Skip navigation.

BI & Warehousing

Announcing Oracle E-Business Suite for Hadoop and MongoDB

Rittman Mead Consulting - Tue, 2015-03-31 23:50

Rittman Mead are very pleased today to announce our special edition of Oracle E-Business Suite R12 running on Apache Hadoop and MongoDB, for customers looking for the ultimate in scalability, flexible data storage and lower cost-of-ownership. Powered by Hadoop technologies such as Apache Hive, HDFS and MapReduce, optional reference data storage in MongoDB and reporting provided by Apache Pig, we think this represents the ultimate platform for large deployments of Oracle’s premier ERP suite.


In this special edition of Oracle E-Business Suite R12, we’ve replaced the Oracle Database storage engine with Hadoop, MapReduce and Apache Hive, with MapReduce providing the data processing engine and Apache Hive providing a SQL layer integrated with Oracle Forms. We’ve replaced Oracle Workflow with Apache Oozie and MongoDB as the optional web-scale NoSQL database for document and reference data storage, freeing you from the size limitations of relational databases, the hassles of referential integrity and restrictions of defined schemas. Developer access is provided through Apache Hue, or you can write your own Java MapReduce and or JavaScript MongoDB API programs to extend E-Business Suite’s functionality. Best of all, there’s no need for expensive DBAs as developers handle all data-modeling themselves (with MongoDB’s collections automatically adapting to new data schemas), and HDFS’s three-node replication removes the need for complicated backup & recovery procedures.


We’ve also brought Oracle Reports into the 21st century by replacing it with Apache Pig, a high-level abstraction language for Hadoop that automatically compiles your “Pig Latin” programs into MapReduce code, and allows you to bring in data from Facebook, Twitter to combine with your main EBS dataset stored in Hive and MongoDB.


On the longer-term roadmap, features and enhancements we’re planning include:

  • Loosening the current INSERT-only restriction to allow UPDATES, DELETEs and full ACID semantics once HIVE-5317 is implemented. 
  • Adding MongoDB’s new write-reliabiity and durability so that data is always saved when EBS writes it to the underlying MongoDB collection
  • Reducing the current 5-30 minute response times to less than a minute by moving to Tez or Apache Spark
  • Providing integration with Oracle Discoverer 9iAS to delight end-users, and provide ad-hoc reporting truly at the speed-of-thought

For more details on our special Oracle E-Business Suite for Hadoop edition, contact us at – but please note we’re only accepting new customers for today, April 1st 2015. 

Categories: BI & Warehousing

Business Intelligence Cloud Service – Data Modeler

Dylan's BI Notes - Mon, 2015-03-30 19:27
These video shows how the data are loaded to BI Cloud Service and are modeled as dimensions and facts. We do not need to use the BI admin tool to create model. For BICS, we can create model using the browser.
Categories: BI & Warehousing

Oracle GoldenGate, MySQL and Flume

Rittman Mead Consulting - Mon, 2015-03-30 13:05

Back in September Mark blogged about Oracle GoldenGate (OGG) and HDFS . In this short followup post I’m going to look at configuring the OGG Big Data Adapter for Flume, to trickle feed blog posts and comments from our site to HDFS. If you haven’t done so already, I strongly recommend you read through Mark’s previous post, as it explains in detail how the OGG BD Adapter works.  Just like Hive and HDFS, Flume isn’t a fully-supported target so we will use Oracle GoldenGate for Java Adapter user exits to achieve what we want.

What we need to do now is

  1. Configure our MySQL database to be fit for duty for GoldenGate.
  2. Install and configure Oracle GoldenGate for MySQL on our DB server
  3. Create a new OGG Extract and Trail files for the database tables we want to feed to Flume
  4. Configure a Flume Agent on our Cloudera cluster to ‘sink’ to HDFS
  5. Create and configure the OGG Java adapter for Flume
  6. Create External Tables in Hive to expose the HDFS files to SQL access

OGG and Flume

Setting up the MySQL Database Source Capture

The MySQL database I will use for this example contains blog posts, comments etc from our website. We now want to use Oracle GoldenGate to capture new blog post and our readers’ comments and feed this information in to the Hadoop cluster we have running in the Rittman Mead Labs, along with other feeds, such as Twitter and activity logs.

The database has to be configured to user binary logging and also we need to ensure that the socket file can be found in /tmp/mysql.socket. You can find the details for this in the documentation. Also we need to make sure that the tables we want to extract from are using the InnoDB engine and not the default MyISAM one. The engine can easily be changed by issuing

alter table wp_mysql.wp_posts engine=InnoDB;

Assuming we already have installed OGG for MySQL on /opt/oracle/OGG/ we can now go ahead and configure the Manager process and the Extract for our tables. The tables we are interested in are


First configure the manager

-bash-4.1$ cat dirprm/mgr.prm 
PORT 7809

Now configure the Extract to capture changes made to the tables we are interested in

-bash-4.1$ cat dirprm/mysql.prm 
SOURCEDB wp_mysql, USERID root, PASSWORD password
discardfile /opt/oracle/OGG/dirrpt/FLUME.dsc, purge
EXTTRAIL /opt/oracle/OGG/dirdat/et
TRANLOGOPTIONS ALTLOGDEST /var/lib/mysql/localhost-bin.index
TABLE wp_mysql.wp_comments;
TABLE wp_mysql.wp_posts;
TABLE wp_mysql.wp_users;
TABLE wp_mysql.wp_terms;
TABLE wp_mysql.wp_term_taxonomy;

We should now be able to create the extract and start the process, as with a normal extract.

ggsci>add extract mysql, tranlog, begin now
ggsci>add exttrail ./dirdat/et, extract mysql
ggsci>start extract mysql
ggsci>info mysql
ggsci>view report mysql

We will also have to generate metadata to describe the table structures in the MySQL database. This file will be used by the Flume adapter to map columns and data types to the Avro format.

-bash-4.1$ cat dirprm/defgen.prm 
-- To generate trail source-definitions for GG v11.2 Adapters, use GG 11.2 defgen,
-- or use GG 12.1.x defgen with "format 11.2" definition format.
-- If using GG 12.1.x as a source for GG 11.2 adapters, also generate format 11.2 trails.

-- UserId logger, Password password
SOURCEDB wp_mysql, USERID root, PASSWORD password

DefsFile dirdef/wp.def

TABLE wp_mysql.wp_comments;
TABLE wp_mysql.wp_posts;
TABLE wp_mysql.wp_users;
TABLE wp_mysql.wp_terms;
TABLE wp_mysql.wp_term_taxonomy;
-bash-4.1$ ./defgen PARAMFILE dirprm/defgen.prm 

        Oracle GoldenGate Table Definition Generator for MySQL
      Version OGGCORE_12.

