likely to face difficulty getting approval for and implementing your application.

Any database applications or solutions you build or adopt for your organization must be “enterprise-ready”: secure, stable and scalable with a proven, tested capacity for deployment. They must be easy to administer and easy to support. But how do you make sure that happens?

Here are eight things to consider before declaring your proposed solution enterprise-ready:

  1. Open communications: A close working relationship between the development and operations teams goes a long way toward seamless integration of your database applications. By working together (from start to finish, as early on as possible), you can better anticipate the issues to be solved so your app or solution gets up and running faster.
  2. Platform reliability: Open source databases are great for obvious reasons: they’re free and easily customizable. But if your app is revenue-generating or mission-critical, it’s better to use a tested and proven distribution platform like Datastax Enterprise for Cassandra, Cloudera or HortonWorks for Hadoop, and Oracle or Percona for MySQL.
  3. Continuous quality: No matter which technology you use to build your app, make sure it passes rigorous quality assurance, scale and performance testing — both initially and with every new release of the software. Your vendor also needs to be proactive when it comes to releasing patches and fixing bugs.
  4. Suitable infrastructure: Consider whether the infrastructure you’re running is appropriate for the app you’re developing. If the database is set to run on multiple nodes of commodity hardware — to cover your bases in case one fails — but your operations team likes to store everything on an expensive SAN device, you might want to look into other alternatives.
  5. Experienced hosting: You’ll want to find a hosting company that is reliable, cost-effective and meets your company’s security policies. It should also have experience hosting the database technology you plan on using; that way, it knows how to respond when issues or challenges arise.
  6. Expert talent: Bring in a team of experts that can support your entire environment. While your operations team may want to take care of some elements themselves (everything up to the OS level, for instance), you’ll still want to show them that you have 24/7 support coverage available if needed. This team should be committed to continuous training and have enough people skilled with your technology to provide uninterrupted coverage.
  7. Comprehensive skills: Your team should be able to check your configurations against best practices for security, performance and availability — but don’t forget to ensure that they’re also set up for the more mundane things like systems monitoring, responding to alerts and fault finding.
  8. Ongoing costs: When tallying the running cost of your application, keep in mind that you need to incorporate the cost of the distributed version, its hosting, and 24/7 support and optimization.

With all the elements that go into getting an application enterprise-ready, it might be easier to work with a reputable partner who has the experience and expertise to help you deploy the right solution for your organization and ensure its long-term success.

Find out how Pythian’s solutions can help you succeed.

Categories: DBA Blogs

Time Slider Bug In Solaris 11: Snapshots Created Only For Leaf Datasets

Tue, 2016-06-21 14:06

Time Slider is a feature in Solaris that allows you to open past versions of your files.

It is implemented via a service which creates automatic ZFS snapshots every 15 minutes (frequent), hourly, daily, weekly and monthly. By default it retains only 3 frequent, 23 hourly, 6 daily, 3 weekly and 12 monthly snapshots.

I am using Time Slider on several Solaris 11.x servers and I found the same problem on all of them – it doesn’t create any automatic snapshots for some datasets.
For example, it doesn’t create any snapshots for the Solaris root dataset rpool/ROOT/solaris. However it creates snapshots for the leaf dataset rpool/ROOT/solaris/var. The rpool/ROOT dataset also doesn’t have any automatic snapshots, but rpool itself has snapshots, so it’s not easy to understand what is happening.

I searched for this problem and found that other people have noticed it as well.

There is a thread about it in the Oracle Community:

Solaris 11, 11.1, 11.2 and 11.3: Possible Bug in Time-Slider/ZFS Auto-Snapshot

The last message mentions the following bug in My Oracle Support:

Bug 15775093 : SUNBT7148449 time-slider only taking snapshots of leaf datasets

This bug has been created on 24-Feb-2012 but still isn’t fixed.

After more searching, I found this issue in the bug tracker of the illumos project:

Bug #1013 : time-slider fails to take snapshots on complex dataset/snapshot configuration

The original poster (OP) has encountered a problem with a complex pool configuration with many nested datasets having different values for the com.sun:auto-snapshot* properties.
He has dug into the Time Slider Python code and has proposed a change, which has been blindly accepted without proper testing and has ended up in Solaris 11.
Unfortunately, this change has introduced a serious bug which has destroyed the logic for creating recursive snapshots where they are possible.

Let me quickly explain how this is supposed to work.
If a pool has com.sun:auto-snapshot=true for the main dataset and all child datasets inherit this property, Time Slider can create a recursive snapshot for the main dataset and skip all child datasets, because they should already have the same snapshot.
However, if any child dataset has com.sun:auto-snapshot=false, Time Slider can no longer do this.
In this case the intended logic is to create recursive snapshots for all sub-trees which don’t have any excluded children and then create non-recursive snapshots for the remaining datasets which also have com.sun:auto-snapshot=true.
The algorithm is building separate lists of datasets for recursive snapshots and for single snapshots.

Here is an excerpt from /usr/share/time-slider/lib/time_slider/

        # Now figure out what can be recursively snapshotted and what
        # must be singly snapshotted. Single snapshot restrictions apply
        # to those datasets who have a child in the excluded list.
        # 'included' is sorted in reverse alphabetical order.
        for datasetname in included:
            excludedchild = False
            idx = bisect_right(everything, datasetname)
            children = [name for name in everything[idx:] if \
                        name.find(datasetname) == 0]
            for child in children:
                idx = bisect_left(excluded, child)
                if idx < len(excluded) and excluded[idx] == child:
                    excludedchild = True
            if excludedchild == False:
                # We want recursive list sorted in alphabetical order
                # so insert instead of append to the list.
                recursive.append (datasetname)

This part is the same in all versions of Solaris 11 (from 11-11 to 11.3, which is currently the latest).
If we look at the comment above the last line, it says that it should do “insert instead of append to the list”.
This is because the included list is sorted in reverse alphabetical order when it is built.
And this is the exact line that has been modified by the OP. When append is used instead of insert the recursive list becomes sorted in reverse alphabetical order as well.
The next part of the code is traversing the recursive list and is trying to skip all child datasets which already have their parent marked for recursive snapshot:

        for datasetname in recursive:
            parts = datasetname.rsplit('/', 1)
            parent = parts[0]
            if parent == datasetname:
                # Root filesystem of the Zpool, so
                # this can't be inherited and must be
                # set locally.
            idx = bisect_right(recursive, parent)
            if len(recursive) > 0 and \
               recursive[idx-1] == parent:
                # Parent already marked for recursive snapshot: so skip

This code heavily relies on the sort order and fails to do its job when the list is sorted in reverse order.
What happens is that all datasets remain in the list with child datasets being placed before their parents.
Then the code tries to create recursive snapshot for each of these datasets.
The operation is successful for the leaf datasets, but fails for the parent datasets because their children already have a snapshot with the same name.
The snapshots are also successful for the datasets in the single list (ones that have excluded children).
The rpool/dump and rpool/swap volumes have com.sun:auto-snapshot=false. That’s why rpool has snapshots.

Luckily, the original code was posted in the same thread so I just reverted the change:

            if excludedchild == False:
                # We want recursive list sorted in alphabetical order
                # so insert instead of append to the list.
                recursive.insert(0, datasetname)

After doing this, Time Slider immediately started creating snapshots for all datasets that have com.sun:auto-snapshot=true, including rpool/ROOT and rpool/ROOT/solaris.
So far I haven’t found any issue and snapshots work as expected.
There may be some issues with very complex structure like the OP had, but his change has completely destroyed the clever algorithm for doing recursive snapshots where they are possible.

Final Thoughts.

It is very strange that Oracle hasn’t paid attention to this bug and has left it hanging for more than 4 years. Maybe they consider Time Slider a non-important Desktop feature. However I think that it’s fairly useful for servers as well.

The solution is simple – a single line change, but it will be much better if this is resolved in a future Solaris 11.3 SRU. Until then I hope that my blog post will be useful for anyone who is trying to figure out why the automatic snapshots are not working as intended.

Categories: DBA Blogs

ASMCMD&gt: A Better DU, Version 2

Tue, 2016-06-21 13:03

A while ago, I posted a better “du” for asmcmd . Since then, Oracle 12cR2 beta has been released but it seems that our poor old “du” will not be improved.

I then wrote a better “better du for asmcmd” with some cool new features compared to the previous one which was quite primitive.

In this second version you will find :

  • No need to set up your environment, asmdu will do it for you
  • If no parameter is passed to the script, asmdu will show you a summary of all the diskgroups : asmdu_1
  • If a parameter (a diskgroup) is passed to the script, asmdu will show you a summary of the diskgroup size with its filling rate and the list of the directories it contains with their sizes :asmdu_2Note : you can quickly see in this screenshot that “DB9” consumes the most space in the FRA diskgroup; it is perhaps worth to have a closer look
  • A list of all running instances on the server is now shown on top of the asmdu output; I found that handy to have that list here
  • I also put some colored thresholds (red, yellow and green) to be able to quickly see which diskgroup has a space issue; you can modify it easily in the script :
# Colored thresholds (Red, Yellow, Green)
  • The only pre-requisite is that oraenv has to work

Here is the code :

# Fred Denis -- -- 2016
# - If no parameter specified, show a du of each DiskGroup
# - If a parameter, print a du of each subdirectory


# Colored thresholds (Red, Yellow, Green)

# Set the ASM env
ORACLE_SID=`ps -ef | grep pmon | grep asm | awk '{print $NF}' | sed s'/asm_pmon_//' | egrep "^[+]"`
. oraenv > /dev/null 2>&1

# A quick list of what is running on the server
ps -ef | grep pmon | grep -v grep | awk '{print $NF}' | sed s'/.*_pmon_//' | egrep "^([+]|[Aa-Zz])" | sort | awk -v H="`hostname -s`" 'BEGIN {printf("%s", "Databases on " H " : ")} { printf("%s, ", $0)} END{printf("\n")}' | sed s'/, $//'

# Manage parameters
if [[ -z $D ]]
then # No directory provided, will check all the DG
 DG=`asmcmd lsdg | grep -v State | awk '{print $NF}' | sed s'/\///'`
 SUBDIR="No" # Do not show the subdirectories details if no directory is specified
 DG=`echo $D | sed s'/\/.*$//g'`

# A header
printf "\n%25s%16s%16s%14s" "DiskGroup" "Total_MB" "Free_MB" "% Free"
printf "\n%25s%16s%16s%14s\n" "---------" "--------" "-------" "------"

# Show DG info
for X in ${DG}
 asmcmd lsdg ${X} | tail -1 |\
 awk -v DG="$X" -v W="$WARNING" -v C="$CRITICAL" '\
 {COLOR_BEGIN = "\033[1;" ;
 COLOR_END = "\033[m" ;
 { FREE = sprintf("%12d", $8/$7*100) ;
 if ((100-FREE) > W) {COLOR=YELLOW ;}
 if ((100-FREE) > C) {COLOR=RED ;}
 printf("%25s%16s%16s%s\n", DG, $7, $8, COLOR FREE COLOR_END) ; }'
printf "\n"

# Subdirs info
if [ -z ${SUBDIR} ]
(for DIR in `asmcmd ls ${D}`
 echo ${DIR} `asmcmd du ${D}/${DIR} | tail -1`
done) | awk -v D="$D" ' BEGIN { printf("\n\t\t%40s\n\n", D " subdirectories size") ;
 printf("%25s%16s%16s\n", "Subdir", "Used MB", "Mirror MB") ;
 printf("%25s%16s%16s\n", "------", "-------", "---------") ;}
 printf("%25s%16s%16s\n", $1, $2, $3) ;
 use += $2 ;
 mir += $3 ;
 END { printf("\n\n%25s%16s%16s\n", "------", "-------", "---------") ;
 printf("%25s%16s%16s\n\n", "Total", use, mir) ;} '

#* E N D O F S O U R C E *#

We use it a lot in my team and found no issue with the script so far. Let me know if you find one and enjoy!


Categories: DBA Blogs

Encrypting sensitive data in puppet using hiera-eyaml

Fri, 2016-05-27 13:36

Puppet manifests can hold a lot of sensitive information. Sensitive information like passwords or certificates are used in the configuration of many applications. Exposing them in a puppet manifest is not ideal and may conflict with an organization’s compliance policies. That is why data separation is very important aspect of secure puppet code.

Hiera is a pluggable Hierarchical Database. Hiera can help by keeping data out of puppet manifests. Puppet classes can look for data in hiera and hiera would search hierarchically and provide the first instance of value.

Although Hiera is able to provide data separation, it cannot ensure security of sensitive information. Anyone with access to the Hiera data store will be able to see the data.

Enter Hiera-eyaml. Hiera-eyaml is a backend for Hiera that provides per-value encryption of sensitive data within yaml files to be used by Puppet.

The following puppet module can be used to manage hiera with eyaml support. puppet module.

The module class can be used like below,

modules/profile/manifests/hieraconf.ppclass profile::hieraconf {
# hiera configuration
class { ‘hiera’:
hierarchy => [

The /etc/hiera.conf would look like following after the puppet run,

/etc/puppet/hiera.yaml# managed by puppet

– yaml
:logger: console
– “%{environment}/%{calling_class}”
– “%{environment}”
– “%{fqdn}”
– common
– accounts
– dev
:datadir: /etc/puppet/hieradata

Moving data to Hiera
In following example, diamond collector for Mongodb does have data like, hosts, user and password. The collector is only enabled for host.

[..] diamond::collector { ‘MongoDBCollector’:
options => {
enabled => $fqdn ? { / =>True, default => false },
hosts => ‘,’,
user => ‘grafana’,
passwd => ‘xxxx’,

To move the data to hiera, create_resources function can be used in the manifest.

modules/profile/manifests/diamond_coll.ppclass profile::diamond_coll{
[..] $mycollectors = hiera(‘diamond::collectors’, {})
create_resources(‘diamond::collector’, $mycollectors)
[..] }

Then a new yaml file can be created and diamond::collectors code for MongoDBCollector can be abstracted like below,


enabled: True
user: grafana
passwd: xxxx

Moving data to Hiera-eyaml
Hiera puppet code can be changed to following to enable eyaml.

class profile::hieraconf {
# hiera configuration
class { ‘hiera’:
hierarchy => [
eyaml => true,
eyaml_datadir => ‘/etc/puppet/hieradata’,
eyaml_extension => ‘eyaml’,

This will add eyaml backend to puppet after a puppet run on puppet server. Puppet modules does following to achieve this.