**            Running with the following parameters                  **
SOURCEDB wp_mysql, USERID root, PASSWORD ******
DefsFile dirdef/wp.def
TABLE wp_mysql.wp_comments;
Retrieving definition for wp_mysql.wp_comments.
TABLE wp_mysql.wp_posts;
Retrieving definition for wp_mysql.wp_posts.
TABLE wp_mysql.wp_users;
Retrieving definition for wp_mysql.wp_users.
TABLE wp_mysql.wp_terms;
Retrieving definition for wp_mysql.wp_terms.
TABLE wp_mysql.wp_term_taxonomy;
Retrieving definition for wp_mysql.wp_term_taxonomy.

Definitions generated for 5 tables in dirdef/wp.def.

Setting up the OGG Java Adapter for Flume

The OGG Java Adapter for Flume will use the EXTTRAIL created earlier as a source, pack the data up and feed to the cluster Flume Agent, using Avro and RPC. The Flume Adapter thus needs to know

  • Where is the OGG EXTTRAIL to read from
  • How to treat the incoming data and operations (e.g. Insert, Update, Delete)
  • Where to send the Avro messages to

First we create a parameter file for the Flume Adapter

-bash-4.1$ cat dirprm/flume.prm
SETENV ( GGS_USEREXIT_CONF = "dirprm/flume.props")
SOURCEDEFS ./dirdef/wp.def
DISCARDFILE ./dirrpt/flume.dsc, purge

TABLE wp_mysql.wp_comments;
TABLE wp_mysql.wp_posts;
TABLE wp_mysql.wp_users;
TABLE wp_mysql.wp_terms;
TABLE wp_mysql.wp_term_taxonomy;

There are two things to note here

  • The OGG Java Adapter User Exit is configured in a file called flume.props
  • The source tables’ structures are defined in wp.def

The flume.props file is a ‘standard’ User Exit config file

-bash-4.1$ cat dirprm/flume.props 

# Indicates if the operation timestamp should be included as part of output in the delimited separated values
# true - Operation timestamp will be included in the output
# false - Operation timestamp will not be included in the output
# Default :- true

# Optional properties to use the transaction grouping functionality

### native library config ###

javawriter.bootoptions=-Xmx32m -Xms32m -Djava.class.path=ggjava/ggjava.jar

Some points of interest here are

  • The Flume agent we will send our data to is running on port 4545 on host
  • We want each record to be prefixed with I(nsert), U(pdated) or D(delete)
  • We want each record to be postfixed with a timestamp of the transaction date
  • The Java class will do the actual work. (The curios reader can view the code in /opt/oracle/OGG/AdapterExamples/big-data/flume/src/main/java/com/goldengate/delivery/handler/flume/

Before starting up the OGG Flume, let’s first make sure that the Flume agent on bd5node1 is configure to receive our Avro message (Source) and also what to do with the data (Sink)

a1.channels = c1
a1.sources = r1
a1.sinks = k2
a1.channels.c1.type = memory
a1.sources.r1.channels = c1 
a1.sources.r1.type = avro 
a1.sources.r1.bind = bda5node1
a1.sources.r1.port = 4545
a1.sinks.k2.type = hdfs = c1
a1.sinks.k2.hdfs.path = /user/flume/gg/%{SCHEMA_NAME}/%{TABLE_NAME} 
a1.sinks.k2.hdfs.filePrefix = %{TABLE_NAME}_ 

Here we note that

  • The agent’s source (inbound data stream) is to run on port 4545 and to use avro
  • The agent’s sink will write to HDFS and store the files  in /user/flume/gg/%{SCHEMA_NAME}/%{TABLE_NAME}
  • The HDFS files will be rolled over every 1Mb (1048576 bytes)

We are now ready to head back to the webserver that runs the MySQL database and start the Flume extract, that will feed all committed MySQL transactions against our selected tables to the Flume Agent on the cluster, which in turn will write the data to HDFS

-bash-4.1$ export LD_LIBRARY_PATH=/usr/lib/jvm/jdk1.7.0_55/jre/lib/amd64/server
-bash-4.1$ export JAVA_HOME=/usr/lib/jvm/jdk1.7.0_55/
-bash-4.1$ ./ggsci
ggsci>add extract flume, exttrailsource ./dirdat/et 
ggsci>start flume
ggsci>info flume
EXTRACT    FLUME     Last Started 2015-03-29 17:51   Status RUNNING
Checkpoint Lag       00:00:00 (updated 00:00:06 ago)
Process ID           24331
Log Read Checkpoint  File /opt/oracle/OGG/dirdat/et000008
                     2015-03-29 17:51:45.000000  RBA 7742

If I now submit this blogpost I should see the results showing up our Hadoop cluster in the Rittman Mead Labs.

[oracle@bda5node1 ~]$ hadoop fs -ls /user/flume/gg/wp_mysql/wp_posts
-rw-r--r--   3 flume  flume   3030 2015-03-30 16:40 /user/flume/gg/wp_mysql/wp_posts/wp_posts_.1427729981456

We can quickly create an externally organized table in Hive to view the results with SQL

     op string, 
 ID                     int,
 post_author            int,
 post_date              String,
 post_date_gmt          String,
 post_content           String,
 post_title             String,
 post_excerpt           String,
 post_status            String,
 comment_status         String,
 ping_status            String,
 post_password          String,
 post_name              String,
 to_ping                String,
 pinged                 String,
 post_modified          String,
 post_modified_gmt      String,
 post_content_filtered  String,
 post_parent            int,
 guid                   String,
 menu_order             int,
 post_type              String,
 post_mime_type         String,
 comment_count          int,
     op_timestamp timestamp
 COMMENT 'External table ontop of GG Flume sink, landed in hdfs'
 LOCATION '/user/flume/gg/wp_mysql/wp_posts/';

hive> select post_title from gg_flume.wp_posts where op='I' and id=22112;
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks is set to 0 since there's no reduce operator
Starting Job = job_1427647277272_0017, Tracking URL =
Kill Command = /opt/cloudera/parcels/CDH-5.3.0-1.cdh5.3.0.p0.30/lib/hadoop/bin/hadoop job  -kill job_1427647277272_0017
Hadoop job information for Stage-1: number of mappers: 2; number of reducers: 0
2015-03-30 16:51:17,715 Stage-1 map = 0%,  reduce = 0%
2015-03-30 16:51:32,363 Stage-1 map = 50%,  reduce = 0%, Cumulative CPU 1.88 sec
2015-03-30 16:51:33,422 Stage-1 map = 100%,  reduce = 0%, Cumulative CPU 3.38 sec
MapReduce Total cumulative CPU time: 3 seconds 380 msec
Ended Job = job_1427647277272_0017
MapReduce Jobs Launched: 
Stage-Stage-1: Map: 2   Cumulative CPU: 3.38 sec   HDFS Read: 3207 HDFS Write: 35 SUCCESS
Total MapReduce CPU Time Spent: 3 seconds 380 msec
Oracle GoldenGate, MySQL and Flume
Time taken: 55.613 seconds, Fetched: 1 row(s)

Please leave a comment and you’ll be contributing to an OGG Flume!