1. The hiera-eyaml gem will be installed.
2. Following keys will be created for hiera-eyaml using ‘eyaml createkeys’.


3. Update /etc/hiera.conf.

The /etc/hiera.conf would look like following after the puppet run,

/etc/puppet/hiera.yaml# managed by puppet

– eyaml
– yaml
:logger: console
– “%{environment}/%{calling_class}”
– “%{environment}”
– “%{fqdn}”
– common
– accounts
– dev
:datadir: /etc/puppet/hieradata
:datadir: /etc/puppet/hieradata
:extension: eyaml
:pkcs7_private_key: /etc/puppet/keys/private_key.pkcs7.pem
:pkcs7_public_key: /etc/puppet/keys/public_key.pkcs7.pem

Puppetmaster need to be restarted after this as changes to hiera.conf would need a restart to apply.

Using eyaml command line

Eyaml commands need to be used in a directory with keys directory(In this example /etc/puppet). Following command can be used to encrypt a password. The command would give us two options, string and block.

# eyaml encrypt -p
Enter password: ****
string: ENC[PKCS7,MIIBeQYJKoZIhvcN[..]Fg3jAmdlCLbQ] OR
block: >

To decrypt following command can be used.

# eyaml decrypt -s ‘ENC[PKCS7,MIIBeQYJKoZIhvcN[..]Fg3jAmdlCLbQ]’

The encrypted string or block can be used in hiera. While using our previous example, the hiera file would look like following. We also have to rename the file to .eyaml from .yaml.


enabled: True
user: grafana
passwd: ENC[PKCS7,MIIBeQYJKoZIhvcN[..]Fg3jAmdlCLbQ]

Encrypting certificates
Following is a standard file resource used to copy an ssl certificate..

environments/production/manifests/ { ‘/etc/logstash/certs/logstash-forwarder.crt’:
ensure => present,
mode => ‘0644’,
owner => ‘root’,
group => ‘root’,
source => ‘puppet:///modules/logstash/logstash-forwarder.crt’,

The file resource can be moved to hiera using hiera_hash.

environments/production/manifests/$fileslog = hiera_hash(‘fileslog’)
create_resources ( file, $fileslog )

The data can be added to a yaml file.

ensure : present
mode : ‘0644’
owner : ‘root’
group : ‘root’
source : ‘puppet:///modules/logstash/logstash-forwarder.key’

To encrypt data, following command can be used.

# eyaml encrypt -f modules/logstash/files/logstash-forwarder.crt

The returned string value can be added using content parameter of file resource.

[..] files:
ensure : present
mode : ‘0644’
owner : ‘root’
group : ‘root’
content : ‘ENC[PKCS7,MIIB+wYJKoZI[..]C609Oc2QUvxARaw==]’

The above examples covers encrypting strings and files, which constitutes most of the sensitive data used in puppet code. Incorporating hiera-eyaml into puppet work-flow will ensure compliance and security of sensitive data.

Categories: DBA Blogs

Understanding MySQL Fabric Faulty Server Detection

Thu, 2016-05-26 13:39

Awhile ago I found myself analyzing a MySQL fabric installation to understand why a group member was occasionally being marked as FAULTY even when the server was up and running and no failures were observed.  

                         server_uuid     address  status       mode weight
------------------------------------ ----------- ------- ---------- ------
ab0b0653-6121-11c5-55a0-007543445454 mysql1:3306 PRIMARY READ_WRITE    1.0
f34dd331-2432-11f4-a2d3-006754678533 mysql2:3306 FAULTY  READ_ONLY     1.0


Upon reviewing mysqlfabric logs, I found the following warnings were being logged from time to time:

[WARNING] 1442221217.920115 - FailureDetector(xc_grp_1) - Server (f34dd331-2432-11f4-a2d3-006754678533) in group (xc_grp_1) is unreachable


Since I was not clear under which circumstances a server is marked as FAULTY, I decided to review MySQL Fabric code (Python) to better understand the process.

The module responsible for printing this message is and more specifically, the _run method belonging to FailureDetector class. This method will loop through every server in a group, and attempt a connection to the MySQL instance running on that node. MySQLServer.Is_alive (mysql/fabric/ method is called for this purpose.

Before reviewing the failure detection process, we first need to know that there are four MySQL fabric parameters that will affect when a server is considered unstable or faulty:



Based on the above variables, the logic followed by FailureDetector._run() to mark a server as FAULTY is the following:

1) Every {DETECTION_INTERVAL/DETECTIONS} seconds, a connection against each server in the group is attempted with a timeout equal to DETECTION_TIMEOUT

2) If DETECTION_TIMEOUT is exceeded, the observed message is logged and a counter incremented

3) When this counter reaches DETECTIONS, the server is marked as “unstable” and if the last time the master changed was greater than FAILOVER_INTERVAL ago, the server is marked as FAULTY

With a better understanding of the logic followed by MySQL fabric to detect faulty nodes, I went to the configuration file to check the existing values for each of the parameters:


From the values above we can notice that each group will be polled every 2 seconds (DETECTION_INTERVAL/DETECTIONS) and that the monitored server should respond within a second for the test to be considered successful.

On high concurrency nodes, or nodes under heavy load, a high polling frequency combined with tight timeouts could cause the servers to be marked as FAULTY just because the connection attempt would not be completed or processed (in the case of high connection rates or saturated network interfaces) before the timeout occurs.

Also, having FAILOVER_INTERVAL reduced to 0, will cause the server to be marked as FAULTY even if a failover had just occurred.

A less aggressive configuration would be more appropriated for heavy loaded environment:



As with any other database clustering solution that relies on a database connection to test node status, situations where the database server would take longer to respond should also be considered. The polling frequency should be adjusted so the detection window is within an acceptable range, but the rate of monitoring connections generated is also kept to the minimum. Check timeouts should also be adjusted to avoid false positives caused by the server not being able to respond in a timely manner.


Categories: DBA Blogs

Tracefile Automation – Simplify Your Troubleshooting Tasks

Thu, 2016-05-26 10:06

Here’s a common Oracle troubleshooting scenario when a SQL statement needs tuning and/or troubleshooting:

  • log on to dev server
  • connect to database (on a different server)
  • run the SQL statement with 10046 tracing enabled
  • ssh to the database server
  • copy the trace file back to work environment
  • process the trace file.


All of this takes time. Granted, not a great deal of time, but tuning is an iterative process and so these steps will be performed multiple times. Not only are these steps a productivity killer, but they are repetitive and annoying. No one wants to keep running the same manual command over and over.

This task is ripe for some simple automation.

If both the client and database servers are some form of Unix, automating these tasks is straightforward.

Please note that these scripts require an 11g or later version of the Oracle database. These scripts are dependent on the v$diag_info view to retrieve the tracefile name. While these scripts could be made to work on 10g databases, that is left as an exercise for the reader.

Step by Step

To simplify the process it can be broken down into steps.


1. Reconnect

The first step is to create a new connection each time the SQL is executed. Doing so ensures the database session gets a new tracefile, as we want each execution to be isolated.

-- reconnect.sql

connect jkstill/XXXX@oravm


2. Get the Tracefile hostname, owner and filename

Oracle provides all the information needed.

In addition the script will set the 10046 event, run the SQL of interest and then disable the 10046 event.

Following is a snippet from the tracefile_identifier_demo.sql script.


-- column variables to capture host, owner and tracefile name
col tracehost new_value tracehost noprint
col traceowner new_value traceowner noprint
col tracefile new_value tracefile noprint

set term off head off feed off

-- get oracle owner
select username traceowner from v$process where pname = 'PMON';

-- get host name
select host_name tracehost from v$instance;

-- set tracefile identifier
alter session set tracefile_identifier = 'MYTRACEFILE';

select value tracefile from v$diag_info where name = 'Default Trace File';

set term on head on feed on

-- do your tracing here
alter session set events '10046 trace name context forever, level 12';

-- run your SQL here

alter session set events '10046 trace name context off';


In this case sql2trace.sql is a simple SELECT from a test table.  All of the scripts used here appear in Github as mentioned at the end of this article.


3. Process the Tracefile

Now that the tracefile has been created, it is time to retrieve it.

The following script scp.sql is called from tracefile_identifier_demo.sql.


col scp_src new_value scp_src noprint
col scp_target new_value scp_target noprint

set term off feed off verify off echo off

select '&&1' scp_src from dual;
select '&&2' scp_target from dual;

set feed on term on verify on


host scp &&scp_src &&scp_target

Following is an example putting it all together in tracefile_identifier_demo.sql.


SQL> @tracefile_identifier_demo

1 row selected.

PRODUCT                        VERSION              STATUS
------------------------------ -------------------- --------------------
NLSRTL                          Production
Oracle Database 12c Enterprise           64bit Production

PL/SQL                          Production
TNS for Linux:                  Production

Data Base

INSTANCE_NAME        HOST_NAME                      CURRDATE
-------------------- ------------------------------ ----------------------
js122a1             2016-05-23 16:38:11

04/02/2016 11:22:12

Session altered.

Elapsed: 00:00:00.00

OWNER        OBJECT NAME                     OBJECT_ID OBJECT_TYPE             CREATED
------------ ------------------------------ ---------- ----------------------- -------------------
SYS          OLAP_EXPRESSION                     18200 OPERATOR                2016-01-07 21:46:54
SYS          OLAP_EXPRESSION_BOOL                18206 OPERATOR                2016-01-07 21:46:54
SYS          OLAP_EXPRESSION_DATE                18204 OPERATOR                2016-01-07 21:46:54
SYS          OLAP_EXPRESSION_TEXT                18202 OPERATOR                2016-01-07 21:46:54
SYS          XMLSEQUENCE                          6379 OPERATOR                2016-01-07 21:41:25
SYS          XQSEQUENCE                           6380 OPERATOR                2016-01-07 21:41:25
SYS          XQWINDOWSEQUENCE                     6393 OPERATOR                2016-01-07 21:41:25

7 rows selected.

Elapsed: 00:00:00.00

Session altered.

Elapsed: 00:00:00.00

js122a1_ora_1725_MYTRACEFILE.trc                                                                                                                                                            100% 3014     2.9KB/s   00:00

SQL> host ls -l js122a1_ora_1725_MYTRACEFILE.trc
-rw-r----- 1 jkstill dba 3014 May 23 16:38 js122a1_ora_1725_MYTRACEFILE.trc

But Wait, There’s More!

This demo shows you how to automate the retrieval of the trace file. But why stop there?  The processing of the file can be modified as well.

Really, it isn’t even necessary to copy the script over, as the content can be retrieved and piped to your favorite command.  The script mrskew.sql for instance uses ssh to cat the tracefile, and then pipe the contents to the Method R utility, mrskew.  Note: mrskew is a commercial utility, not open source software.


-- mrskew.sql

col ssh_target new_value ssh_target noprint
col scp_filename new_value scp_filename noprint

set term off feed off verify off echo off

select '&&1' ssh_target from dual;
select '&&2' scp_filename from dual;

set feed on term on verify on


host ssh &&ssh_target 'cat &&scp_filename' | mrskew


Following is another execution of tracefile_identifier_demo.sql, this time piping output to mrskew. Only the final part of the output is shown following



Elapsed: 00:00:00.01

CALL-NAME                    DURATION       %  CALLS      MEAN       MIN       MAX
---------------------------  --------  ------  -----  --------  --------  --------
PARSE                        0.002000   33.1%      2  0.001000  0.000000  0.002000
db file sequential read      0.001211   20.0%      5  0.000242  0.000056  0.000342
FETCH                        0.001000   16.5%      1  0.001000  0.001000  0.001000
gc cr grant 2-way            0.000999   16.5%      1  0.000999  0.000999  0.000999
SQL*Net message from client  0.000817   13.5%      2  0.000409  0.000254  0.000563
Disk file operations I/O     0.000018    0.3%      2  0.000009  0.000002  0.000016
SQL*Net message to client    0.000002    0.0%      2  0.000001  0.000001  0.000001
CLOSE                        0.000000    0.0%      2  0.000000  0.000000  0.000000
EXEC                         0.000000    0.0%      2  0.000000  0.000000  0.000000
---------------------------  --------  ------  -----  --------  --------  --------
TOTAL (9)                    0.006047  100.0%     19  0.000318  0.000000  0.002000

Now we can see where all the db time was consumed for this SQL statement, and there was no need to copy the trace file to the current working directory. The same can be done for tkprof and other operations.  Please see the plan.sql and tkprof.sql scripts in the Github repository.


Wrapping It Up

A little bit of automation goes a long way. Commands that are repetitive, boring and error prone can easily be automated.  Both your productivity and your mood will soar when little steps like these are used to improve your workflow.

All files for this demo can be found at

Categories: DBA Blogs

Log Buffer #475: A Carnival of the Vanities for DBAs

Wed, 2016-05-25 14:32

This Log Buffer Edition goes through various blogs, and selects some of the top posts from Oracle, SQL Server and MySQL.


MOS Note:136697.1 – New HCHECK.SQL for Oracle Database 12c

ORAchk / EXAchk questions.

Cloud control won’t start!

ASMLib is an optional utility that can be used on Linux systems to manage Oracle ASM devices.

ORA-56841: Master Diskmon cannot connect to a CELL.

Oracle BITAND Function with Examples.


SQL Server:

Natively Compiled Stored Procedures: What they are all about

Considerations around validation errors 41305 and 41325 on memory optimized tables with foreign keys

Taking Azure SQL Data Warehouse for a Test-Drive.

Persistent PowerShell: The PowerShell Profile.

SQL Server Always On Endpoint Encryption Algorithm Compatibility Error.



Fixing MySQL scalability problems with ProxySQL or thread pool.

Installing a Web, Email & MySQL Database Cluster on Debian 8.4 Jessie with ISPConfig 3.1

Planets9s – Download the new ClusterControl 1.3 for MySQL, MongoDB & PostgreSQL

AWS Aurora Benchmark – Choose the right tool for the job

Where is the MySQL 5.7 root password?

Categories: DBA Blogs

SharePlex Replication Between Two Instances On The Same Host

Wed, 2016-05-25 10:12

Several days ago I was asked a question about SharePlex and should verify behaviour before providing the answer. I had one linux VM with two databases and needed to setup replication between them. One of them (orcl) was a target 12c EE database while the second one was a source 10g SE. Accordingly, I should use two different versions of SharePlex to mimic the questionable behaviour, so I should have two different SharePlex instances talking to each other on the same host machine. It worked pretty well for, as an example, GoldenGate, where you just setup different ports for manager processes. However, in SharePlex all the instances participating in a configuration should use the same port. The SharePlex documentation states:
“Important! The SharePlex port number must be the same one on all machines in the replication configuration so that they can communicate through TCP/IP connections.”