Categories: BI & Warehousing

Dimensional Modeling

Dylan's BI Notes - Mon, 2015-03-30 11:06
Moved the content into a page – Dimensional Modeling
Categories: BI & Warehousing

Lifting the Lid on OBIEE Internals with Linux Diagnostics Tools

Rittman Mead Consulting - Fri, 2015-03-27 08:44

There comes the point in any sufficiently complex or difficult problem diagnosis that the log files in OBIEE alone are not sufficient for building up a complete picture of what’s going on. Even with the debug/trace data that Presentation Services and other components can be configured precisely to write you’re sometimes just left having to guess what is going on inside the black box of each of the OBIEE system components.

Here we’re going to look at a couple of examples of lifting the lid just a little bit further on what OBIEE is up to, using standard Linux diagnostic tools. These are not something to be reaching for in the first instance, but more getting on to a last resort. Almost always the problem is simpler than you’ll think, and leaping for an network trace or stack trace is going to be missing the wood for the trees.

Diagnostics in action

At a client recently they had a problem with a custom skin deployment on a clustered (scaled-out) OBIEE deployment. Amongst other things the skin was setting the default palette for charts (viewui/chart/dvt-graph-skin.xml), and they were seeing only 50% of chart executions pick up the custom palette – the other 50% used the default. If either entire node was shut down, things were fine, but otherwise it was a 50:50 chance what the colours would be. Most odd….

When you configure a custom skin in OBIEE you should be setting CustomerResourcePhysicalPath in instanceconfig.xml, along with CustomerResourceVirtualPath. Both these are necessary so that Presentation Services knows:

  1. Logical – How to generate URLs for content requested by the user’s browser (eg logos, CSS files, etc).
  2. Physical – How to physically reference files on the file system that are read by OBIEE itself (eg XML files, language files)

The way the client had configured their custom skin was that it was on storage local to each node, and in a node-specific path, something like this:

  • /data/instance1/s_custom/
  • /data/instance2/s_custom/

Writing out the details in hindsight always makes a problem’s root cause a lot more obvious, but at the time this was a tricky problem. Let’s start with the basics. Java Host is responsible for rendering charts, and for some reason, it was not reading the custom colour scheme file from the custom skin correctly. Presentation Services uses all the available Java Hosts in a cluster to request charts, presumably on some kind of round-robin basis. An analysis request on NODE01 has a 50:50 chance of getting its chart rendered on Java Host on NODE01 or Java Host on NODE02:

Turned all the log files up to 11 didn’t yield anything useful. For some reason half the time Java Host would just “ignore” the custom skin. Shutting down each node proved that in isolation the custom skin configuration on each node was definitely correct, because then the colours started working just fine. It was only when multiple Java Hosts across the nodes were active that there was a problem.

How Java Host picks up the custom skin is entirely undocumented, and I ended up figuring out that it must get the path to the skin as part of the chart request from Presentation Services. Since Presentation Services on NODE01 has been configured with a CustomerResourcePhysicalPath of /data/instance1/s_custom/, Java Host on NODE02 would fail to find this path (since on NODE02 the skin is located at /data/instance2/s_custom/) and so fall back on the default. This was my hypothesis that I then proved by making the path available for each skin available on each node (symlink, or using a standard path would also have worked, eg /data/shared/s_custom, or even better, a shared mount point), and from there everything worked just fine.

But a hypothesis and successful resolution alone wasn’t entirely enough. Sure the client was happy, but there was that little itch, that unknown “black box” system that appeared to behave how I had deduced, but could we know for sure?

tcpdump – network analysis

All of the OBIEE components communicate with each other and the outside world over TCP. When Presentation Services wants a chart rendered it does so by sending a request to Java Host – over TCP. Using the tcpdump tool we can see that in action, and inspect what gets sent:

$ sudo tcpdump -i venet0 -i lo -nnA 'port 9810'

The -A flag capture the ASCII representation of the packet; use -X if you want ASCII and hex. Port 9810 is the Java Host listen port.

The output looks like this:

You’ll note that in this case it’s intra-node communication, i.e. src and dest IP addresses are the same. The port for Java Host (9810) is clear, and we can verify that the src port (38566) is Presentation Services with the -p (process) flag of netstat:

$ sudo netstat -pn |grep 38566
tcp        0      0         ESTABLISHED 5893/sawserver

So now if you look in a bit more detail at the footer of the request from Presentation Services that tcpdump captured you’ll see loud and clear (relatively) the custom skin path with the graph customisation file:

Proof that the Presentation Services is indeed telling Java Host where to go and look for the custom attributes (including colours)! NB this is on a test environment, so that paths vary from the /data/instance... example above)

strace – system call analysis

So tcpdump gives us the smoking gun, but can we find the corpse as well? Sure we can! strace is a tool for tracing system calls, and a fantastically powerful one, but here’s a very simple example:

$strace -o /tmp/obijh1_strace.log -f -p $(pgrep -f obijh1)

-o means to write it to file, -f follows child processes as well, and -p passes the process id that strace should attach to. Have set the trace running I run my chart, and then go and pick through my trace file.

We know it’s the dvt-graph-skin.xml file that Java Host should be reading to pick up the custom colours, so let’s search for that:

Well there we go – Java Host went to go and look for the skin in the path that it was given by Presentation Services, and couldn’t find it. From there it’ll fall back on the product defaults.

Right Tool, Right Job

As as I said at the top of this article, these diagnostic tools are not the kind of things you’d be using day to day. Understanding their output is not always easy and it’s probably easy to do more harm than good with false assumption about what a trace is telling you. But, in the right situations, they are great for really finding out what is going on under the covers of OBIEE.

If you want to find out more about this kind of thing, this page is a great starting point.

Categories: BI & Warehousing

New Oracle Big Data Quick-Start Packages from Rittman Mead

Rittman Mead Consulting - Wed, 2015-03-25 05:00

Many organisations using Oracle’s business intelligence and data warehousing tools are now looking to extend their capabilities using “big data” technologies. Customers running their data warehouses on Oracle Databases are now looking to use Hadoop to extend their storage capacity whilst offloading initial data loading and ETL to this complementary platform; other customers are using Hadoop and Oracle’s Big Data Appliance to add new capabilities around unstructured and sensor data analysis, all at considerably lower-cost than traditional database storage.


In addition, as data and analytics technologies and capabilities have evolved, there has never been a better opportunity to reach further into your data to exploit more value. Big Data platforms, Data Science methods and data discovery technologies make it possible to unlock the power of your data and put it in the hands of your  executives and team members – but what is it worth to you? What’s the value to your organisation of exploring deeper int the data you have, and how do you show return?

Many organisations have begin to explore Big Data technologies to understand where they can exploit value and extend their existing analytics platforms, but what’s the business case? The good news is, using current platforms, and following architectures like the Oracle Information Management and Big Reference Architecture written in conjunction with Rittman Mead, the foundation is in place to unlock a range of growth opportunities. Finding new value in existing data, predictive analytics, data discovery, reducing the cost of data storage, ETL offloading are all starter business cases proven to return value quickly.