Of course you cannot use the same port on the same network interface for two independent SharePlex processes working from different homes. In addition, SharePlex is not asking you about a hostname either during installation, or when you start it.
In my case I had 2 interfaces in different subnets on my VM, and I could use them for my replication. But how should I tell to the SharePlex to use one or the other? The answer was simple. You should use “SP_SYS_HOST_NAME” parameter as the environment variable for your shell. Here is how I’ve done that.
I added two new hostnames for those interfaces to my /etc/hosts file to be used for my SharePlex instances:

[root@sandbox ~]$ cat /etc/hosts | grep splex	splexhost	splexstor
[root@sandbox ~]$ 

I unzipped SharePlex 8.6.3 for Oracle 10 and prepared installation. By default the installer will pick up the hostname automatically and use it during installation.
Here is my hostname and it is not what I want to use for my first SharePlex instance:

[oracle@sandbox ~]$ hostname
[oracle@sandbox ~]$ ping sandbox.localdomain
PING sandbox.localdomain ( 56(84) bytes of data.
64 bytes from sandbox.localdomain ( icmp_seq=1 ttl=64 time=0.058 ms
64 bytes from sandbox.localdomain ( icmp_seq=2 ttl=64 time=0.061 ms
--- sandbox.localdomain ping statistics ---
2 packets transmitted, 2 received, 0% packet loss, time 1468ms
rtt min/avg/max/mdev = 0.058/0.059/0.061/0.007 ms
[oracle@sandbox ~]$

What you need to do is to setup an environment variable SP_SYS_HOST_NAME. I wanted the first SharePlex listening and working on hostname splexhost using default port 2100. I should also define proper Oracle home and Oracle database SID during installation. I used standard Oracle utility oraenv to have Oracle variables setup. Here is how I started installation for the source:

[oracle@sandbox ~]$ export SP_SYS_HOST_NAME=splexhost
[oracle@sandbox ~]$ . oraenv
ORACLE_SID = [test] ?
The /u01/app/oracle/product/10.2.0/sehome_1/bin/orabase binary does not exist
You can set ORACLE_BASE manually if it is required.
[oracle@sandbox ~]$ cd /u01/distr/SharePlex
[oracle@sandbox SharePlex]$ ll
total 96736
-rwxr-xr-x. 1 oracle oinstall 99056391 Jan 11 21:56 SharePlex-8.6.3-b171-oracle100-rh-40-amd64-m64.tpm
[oracle@sandbox SharePlex]$ ./SharePlex-8.6.3-b171-oracle100-rh-40-amd64-m64.tpm
Unpacking ..................................................................

During the installation I provided directories for program and variable files, port number and information about ORACLE_SID and ORACLE_HOME. Everything went smoothly. I finished the installation by running the “ora_setup” utility creating necessary schema and objects in the source database.
For target I used the same strategy setting up Oracle variables by “oraenv” and exporting explicitly SP_SYS_HOST_NAME=splexstor. The installation worked out without any problems and I got two SharePlex instances installed to different directories.
To start a SharePlex home for a certain IP address and interface you need to explicitly setup SP_SYS_HOST_NAME to an appropriate value. Let’s see how it’s been done for source.

[oracle@sandbox ~]$ export SP_SYS_HOST_NAME=splexhost
[oracle@sandbox ~]$ . oraenv
ORACLE_SID = [test] ?
The /u01/app/oracle/product/10.2.0/sehome_1/bin/orabase binary does not exist
You can set ORACLE_BASE manually if it is required.
[oracle@sandbox ~]$ cd /u01/sp10/bin
[oracle@sandbox bin]$ telnet splexhost 2100
telnet: connect to address Connection refused

[oracle@sandbox bin]$ nohup /u01/sp10/bin/sp_cop -usp10 &
[1] 2023
[oracle@sandbox bin]$ nohup: ignoring input and appending output to `nohup.out'

[oracle@sandbox bin]$ telnet splexhost 2100
Connected to splexhost.
Escape character is '^]'.
telnet> q
Connection closed.
[oracle@sandbox bin]$

For target you have to adjust your environment variables accordingly and do the same.

[oracle@sandbox ~]$ export SP_SYS_HOST_NAME=splexstor
[oracle@sandbox ~]$ . oraenv
ORACLE_SID = [test] ? orcl
The Oracle base has been set to /u01/app/oracle
[oracle@sandbox ~]$cd /u01/sp12/bin
[oracle@sandbox bin]$ nohup /u01/sp12/bin/sp_cop -usp12 &
[1] 2066
[oracle@sandbox bin]$ nohup: ignoring input and appending output to `nohup.out'

[oracle@sandbox bin]$ 

As result we have two SharePlex instances running on the same host and talking to each other. Now we can create a sample replication. In the database schema “splex” created by “ora_setup” utility we have DEMO_SRC and DEMO_DST tables. SharePlex is using those tables for a demo replication. We can use them too and see how it works in our case. We can either modify a default pre-created sample configuration “ORA_config” or we can create a new one. I’ve created a new config “sample” on my source SharePlex and activated it:

sp_ctrl (splexhost:2100)> list config

File   Name                                         State       Datasource
--------------------------------------------------  ----------  ---------------
ORA_config                                          Inactive    o.SOURCE_SID
Last Modified At: 17-May-16 11:17    Size: 151

sp_ctrl (splexhost:2100)> create config sample

The command opens default editor and you can write your configuration.
Here is what I put to my “sample” config.


#source tables      target tables           routing map

splex.demo_src      splex.demo_dest             splexstor@o.orcl

Now we can activate config.

sp_ctrl (splexhost:2100)> activate config sample

sp_ctrl (splexhost:2100)> list config

File   Name                                         State       Datasource
--------------------------------------------------  ----------  ---------------
ORA_config                                          Inactive    o.SOURCE_SID
Last Modified At: 17-May-16 11:17    Size: 151

sample                                              Active      o.test
Last Modified At: 17-May-16 11:30    Size: 134     Internal Name: .conf.1

sp_ctrl (splexhost:2100)>

Now we can see all the processes running :

On the source:

sp_ctrl (splexhost:2100)> lstatus

Detailed Status for splexhost
Process          State                             PID     Running   Since
---------------  ------------------------------  --------  --------------------
Cop              Running                             2023  17-May-16 11:24:39
Capture          Running                             2250  17-May-16 11:30:53
  Data/Host:   o.test
Read             Running                             2279  17-May-16 11:30:53
  Data/Host:   o.test
Export           Running                             2304  17-May-16 11:30:56
  Data/Host:   splexstor
  Queue Name:  splexhost
Cmd & Ctrl       Running                             2581  17-May-16 11:40:39
  Data/Host:   splexhost

On the target:

sp_ctrl (splexstor:2100)> lstatus

Detailed Status for splexstor
Process          State                             PID     Running   Since
---------------  ------------------------------  --------  --------------------
Cop              Running                             2066  17-May-16 11:26:23
Import           Running                             2305  17-May-16 11:30:56
  Data/Host:   splexhost
  Queue Name:  splexhost
Post             Running                             2306  17-May-16 11:30:56
  Data/Host:   o.test-o.orcl
  Queue Name:  splexhost
Cmd & Ctrl       Running                             2533  17-May-16 11:38:18
  Data/Host:   splexstor

Let’s insert a row on the source :

test>  insert into splex.demo_src values ('JIM', '8001 Irvine Center Drive', '949-754-8000');

1 row created.

test> commit;

Commit complete.


And we can see the row was successfully replicated to target:

orcl> select * from splex.demo_dest;

NAME							     ADDRESS							  PHONE#
------------------------------------------------------------ ------------------------------------------------------------ ------------
JIM							     8001 Irvine Center Drive					  949-754-8000


As you can see, we were able to use one box to replicate data between two different databases, using two different SharePlex installations. The idea was simple and clear from the start, but I couldn’t find enough information in the installation guide for the SP_SYS_HOST_NAME parameter except documentation about configuring it for cluster installation. The parameter was documented in the reference section of documentation, though in reality it was not.
It could be even better if we could place the parameter inside, and not think about setting variables, but unfortunately setting the parameter in the “paramdb” didn’t work for me. Even having the parameter, you still need to setup your environment variable SP_SYS_HOST_NAME=your_host_name for non default hostname. I hope the article may help somebody save a bit of time.

Categories: DBA Blogs

Understanding The Database Options – AutoClose

Fri, 2016-05-20 14:03

In this blog post we’ll cover how AutoClose works, and why it’s recommended to disable this property.

At the time that the SQL Server service is started, the operating system logs to find the location of the master system database, and requests exclusive lock of the data and log files.

After that, SQL Server performs the reading in view of sys.master_files system and finds all the data files (.mdf, .NDF) and transaction log (.ldf) from all databases stored on the instance, and also requests the exclusive lock these files to initialize each of the databases.

The first situation in which the AutoClose property can influence the performance drop is in the acquisition of this exclusive lock on the data and log files.

If the property is off, this lock is held since service startup until you stop, however if the property is enabled, from the time when there is no more activity in the database, this lock is released and the data and log files are available to any other process.

Initially this situation may seem very interesting, because we could manipulate the data and log files and perform some administrative tasks, such as a backup.

Now imagine that during any backup, an application needs to access the database, what would happen?

The SQL Server would return an error alerting the impossibility of acquiring exclusive lock on files and the database initialization would fail.

Another big performance problem resulting from use of the AutoClose property is related to the use of the Memory Cache and Buffer areas, Plan Cache.

Whenever a query is performed, generates an execution plan that is kept in an area of memory called the Plan Cache. This area of memory is to store the generated execution plans so that they can be reused if the query is executed again.

After the execution plan generation, all search operators are executed and the data pages selected by the query are stored in an area of memory called Cache Buffer. This area of memory is to store the pages of data so that you don’t have to perform new accesses to the disk subsystem and thus optimize the next i/o requests.

When the AutoClose property is enabled and there are no more connections to the database, all the data pages and execution plans that are in memory will be deleted, thus creating a big drop of performance.

We ran a small demonstration just to be clear on this behavior.

Initially you will enable the AutoClose property in the AdventureWorks2012 database, as script below:

USE master

Then let’s perform some queries in the database AdventureWorks2012, as script below:

USE AdventureWorks2012
SELECT * FROM Person.person
SELECT * FROM Sales.salesorderheader


After the execution of queries, it is possible to analyze, through the DMV os_buffer_descriptors, the amount of data pages that have been allocated in memory to the database AdventureWorks2012, as illustrated on Figure 1:

Figure 1 – Data pages that have been allocated in memory

With the DMVs sys.dm_exec_cached_plans and sys.dm_exec_sql_text we can check execution plans that were stored in memory for queries executed, as illustrated on Figure 2.

Figure 2 – Execution plans stored in memory

So when all connections to the database AdventureWorks2012 are finished, all the memory areas will be cleaned, as the image below:

--Amount of data pages in memory
Count (*) TotalPages,
DB_NAME (database_id) DBname
Db_name (database_id)

--Amount of in-memory execution plans
COUNT (*) TotalPlanos
sys.dm_exec_sql_text (plan_handle)
[dbid] = 7 and objtype = ' Adhoc '

Figure 3 – Memory usage after close all connections

With this demonstration is extremely simple to conclude that the AutoClose property is always disabled due to performance problems that can bring to a high performance database.

Categories: DBA Blogs

An Effective Approach to Migrate Dynamic Thrift Data to CQL: Part 2

Fri, 2016-05-20 13:22

Note that this post is Part 2 of a 3-part blog series. If you haven’t read Part 1 of this series, please do so before continuing. Part 1 gives some background knowledge of the problem we’re trying to solve, as well as some fundamental concepts used in the following discussion. The chapter number sequencing also follows that from Part 1. (note: Part 3 can be found here)

4. Static and Dynamic Cassandra Table Definition

In Thrift, Cassandra tables can be defined either statically, or dynamically. When a table is defined dynamically, an application can add new columns on the fly and the column definition for each storage row doesn’t necessary need to be the same. Although a little bit flexible this way, it can be problematic as well because the dynamic column definition information is merely available in the application and invisible to outside world.

In CQL, however, tables are always statically defined, which means that any column and its type information has to be defined in advance before it can be used. Each row in a CQL table has exactly the same columns and type definitions.

In CQL, the dynamism of a Thrift table definition can be achieved through clustering column and/or more advanced column type definition like collections and user defined types (UDTs).

4.1 Static Table Definition

In Thrift, a statically defined table has column_metadata information in the table definition statement, as following:

create column family avg_grade
    with key_validation_class = Int32Type
     and comparator = UTF8Type
     and column_metadata = [
       {column_name: class_id, validation_class: Int32Type},
       {column_name: grade, validation_class: FloatType}

A strictly equivalent CQL table definition is like this (note the “WITH COMPACT STORAGE” table property):

create table avg_grade (
    key int primary key,
    class_id int,
    grade float
) with compact storage

A statically table defined in either a Thrift utility (cassandra-cli) or a CQL utility (cqlsh) can be accessed in another one with no problem. One difference between the Thrift and CQL definition is that in CQL definition, the row key definition has a name, but Thrift definition doesn’t. In this case, when accessing a table defined in Thrift, CQL uses a default name (“key”) for the row key.

4.2 Dynamic Table Definition

In Thrift, a dynamically defined table does NOT have column_metadata information in the table definition statement. Typically, time-series application adopts dynamic table definition. For example, for a sensor monitoring application, we may use sensor id as the storage row key and for each sensor, we want to record different event values detected by the sensor within a period of time. An example table definition is as following:

create column family sensor_data
   with key_validation_class = Int32Type
    and comparator = TimeUUIDType
    and default_validation_class = DecimalType;

Suppose for this table, 2 events are recorded for sensor 1 and 1 event is recorded for sensor 2. The output from cassandra-cli utility is like below:

RowKey: 1
=> (name=1171d7e0-14d2-11e6-858b-5f3b22f4f11c, value=21.5, timestamp=1462680314974000)
=> (name=23371940-14d2-11e6-858b-5f3b22f4f11c, value=32.1, timestamp=1462680344792000)
RowKey: 2
=> (name=7537fcf0-14d2-11e6-858b-5f3b22f4f11c, value=11.0, timestamp=1462680482367000)

The above shows output that the columns for each row are dynamically generated by the application and can be different between rows. In CQL, a strictly equivalent table definition and output for the above dynamic Thrift able is as below:

CREATE TABLE sensor_data (
    key int,
    column1 timeuuid,
    value decimal,
    PRIMARY KEY (key, column1)
key  | column1                              | value
   1 | 1171d7e0-14d2-11e6-858b-5f3b22f4f11c | 0E-1077248000
   1 | 23371940-14d2-11e6-858b-5f3b22f4f11c | -8.58993459E-1077939396
   2 | 7537fcf0-14d2-11e6-858b-5f3b22f4f11c | 0E-1076232192

Since the columns are generated on the fly by the application, CQL doesn’t know the column names in advance. So it uses the default column name “column1” (and also the default key name “key”) in its definition. Functionally, it can be transformed equally to a more descriptive definition as below by using “ALTER TABLE” CQL command to rename the column names (e.g. “key” to “sensor_id”, “column1” to “event_time”), as below:

CREATE TABLE sensor_data (
    sensor_id int,
    event_time timeuuid,
    value decimal,
    PRIMARY KEY (sensor_id, event_time)
4.3 Mixed Table Definition

In thrift, a table can also be in a mixed mode, which means that when a table is created, it has part of its column information being defined in column_metadata, just like a static table. But during runtime, a Thrift based application can add more columns on the go.