To help you start on the Oracle big data journey, Rittman Mead have put together two quick-start packages focuses on the most popular Oracle customer use-cases;

If this sounds like something you or your organization might be interested in, take a look at our new Quick Start Oracle Big Data and Big Data Discovery packages from Rittman Mead home page, or drop me an email at and I’ll let you know how we can help.

Categories: BI & Warehousing

Source Dependent Extract and Source Independent Load

Dylan's BI Notes - Tue, 2015-03-24 17:40
Typical data warehousing ETL process involves Extract, Transform, and Load. The concept of Source Dependent Extract (SDE) and Source Independent Load (SIL) are unique part of the BI Apps ETL since BI Apps has a universal data warehouse. Since the staging schema are designed according to the universal data warehouse design, the logic of loading data […]
Categories: BI & Warehousing

RM BI Forum 2015 : Justification Letters for Employers

Rittman Mead Consulting - Tue, 2015-03-24 03:48

(Thanks to Christian Berg @Nephentur for the suggestion, and acknowledgements to ODTUG KScope for the original idea – our favourite conference after the BI Forum)

The Rittman Mead BI Forum 2015 promises to be our best BI Forum yet, with fantastic speakers at each event, keynotes and guest speakers from Oracle and John Foreman, author of the bestselling book “Data Smart”, a data visualisation challenge and an optional one-day masterclass on delivering Oracle’s new Information Management and Big Data reference architecture by Rittman Mead’s Mark Rittman and Jordan Meyer. Uniquely amongst Oracle BI events we keep the numbers attending very limited and run just a single stream at each event, so everyone takes part in the same sessions and gets to meet all the attendees and speakers over the three days.

Sometimes though, management within organizations require special justification for team members to attend events like these, and to help you put your case together and get across the unique education and networking benefits of the Rittman Mead BI Forum, we’ve prepared justification letters for you to complete with your details, one each for the Brighton and Atlanta events. Click on the links below to download sample justification letters for the Brighton BI Forum running on May 6th-8th 2015, and the Atlanta one running the week after on May 13th-15th 2015:

Full details on the BI Forum 2015 agenda and how to register can be found on the Rittman Mead BI Forum 2015 home page, with registration open until the weekend before each event – hurry though as attendee numbers are strictly limited.

Categories: BI & Warehousing

OBIEE nqcmd Tidbits

Rittman Mead Consulting - Mon, 2015-03-23 21:42

nqcmd is the ODBC command line tool that always has, and hopefully always will, shipped with OBIEE. It enables you to manually fire queries directly at the BI Server, rather than through the usual way of Presentation Services generating Logical SQL and sending it to BI Server. This can be useful in several cases:

  1. Automated cache purging, by sending one of the SAPurge[…] ODBC commands to the BI Server, usually done as part of a script
  2. Automated execution of Logical SQL, often done to support testing scenarios
  3. Load Testing the BI Server (via a magic undocumented switch, SA_NQCMD_ADVANCED)
  4. Manual interogation of the BI Server – if you want to poke and prod nqsserver without launching a web browser, nqcmd is your friend :)

In using nqcmd there’re a couple of things I want to demonstrate here that I find useful but haven’t seen discussed [in detail] elsewhere.

Query Log via nqcmd

All BI Server queries run with a LOGLEVEL>=1 will write some log details to nqquery.log. The usual route to view this is either on the server directly itself, transferring it off with a tool such as WinSCP, or through the Administration page of OBIEE. Another option that is available is from nqcmd itself. You need to do two things:

  1. Set the environment variable SA_NQCMD_ADVANCED to Yes
  2. Include the command line arguments -ShowQueryLog -H when you invoke nqcmd. I don’t know what -H does – it’s just specified as being required for this to work.

Here’s a simple example in action:

[oracle@demo ~]$ export SA_NQCMD_ADVANCED=Yes
[oracle@demo ~]$ nqcmd -d AnalyticsWeb -u prodney -p Admin123 -ShowQueryLog -H

          Oracle BI ODBC Client
          Copyright (c) 1997-2013 Oracle Corporation, All rights reserved

Connection open with info:
[0][State: 01000] [DataDirect][ODBC lib] Application's WCHAR type must be UTF16, because odbc driver's unicode type is UTF16

        [T]able info
        [C]olumn info
        [D]ata type info
        [F]oreign keys info
        [P]rimary key info
        [K]ey statistics info
        [S]pecial columns info
        [Q]uery statement
Select Option: Q

Give SQL Statement: SET VARIABLE LOGLEVEL=1:SELECT "A - Sample Sales"."Base Facts"."1- Revenue" s_1 FROM "A - Sample Sales"
SET VARIABLE LOGLEVEL=1:SELECT "A - Sample Sales"."Base Facts"."1- Revenue" s_1 FROM "A - Sample Sales"
Row count: 1
[2015-03-21T16:36:31.000+00:00] [OracleBIServerComponent] [TRACE:1] [USER-0] [] [ecid: 0054Sw944KmFw000jzwkno0003ac0000rl,0] [tid: 56660700] [requestid: 201f0002] [sessionid: 201f0000] [username: prodney] ###
########################################### [[
-------------------- SQL Request, logical request hash:
SET VARIABLE LOGLEVEL=1:SELECT "A - Sample Sales"."Base Facts"."1- Revenue" s_1 FROM "A - Sample Sales"

[2015-03-21T16:36:31.000+00:00] [OracleBIServerComponent] [TRACE:1] [USER-34] [] [ecid: 0054Sw94mRzFw000jzwkno0003ac0000ro,0] [tid: 56660700] [requestid: 201f0002] [sessionid: 201f0000] [username: prodney] -------------------- Query Status: Successful Completion [[

[2015-03-21T16:36:31.000+00:00] [OracleBIServerComponent] [TRACE:1] [USER-28] [] [ecid: 0054Sw94mRzFw000jzwkno0003ac0000ro,0] [tid: 56660700] [requestid: 201f0002] [sessionid: 201f0000] [username: prodney] -------------------- Physical query response time 0 (seconds), id <<333971>> [[


[2015-03-21T16:36:31.000+00:00] [OracleBIServerComponent] [TRACE:1] [USER-29] [] [ecid: 0054Sw94mRzFw000jzwkno0003ac0000ro,0] [tid: 56660700] [requestid: 201f0002] [sessionid: 201f0000] [username: prodney] -------------------- Physical Query Summary Stats: Number of physical queries 1, Cumulative time 0, DB-connect time 0 (seconds) [[

[2015-03-21T16:36:31.000+00:00] [OracleBIServerComponent] [TRACE:1] [USER-33] [] [ecid: 0054Sw94mRzFw000jzwkno0003ac0000ro,0] [tid: 56660700] [requestid: 201f0002] [sessionid: 201f0000] [username: prodney] -------------------- Logical Query Summary Stats: Elapsed time 0, Response time 0, Compilation time 0 (seconds) [[