The table below, blogs, is such an example.  This table has one column “author” as statically defined. There are also 3 more columns (tags:category, tags:rating, and tags:recommend) for RowKey 1 defined on the fly.

create column family blogs
    with key_validation_class = Int32Type
     and comparator = UTF8Type
     and column_metadata = [
       {column_name: author, validation_class: UTF8Type}
RowKey: 1
=> (name=author, value=Donald, timestamp=1462720696717000)
=> (name=tags:category, value=music, timestamp=1462720526053000)
=> (name=tags:rating, value=A, timestamp=1462720564590000)
=> (name=tags:recommend, value=Yes, timestamp=1462720640817000)
RowKey: 2
=> (name=author, value=John, timestamp=1462720690911000)

When examining in CQL, in both table schema and data output, we can only see the columns as statically defined. The dynamically columns are NOT displayed.

    key int PRIMARY KEY,
    author text
key  | author
1    | Donald
2    | John
Categories: DBA Blogs

A Test Drive With Azure

Thu, 2016-05-19 12:55

While recently reading the latest Microsoft Azure updates, I found that we could try some VM’s from Azure Marketplace for free, even without any Azure subscription account. The feature is called “Azure test drive”. I found it interesting and decided to give a try, and see what it could offer. Here I am going to share my “driving” experience with the service.


I opened a browser where I was not logged to Azure, and went to the Test drives on Microsoft Azure webpage. There you can see a list of different VMs including DataStax, NetApp DataVault, and others. There are dozens of services listed there. I picked up a “SoftNAS Cloud NAS”, clicked and was redirected to the product page. On this page I had two options to create a VM. The main one was the usual “Create Virtual Machine” button, and the second one was located a bit lower and stated “Or, take a free test drive first”. I correctly assumed that was exactly what I needed (not a rocket science), and clicked it. I was was offered either to login or create a new “Orbitera” account for Azure Marketplace test drives.


The new service did not required an Azure account, but still asked to create one on another service. I didn’t have that account yet, and opted to create a new one. On the account creation page I was asked standard questions, filled out forms, and at the bottom confirmed that I was not a robot and pushed “sign up”. I got an email with a validation link just thirty seconds after that.
After validating my account I was able to sign up, and got to the test drive the page. I then received a button saying “Launch Test Drive”, an introduction video and couple of guides in pdf to download. One of the guides was a SoftNAS Cloud NAS Demo which helped get most out of the test drive, and another one was about the “SoftNAS Cloud NAS” itself. I pushed the button and got a counter showing how many many minutes were remaining to complete the deployment of my test VM.


It took less than 7 minutes to get link to the my Softnas interface and credentials. By the way, you don’t need to sit and wait when it is crating, you are going to get an email when the VM is ready to use and when you time is up. The test drive VM lives only one hour, and you have a counter on the test drive page with time left for you exercise. I got an hour to test SoftNAS. I later tried another test drive for SUSE HPC and got only 30 minutes available for it. I think the test time will depend on the service you want to try out, and how much time you may need to go through all the options.
Interestingly, I got a warning connecting to my newly created SoftNAS. It looked like certificate for the web interface was not good. My Firefox browser complained “The owner of has configured their website improperly.” Of course you can wave that warning and add the certificate to exceptions but, I think,for SoftNAS it will be better to fix it.


So, I played with the test VM, shared couple of volumes, and was able to mount them on another VM and on my Mac. It worked pretty well and allowed  me to make myself friendly with the interface and options. When the my hour had finished, the SoftNAS went down and all my mounts became unavailable. If you need more time you can just fire another trial and use one more hour. Yes, it would be a brand new VM, and no configuration would be saved if you had one, but, it will allow you to explore features you weren’t able to try before time was gone.


I liked the service. It provides opportunity to test and play with new products in Marketplace and decide either it suits you or not. And you can just show to somebody how to work with SoftNAS, how to share and mount a new NFS or do other things. I hope the test drive will be growing and we see more and more new brands among available products.

Categories: DBA Blogs

Log Buffer #474: A Carnival of the Vanities for DBAs

Wed, 2016-05-18 15:46

This Log Buffer Edition covers Oracle, SQL Server and MySQL blogs from across the planet.


You might be thinking “Nothing is more frustrating that encountering a string column that is full of dates”. But there is something worse than that.

Unique constraint WWV_FLOW_WORKSHEET_RPTS_UK violated.

Understanding query slowness after platform change

Database Migration and Integration using AWS DMS

Oracle BPM 12c: Browsing the SOAINFRA

SQL Server:

Adding PK Exceptions to SQLCop Tests

Is a RID Lookup faster than a Key Lookup?

Performance Surprises and Assumptions : DATEADD()

Generate INSERT scripts from SQL Server queries and stored procedure output

PowerShell Desired State Configuration: Pull Mode

Continuous Delivery from the 19th Century to TODAY


ProxySQL versus MaxScale for OLTP RO workloads

Properly removing users in MySQL

MySQL/MariaDB cursors and temp tables

Quick start MySQL testing using Docker (on a Mac!)

Query Rewrite plugin can harm performance

Categories: DBA Blogs

Join Pythian at the 2016 Cloud Computing Expo

Wed, 2016-05-18 10:46


It won’t be too long before we’re touching down in New York City to attend another Cloud Computing Expo, June 7-9 at the Javits Centre.

If you plan on attending the show, be sure to stop by Pythian’s booth (#424) to chat with our technical experts about your cloud strategy development and to find how Pythian is currently helping companies maximize performance and reduce costs when moving to the cloud.

Our experts will also be presenting on a wide range of insightful cloud topics, which you won’t want to miss.


Our Speakers:

Chris Presley (Solutions Architect) will be speaking on the factors that you should consider when moving a database onto a public cloud.

Alex Lovell-Troy (Director, DevOps) will be guiding listeners from Configuration Management to Cloud Orchestration.

Warner Chaves (Principal Consultant) will be providing a tour of Data Platforms as a Service.


Interested in meeting up at the show? Drop us a line, we’d love to connect.

We’re looking forward to seeing you there!

Categories: DBA Blogs

An Effective Approach to Migrate Dynamic Thrift Data to CQL, Part 1

Tue, 2016-05-17 16:00
1. Introduction

Recently Pythian’s Cassandra team worked on one customer’s request to copy data of several Cassandra tables between two Cassandra clusters (not Cassandra data centers). The original approach we used to copy data is through Cassandra COPY TO/FROM commands because the size of the data to be copied is not large (several hundred mega-bytes per table). The execution of the commands was successful, but for one of the tables that we did data copy, the application complained about missing data. We examined the data for that table using Cassandra cqlsh utility and found no discrepancy. After a further discussion with the customer, we realized that the source tables were created and manipulated by a Thrift based application and the application can dynamically create different columns for different rows, although each row does share a common set of statically defined columns. It is the data in these dynamic columns that are missing during the data copy process.

We addressed the issue, but in the end we felt that we should write something about Cassandra data migration from Thrift to CQL because this is quite a common problem faced by many existing Cassandra users right now, considering that Cassandra is gradually phasing out Thrift and replacing it with CQL.

This post is the first part of a three-post series. In this post (Part 1), we’re going to dive into the details of Cassandra storage engine (pre-3.0 version) and explore some fundamental concepts that are key to better understanding the discussion in the following posts. In the next post (Part 2), we’re going to explore how Cassandra tables can be defined statically, dynamically, or in a mixed mode in Thrift and what the corresponding table definition in CQL are. In the last post (Part 3), we’ll present an effective approach to migrate dynamically generated data in Thrift into a statically defined CQL table, without suffering any data loss.

1.1. Cassandra Transition from Thrift to CQL API

Apache Thrift is a software framework developed at Facebook for “scalable cross-language services development”. In early days of Cassandra, Thrift base API was the only method to develop Cassandra client applications. But with the maturity of CQL (Cassandra query language), Cassandra is gradually moving away from Thrift API to CQL API. Along with this trend,

  • Thrift based client drivers are not officially supported.
  • Thrift API will not get new Cassandra features; it exists simply for backward compatibility purpose.
  • CQL based “cqlsh” utility is replacing thrift based “cassandra-cli” utility as the main command-line tool to interact with Cassandra.

Please note that the latest version of CQL is 3.x (3.4 as of May, 2016). CQL2 is deprecated and removed for Cassandra 2.2 and later.  In the discussion below, we will simply use CQL to refer to CQL 3.

2. Overview of Internal Cassandra Data Storage Structure

Please note that since Cassandra 3.0, the underlying storage engine for Cassandra has gone through a lot of changes. The discussion in this post series is for pre-3.0 Cassandra (v 2.2 and before).

At very high level, a Cassandra table (or column family by old term) can be seen as a map of sorted map in the following format *:

     Ma<RowKey, SortedMap<ColumnKey, ColumnValue>>

A graphical representation looks like below:


Please note that although there are more complex structures such as Super Column and Composite Column, the basic idea remains the same and the representation above is good enough for us to describe the problem in this document.

Internally, such a storage structure is where both Thrift and CQL APIs are based. The difference is that Thrift API manipulates the storage structure directly, but CQL API does so through an abstraction layer and expresses the data to user in a tabular form similar to what SQL does for a relational database.

In order to make this clearer, let’s use an example to compare the outputs between cassandra-cli and cqlsh command utilities, which are based on Thrift and CQL protocols separately. The table schema is defined as below (in CQL format):

CREATE TABLE song_tags (
   id uuid,
   tag_name text,
   PRIMARY KEY (id, tag_name)

This simple table is used to maintain the song tags. After inserting several rows in this table, we examined the table content using both Thirft based “cassandra-cli” utility and CQL based “cqlsh” utility. The result is as below:


From the example above, it can be easily noticed that between Thrift and CQL, the terms “row” and “column” don’t share the same meaning and this causes some confusion when people do Thrift to CQL migration. For example,

  • In Thrift, one row means one data partition that is determined by the partition key definition. Each row has multiple columns, or more precisely “cells”. Each cell contains the time-stamp of when it is created. The name/key of the cell/column is not necessarily the name as defined in the table definition, especially when there are clustering column(s) defined (just as in the example above)
  • In CQL, one row could be one partition, or could be one part of a partition. It really depends on how the partition key and cluster key are designed

Due to such differences, I’m going to use the following terms in this document for clarification:

  • “CQL Row”, or simply “Row”, refers to a row in CQL context
  • “Storage Row”, refers to a row in Thrift context
  • “Column”, refers to a column in both CQL or Thrift contexts
  • “Cell” particularly refers to a column in Thrift context
  • “Table” refers to a table in CQL context, or a column family in Thrift context
  • “Partition” refers to a data partition determined by the hash key. In Thrift context, “Partition” and “Storage Row” has the same meaning. In CQL context, one “Partition” includes multiple “CQL Rows”.
3. Compact Storage

In CQL, a table property, called for COMPACT STRORAGE, is created for backward compatibility.  As the name suggests, tables created with this directive consumes less storage space compared with those created without this directive.

To make this clear, we also uses an example to explain. Basically, two tables are created to keep track of the average student grades for classes in a school. The table definition for them are exactly the same except that one (avg_grade) is defined without COMPACT STORAGE property, but another (avg_grade2) does. The same records of data are also inserted into both tables, as below:

CREATE TABLE avg_grade (
    student_id int,
    class_id int,
    grade double,
    PRIMARY KEY (student_id, class_id)

CREATE TABLE avg_grade2 (
    student_id int,
    class_id int,
    grade double,
    PRIMARY KEY (student_id, class_id)

insert into avg_grade(student_id, class_id, grade) values (1, 1, 75.2);
insert into avg_grade(student_id, class_id, grade) values (2, 1, 81.3);

insert into avg_grade2(student_id, class_id, grade) values (1, 1, 75.2);
insert into avg_grade2(student_id, class_id, grade) values (2, 1, 81.3);

The statements are executed in cqlsh utility. The data is then flushed from memory to disk with “nodetool flush” command. After that, sstable2json utility is used to examine the contents of the SSTable data files for both tables. Below is the output:


From the output above, we can see that tables NOT in compact storage mode has more cells within each Storage Row (e.g. the extra cell with empty value for clustering column “class”) and each Cell stores more metadata (e.g. the name of the “grade” column is added in each of the row). So just from storage perspective, having a table defined in compact storage could save quite some storage space, especially when we’re dealing with many columns and/or with complex column types like collections.

In Thrift, tables are always stored in compact storage mode. In CQL, tables by default are stored in non-compact storage mode, unless the tables are defined with “COMPACT STRORAGE” property. As a result of this, CQL tables without “COMPACT STORAGE” property are not visible in Thrift based utilities like cassandra-cli.

When a CQL table is defined with COMPACT STORAGE property, it gets the benefit of saving some disk space. However, there are also some caveats that need to pay attention to. For example:

  • It cannot have new columns added or existing columns dropped.
  • If it has a compound primary key (multiple columns), then at most one column can be defined as not part of the key.
  • It cannot have a column defined with non-frozen collection types.
    • Note that for people who are not familiar with the concept of “frozen” vs. “non-frozen” collection, a frozen collection serializes all sub-components of the collection into one single value when stored, which is treated as a blob and the whole value must be updated once. On the contrary, A non-frozen collection allows updates on individual fields.
Categories: DBA Blogs

Characterset Conversion Conundrums

Mon, 2016-05-16 11:30

Every now and then a database needs to be migrated from one characterset to another. The driver behind this is ususally to either synchronize all databases to a single standard or to support new characters or symbols like €,

Categories: DBA Blogs