Neat! But so what? Well, I see two uses straight away:

  1. In some situations you may not have access to the filesystem of the server on which the BI Server is running. For example, as a consultant I’ve been to clients where I’m given the Administration Tool client installation only. If I want to debug an RPD that I’m developing I’ll usually want to poke around in nqquery.log to see quite what physical SQL is being generated – and now I can.
  2. There was a discussion on the EMG mailing list recently about generating Physical SQL without executing it on the database. I’m going to discuss this in the next section of this article, and to do the analysis for this rapidly I’m using the inline query log.
Generating Physical SQL for OBIEE without Executing it – SKIP_PHYSICAL_QUERY_EXEC

OBIEE generates the Physical SQL that it runs against the database dynamically, at runtime. It takes the Logical request (“Logical SQL”), runs it through the RPD and generates one or more “Physical SQL” statements to be executed on the database as required to pull back the necessary data. A question arose recently on the EMG mailing list as to whether it is possible to get the Physical SQL – without executing it. You can imagine the benefits of this (namely, regression testing) since executing the database query each time is typically going to be expensive in machine resource and time consuming.

In SampleApp v406 there is a /home/oracle/scripts/PhysicalSQLGenerator, which does two things. First off it generates the Logical SQL for a given analysis, presumably using the generateReportSQL web service. It then takes that and runs it through nqcmd, scraping the nqquery.log for the resulting Physical SQL. In all of this no database queries get run. Very cool. But what’s the “secret sauce” at play here – can we distill it down in order to use it ourselves?

First, let’s look at how the SampleApp script does it. It sets some additional request variables in the Logical SQL:

[oracle@demo PhysicalSQLGenerator]$ cat lsql-out-dir/q1.lsql
   0 s_0,
   "A - Sample Sales"."Base Facts"."1- Revenue" s_1
FROM "A - Sample Sales"

And if we extract the relevant part out of the bash script we can see that it also uses a couple of extra command line arguments (-q -NoFetch) when invoking nqcmd:

nqcmd -q -NoFetch -d AnalyticsWeb -u weblogic -p Admin123 -s lsql-out-dir/q1.lsql

When it’s run we check nqquery.log and lo-and-behold we get this: (edited for brevity)

------------------- Sending query to database named 01 - Sample App Data (ORCL) (id: <<69923>>), connection pool named Sample Relational Connection, logical request hash dd4fb54f, physical request hash 8d6f36
3d: [[
SAWITH0 AS (select sum(T42442.Revenue) as c1
     BISAMPLE.SAMP_REVENUE_FA2 T42442 /* F21 Rev. (Aggregate 2) */ )
select D1.c1 as c1, D1.c2 as c2 from ( select distinct 0 as c1,
     D1.c1 as c2
     SAWITH0 D1 ) D1 where rownum <= 5000001


Query Status: Successful Completion [[

Rows 0, bytes 24 retrieved from database query id: <<69923>> Simulation Gateway 

Physical query response time 0 (seconds), id <<69923>> Simulation Gateway

Whilst the log says it is “Sending query to database” it does no such thing, and the “Simulation Gateway” is the giveaway clue. Proof that it doesn’t connect to the database? I shut the database down, and it still worked just fine. Crude, yes, but effective.

I’ll intersperse here the little trick that I mentioned in the first part of this article : -ShowQueryLog. It’s tedious switching back and forth between nqcmd and the nqquery.log when doing this kind of testing, so let’s do it all as one:

nqcmd -H -ShowQueryLog -q -NoFetch -d AnalyticsWeb -u weblogic -p Admin123 -s lsql-out-dir/q1.lsql

Unfortunately it looks like -ShowQueryLog is mutually exclusive to -q and -NoFetch since it doesn’t return anything, even though the nqquery.log did get additional entries. But that’s fine, since by removing these two flags in order to get -ShowQueryLog to work we’re whittling down what is actually needed to generate the physical SQL on its own without database execution. Here’s the nqcmd, showing the query log inline and showing still the “Simulation Gateway” indicative of no physical query execution:

[oracle@demo PhysicalSQLGenerator]$ export SA_NQCMD_ADVANCED=Yes
[oracle@demo PhysicalSQLGenerator]$ nqcmd -H -ShowQueryLog -d AnalyticsWeb -u weblogic -p Admin123 -s lsql-out-dir/q1.lsql

          Oracle BI ODBC Client
          Copyright (c) 1997-2013 Oracle Corporation, All rights reserved


s_0          s_1
Row count: 0
[2015-03-23T05:52:57.000+00:00] [OracleBIServerComponent] [TRACE:2] [USER-0] [] [ecid: 0054Ut7AJ33Fw000jzwkno0005UZ00005Q,0] [tid: 8f194700] [requestid: 8a1e0002] [sessionid: 8a1e0000] [username: weblogic] ############################################## [[
-------------------- SQL Request, logical request hash:
   0 s_0,
   "A - Sample Sales"."Base Facts"."1- Revenue" s_1
FROM "A - Sample Sales"


[2015-03-23T05:52:57.000+00:00] [OracleBIServerComponent] [TRACE:2] [USER-18] [] [ecid: 0054Ut7AK5DFw000jzwkno0005UZ00005S,0] [tid: 8f194700] [requestid: 8a1e0002] [sessionid: 8a1e0000] [username: weblogic] -------------------- Sending query to database named 01 - Sample App Data (ORCL) (id: <<70983>>), connection pool named Sample Relational Connection, logical request hash dd4fb54f, physical request hash 8d6f363d: [[
SAWITH0 AS (select sum(T42442.Revenue) as c1
     BISAMPLE.SAMP_REVENUE_FA2 T42442 /* F21 Rev. (Aggregate 2) */ )
select D1.c1 as c1, D1.c2 as c2 from ( select distinct 0 as c1,
     D1.c1 as c2
     SAWITH0 D1 ) D1 where rownum <= 5000001

[2015-03-23T05:52:57.000+00:00] [OracleBIServerComponent] [TRACE:2] [USER-34] [] [ecid: 0054Ut7AYi0Fw000jzwkno0005UZ00005T,0] [tid: 8f194700] [requestid: 8a1e0002] [sessionid: 8a1e0000] [username: weblogic] -------------------- Query Status: Successful Completion [[

[2015-03-23T05:52:57.000+00:00] [OracleBIServerComponent] [TRACE:2] [USER-26] [] [ecid: 0054Ut7AYi0Fw000jzwkno0005UZ00005T,0] [tid: 8f194700] [requestid: 8a1e0002] [sessionid: 8a1e0000] [username: weblogic] -------------------- Rows 0, bytes 24 retrieved from database query id: <<70983>> Simulation Gateway [[

[2015-03-23T05:52:57.000+00:00] [OracleBIServerComponent] [TRACE:2] [USER-28] [] [ecid: 0054Ut7AYi0Fw000jzwkno0005UZ00005T,0] [tid: 8f194700] [requestid: 8a1e0002] [sessionid: 8a1e0000] [username: weblogic] -------------------- Physical query response time 0 (seconds), id <<70983>> Simulation Gateway [[


It’s clear that the “-q -Nofetch” parameters used in nqcmd don’t have an effect on whether the physical query is executed (they’re to do with whether nqcmd as an ODBC client pulls back and displays the data you ask for). It’s actually just a single request variable that does the job, and it goes under the rather obvious name of SKIP_PHYSICAL_QUERY_EXEC. When set to 1 it generates all the necessary physical SQL but doesn’t execute it, and the presence of “Simulation Gateway” in the log signals this.

Categories: BI & Warehousing

BI Apps has an Universal Data Warehouse

Dylan's BI Notes - Mon, 2015-03-23 13:48
BI Apps data warehouse design is based on an assumption that the data warehouse schema design is independent from OLTP system. The staging schema is an universal staging and the data warehouse is an universal data warehouse. The assumption is that no matter what the source system you are using, the business questions the BI […]
Categories: BI & Warehousing

Announcing the BI Forum 2015 Data Visualisation Challenge

Rittman Mead Consulting - Mon, 2015-03-23 03:00

The Rittman Mead BI Forum 2015 is running in Brighton from May 6th-8th 2015, and Atlanta from May 13th – 15th 2015. At this year’s events we’re introducing our first “data visualization challenge”, open to all attendees and with the dataset and scenario open from now until the start of each event. Using Oracle Business Intelligence 11g and any plugins or graphics libraries that embed and interact with OBIEE (full details and rules below), we challenge you to create the most effective dashboard or visualisation and bring it along to demo on the Friday of each event.

Help Donors Use their Funds Most Effectively

This year’s inaugural data visualisation challenge is based around the project and dataset, an online charity that makes it easy for anyone to help public school classroom projects that need funding (Rittman Mead will be making donations on behalf of the Brighton and Atlanta BI Forums to show our support for this great initiative). The project and dataset have been used in several hackathons and data crunching contests around the world, with analysis and visualisations helping to answer questions such as:

  • Why do some projects get funded, while others don’t?
  • Who donates to projects from different subjects?
  • Does proximity to schools change donation behavior?
  • What types of materials are teachers lacking the most? (eg chalk, paper, markers, etc)
  • Do poorer schools ask for more or less money from their donors?
  • If I need product x, what is the difference between projects asking for x that were successful vs those that aren’t.

More details on uses of the dataset can be found on the Donorschoose data blog, and example visualisations you could use to get some ideas and inspiration are on the Data Gallery showcase page.


Your challenge is to import this dataset into your analytical database of choice, and then create the best visualisation or dashboard in OBIEE to answer the following question: “Which project can I donate to, where my donation will have most impact?”

How Do I Take Part?

For more on the BI Forum 2015 Data Visualization Challenge including how to download the dataset and the rules of the challenge, take a look at the Rittman Mead BI Forum 2015 Data Visualisation Challenge web page where we’ve provided full details. You can either enter as an individual or as part of a team, but you must be registered for either the Brighton or Atlanta BI Forum events and come along in-person to demonstrate your solution – numbers at each event are strictly limited though, so make sure you register soon at the Rittman Mead BI Forum 2015 home page.

Categories: BI & Warehousing

Instrumenting OBIEE Database Connections For Improved Performance Diagnostics

Rittman Mead Consulting - Sun, 2015-03-22 19:30

Nearly four years ago I wrote a blog post entitled “Instrumenting OBIEE – The Final Chapter”. With hindsight, that title suffix (“The Final Chapter”) may have been a tad presumptuous and naïve of me (or perhaps I can just pretend to be ironic now and go for a five-part-trilogy style approach…). Back then OBIEE 11g had only just been released (who remembers in all its buggy-glory?), and in the subsequent years we’ve had significant patchset releases of OBIEE 11g bringing us up to now and with talk of OBIEE 12c around the corner.

As a fanboi of Cary Millsap and his approach to measuring and improving performance, instrumenting code in general – and OBIEE specifically – is something that’s interested me for a long time. The article was the final one that I wrote on my personal blog before joining Rittman Mead and it’s one that I’ve been meaning to re-publish here for a while. A recent client engagement gave me cause to revisit the instrumentation approach and refine it slightly as well as update it for a significant change made in OBIEE

What do I mean by instrumentation? Instrumentation is making your program expose information about what is being done, as well as actually doing it. Crudely put, it’s something like this:

40 GOTO 10

Rather than just firing some SQL at the database, instead we associate with that SQL information about what program sent it, and what that program was doing, who was using it, and so on. Instrumentation enables you to start analysing performance metrics against tangible actions rather than just amorphous clumps of SQL. It enables you to understand the workload profile on your system and how that’s affecting end users.

Pop quiz: which of these is going to be easier to work with for building up an understanding of a system’s behaviour and workload?

CLIENT_INFO          MODULE                    ACTION       CPU_TIME DISK_READS 
-------------------- ------------------------  ---------- ---------- ---------- 
                                               a17ff8e1         2999          1 
                                               fe6abd92         1000          6 
                                               a264593a         5999          2 
                                               571fe814         5000         12 
                                               63ea4181         7998          4 
                                               7b2fcb68        11999          5


CLIENT_INFO          MODULE                    ACTION       CPU_TIME DISK_READS
-------------------- ------------------------  ---------- ---------- ----------
06 Column Selector   GCBC Dashboard/Performan  a17ff8e1         2999          1
05 Table with condit GCBC Dashboard/Performan  a264593a         5999          2
06 View Selector     GCBC Dashboard/Performan  571fe814         5000         12
05 Table with condit GCBC Dashboard/Performan  63ea4181         7998          4
<unsaved analysis>   nqsserver@obi11-01        fe6abd92         1000          6
<unsaved analysis>   nqsserver@obi11-01        7b2fcb68        11999          5

The second one gives us the same information as before, plus the analysis being run by OBIEE, and the dashboard and page.

The benefits of instrumentation work both ways. It makes DBAs happy because they can look at resource usage on the database and trace it back easily to the originating OBIEE dashboard and user. Instrumentation also makes life much easier for troubleshooting OBIEE performance because it’s easy to trace a user’s entire session through from browser, through the BI Stack, and down into the database.

Instrumentation for OBIEE – Step By Step

If you want the ‘tl;dr’ version, the “how” rather than the “why”, here we go. For full details of why it works, see later in the article.

  1. In your RPD create three session variables. These are going to be the default values for variables that we’re going to send to the database. Make sure you set “Enable any user to set the value”.

  2. Set up a session variable initialization block to populate these variables. It is just a “dummy” init block as all you’re doing is setting them to empty/default values, so a ‘SELECT … FROM DUAL’ is just fine:

  3. For each Connection Pool you want to instrument, go to the Connection Scripts tab and add these three scripts to the Execute before query section:

    -- Pass the OBIEE user's name to CLIENT_IDENTIFIER
    call dbms_session.set_identifier('VALUEOF(NQ_SESSION.USER)')

    -- Pass the Analysis name to CLIENT_INFO
    call dbms_application_info.set_client_info(client_info=>SUBSTR('VALUEOF(NQ_SESSION.SAW_SRC_PATH)',(LENGTH('VALUEOF(NQ_SESSION.SAW_SRC_PATH)')-instr('VALUEOF(NQ_SESSION.SAW_SRC_PATH)','/',-1,1))*-1))

    -- Pass the dashboard name & page to MODULE
    -- NB OBIEE >= will set ACTION itself so there is no point setting it here (it will get overridden)
    call dbms_application_info.set_module(module_name=> SUBSTR('VALUEOF(NQ_SESSION.SAW_DASHBOARD)', ( LENGTH('VALUEOF(NQ_SESSION.SAW_DASHBOARD)') - INSTR('VALUEOF(NQ_SESSION.SAW_DASHBOARD)', '/', -1, 1) ) *- 1) || '/' || 'VALUEOF(NQ_SESSION.SAW_DASHBOARD_PG)' ,action_name=> '' );

    You can leave the comments in there, and in fact I’d recommend doing so to make it clear for future RPD developers what these scripts are for.

    Your connection pool should look like this:

    An important point to note is that you generally should not be adding these scripts to connection pools that are used for executing initialisation blocks. Initialisation block queries won’t have these request variables so if you did want to instrument them you’d need to find something else to include in the instrumentation.

Once you’ve made the above changes you should see MODULE, CLIENT_IDENTIFIER and CLIENT_INFO being populated in the Oracle system views :


SID PROGRAM CLIENT_ CLIENT_INFO              MODULE                       ACTION
--- ------- ------- ------------------------ ---------------------------- --------
 17 nqsserv prodney Geographical Analysis 2  11.10 Flights Delay/Overview 32846912
 65 nqsserv prodney Delayed Fligth % history 11.10 Flights Delay/Overview 4bc2a368
 74 nqsserv prodney Delayed Fligth % history 11.10 Flights Delay/Overview 35c9af67
193 nqsserv prodney Geographical Analysis 2  11.10 Flights Delay/Overview 10bdad6c
302 nqsserv prodney Geographical Analysis 1  11.10 Flights Delay/Overview 3a39d178
308 nqsserv prodney Delayed Fligth % history 11.10 Flights Delay/Overview 1fad81e0
421 nqsserv prodney Geographical Analysis 2  11.10 Flights Delay/Overview 4e5d36c1

You’ll note that we don’t set ACTION – that’s because OBIEE now sends a hash of the physical query text across in this column, meaning we can’t use it ourselves. Unfortunately the current version of OBIEE doesn’t store the physical query hash anywhere other than in nqquery.log, meaning that you can’t take advantage of it (i.e. link it back to data from Usage Tracking) within the database alone.

That’s all there is to it – easy! If you want to understand exactly how and why it works, read on…

Instrumentation for OBIEE – How Does it Work? Connection Pools

When OBIEE runs a dashboard, it does so by taking each analysis on that dashboard and sending a Logical Request for that analysis to the BI Server (nqsserver). The BI Server parses and compiles that Logical request into one or more Physical requests which it then sends to the source database(s).

OBIEE connects to the database via a Connection Pool which specifies the database-specific connection information including credentials, data source name (such as TNS for Oracle). The Connection Pool, as the name suggests, pools connections so that OBIEE is not going through the overhead of connecting and disconnecting for every single query that it needs to run. Instead it will open one or more connections as needed, and share that connection between queries as needed.

As well as the obvious configuration options in a connection pool such as database credentials, OBIEE also supports the option to send additional SQL to the database when it opens a connection and/or sends a new query. It’s this nice functionality that we piggy-back to enable our instrumentation.


The information that OBIEE can send back through its database connection is limited by what we can expose in variables. From the BI Server’s point of view there are three types of variables:

  1. Repository
  2. Session
  3. Request

The first two are fairly simple concepts; they’re defined within the RPD and populated with Initialisation Blocks (often known as “init blocks”) that are run by the BI Server either on a schedule (repository variables) or per user (session variables). There’s a special type of session variables known as System Session Variables, of which USER is a nice obvious example. These variables are pre-defined in OBIEE and are generally populated automatically when the user session begins (although some, like LOGLEVEL, still need an init block to set them explicitly).

The third type of variable, request variable, is slightly less obvious in function. In a nutshell, they are variables that are specified in the logical request sent to the BI Server, and are passed through to the internals of the BI Server. They’re often used for activating or disabling certain functionality. For example, you can tell OBIEE to specifically not use its cache for a request (even if it finds a match) by setting the request variable DISABLE_CACHE_HIT.

Request variables can be set manually inline in an analysis from the Advanced tab:

And they can also be set from Variable Prompts either within a report prompt or as a standalone dashboard prompt object. The point about request variables is that they are freeform; if they specify the name of an existing session variable then they will override it (if permitted), but they do not require the session variable to exist. We can see this easily enough – and see a variable request prompt in action at the same time. From the Prompts tab of an analysis I’ve added a Variable Prompt (rather than the usual Column Prompt) and given it a made up name, FOO:

Now when I run the analysis I specify a value for it:

and in the query log there’s the request variable:

-------------------- SQL Request, logical request hash:
   0 s_0,
   "A - Sample Sales"."Base Facts"."1- Revenue" s_1
FROM "A - Sample Sales"

I’ve cut the quoted Logical SQL down to illustrate the point about the variable, because what was actually there is this:

-------------------- SQL Request, logical request hash:
SET VARIABLE QUERY_SRC_CD='Report',SAW_SRC_PATH='/users/prodney/request variable example',FOO='BAR', PREFERRED_CURRENCY='USD';
   0 s_0,
   "A - Sample Sales"."Base Facts"."1- Revenue" s_1
FROM "A - Sample Sales"

which brings me on very nicely to the key point here. When Presentation Services sends a query to the BI Server it does so with a bunch of request variables set, including QUERY_SRC_CD and SAW_SRC_PATH. If you’ve worked with OBIEE for a while then you’ll recognise these names – they’re present in the Usage Tracking table S_NQ_ACCT. Ever wondered how OBIEE knows what values to store in Usage Tracking? Now you know. It’s whatever Presentation Services tells it to. You can easily test this yourself by playing around in nqcmd:

[oracle@demo ~]$ rlwrap nqcmd -d AnalyticsWeb -u prodney -p Admin123 -NoFetch

          Oracle BI ODBC Client
          Copyright (c) 1997-2013 Oracle Corporation, All rights reserved



Statement execute succeeded

and looking at the results in S_NQ_ACCT:

BIEE_BIPLATFORM@pdborcl > select to_char(start_ts,'YYYY-MM-DD HH24:MI:SS') as start_ts,saw_src_path,query_src_cd from biee_biplatform.s_nq_acct where start_ts > sysdate -1 order by start_ts;

START_TS            SAW_SRC_PATH                             QUERY_SRC_CD
------------------- ---------------------------------------- --------------------
2015-03-21 11:55:10 /users/prodney/request variable example  Report
2015-03-21 12:44:41 BAR                                      FOO
2015-03-21 12:45:26 BAR                                      FOO
2015-03-21 12:45:28 BAR                                      FOO
2015-03-21 12:46:23 BAR                                      FOO

Key takeaway here: Presentation Services defines a bunch of useful request variables when it sends Logical SQL to the BI Server:

Embedding Variables in Connection Script Calls

There are four options that we can configure when connecting to the database from OBIEE. These are:


As of OBIEE version (i.e. OBIEE >= OBIEE automatically sets the ACTION field to a hash of the physical query – for more information see Doc ID 1941378.1. That leaves us with three remaining fields (since OBIEE sets ACTION after anything we do with the Connection Pool):


The syntax of the command in a Connection Script is physical SQL and the VALUEOF function to extract the OBIEE variable:


As a simple example here is passing the userid of the OBIEE user, using the Execute before query connection script:

-- Pass the OBIEE user's name to CLIENT_IDENTIFIER
call dbms_session.set_identifier('VALUEOF(NQ_SESSION.USER)')

This would be set for every Connection Pool – but only those used for query execution – not init blocks. Run a query that is routed through the Connection Pool you defined the script against and check out V$SESSION:

SQL> select sid,program,client_identifier from v$session where program like 'nqsserver%';

       SID PROGRAM                                          CLIENT_IDENTIFIER
---------- ------------------------------------------------ ----------------------------------------------------------------
        22 (TNS V1-V3)         prodney

The USER session variable is always present, so this is a safe thing to do. But, what about SAW_SRC_PATH? This is the path in the Presentation Catalog of the analysis being executed. Let’s add this into the Connection Pool script, passing it through as the CLIENT_INFO:

-- Pass the Analysis name to CLIENT_INFO
call dbms_application_info.set_client_info(client_info=>'VALUEOF(NQ_SESSION.SAW_SRC_PATH)')

This works just fine for analyses within a dashboard, or standalone analyses that have been saved. But what about a new analysis that hasn’t been saved yet? Unfortunately the result is not pretty:

[10058][State: S1000] [NQODBC] [SQL_STATE: S1000] [nQSError: 10058] A general error has occurred.
[nQSError: 43113] Message returned from OBIS.
[nQSError: 43119] Query Failed:
[nQSError: 23006] The session variable, NQ_SESSION.SAW_SRC_PATH, has no value definition.
Statement execute failed

That’s because SAW_SRC_PATH is a request variable and since the analysis has not been saved Presentation Services does not pass it to BI Server as a request variable. The same holds true for SAW_DASHBOARD and SAW_DASHBOARD_PG if you run an analysis outside of a dashboard – the respective request variables are not set and hence the connection pool script causes the query itself to fail.

The way around this is we cheat, slightly. If you create a session variable with the names of these request variables that we want to use in the connection pool scripts then we avoid the above nasty failures. If the request variables are set then all is well, and if they are not then we fall back on whatever value we initialise the session variable with.

The final icing on the cake of the solution given above is a bit of string munging with INSTR and SUBSTR to convert and concatenate the dashboard path and page into a single string, so instead of :

/shared/01. QuickStart/_portal/1.30 Quickstart/Overview

we get:

1.30 Quickstart/Overview

Which is much easier on the eye when looking at dashboard names. Similarly with the analysis path we strip all but the last section of it.

Granular monitoring of OBIEE on the database

Once OBIEE has been configured to be more articulate in its connection to the database, it enables the use of DBMS_MONITOR to understand more about the performance of given dashboards, analyses, or queries for a given user. Through DBMS_MONITOR the collection of statistics such as DB time, DB CPU, and so can be triggered, as well as trace-file generation for queries matching the criteria specified.

As an example, here is switching on system statistics collection for just one dashboard in OBIEE, using SERV_MOD_ACT_STAT_ENABLE

call dbms_monitor.SERV_MOD_ACT_STAT_ENABLE(
    module_name=>'GCBC Dashboard/Overview'

Now Oracle stats to collect information whenever that particular dashboard is run, which we can use to understand more about how it is performing from a database point of view:

SYS@orcl AS SYSDBA> select module,stat_name,value from V$SERV_MOD_ACT_STATS;

MODULE                   STAT_NAME                           VALUE
------------------------ ------------------------------ ----------
GCBC Dashboard/Overview  user calls                             60
GCBC Dashboard/Overview  DB time                              6789
GCBC Dashboard/Overview  DB CPU                               9996
GCBC Dashboard/Overview  parse count (total)                    15
GCBC Dashboard/Overview  parse time elapsed                    476
GCBC Dashboard/Overview  execute count                          15
GCBC Dashboard/Overview  sql execute elapsed time             3887

Similarly the CLIENT_IDENTIFIER field can be used to collect statistics with CLIENT_ID_STAT_ENABLE or trigger trace file generation with CLIENT_ID_TRACE_ENABLE. What you populate CLIENT_IDENTIFIER with it up to you – by default the script I’ve detailed at the top of this article inserts the OBIEE username in it, but you may want to put the analysis here if that’s of more use from a diagnostics point of view on the database side. The CLIENT_INFO field is still available for the other item, but cannot be used with DBMS_MONITOR for identifying queries.

Categories: BI & Warehousing

More on the Rittman Mead BI Forum 2015 Masterclass : “Delivering the Oracle Big Data and Information Management Reference Architecture”

Rittman Mead Consulting - Thu, 2015-03-12 05:29

Each year at the Rittman Mead BI Forum we host an optional one-day masterclass before the event opens properly on Wednesday evening, with guest speakers over the year including Kurt Wolff, Kevin McGinley and last year, Cloudera’s Lars George. This year I’m particularly excited that together with Jordan Meyer, our Head of R&D, I’ll be presenting the masterclass on the topic of “Delivering the Oracle Big Data and Information Management Reference Architecture”.

NewImageLast year we launched at the Brighton BI Forum event a new reference architecture that Rittman Mead had collaborated with Oracle on, that incorporated big data and schema-on-read databases into the Oracle data warehouse and BI reference architecture. In two subsequent blog posts, and in a white paper published on the Oracle website a few weeks after, concepts such as the “Discovery Lab”, “Data Reservoirs” and the “Data Factory” were introduced as a way of incorporating the latest thinking, and product capabilities, into the reference architecture for Oracle-based BI, data warehousing and big data systems.

One of the problems I always feel with reference architectures though is that they tell you what you should create, but they don’t tell you how. Just how do you go from a set of example files and a vague requirement from the client to do something interesting with Hadoop and data science, and how do you turn the insights produced by that process into a production-ready, enterprise Big Data system? How do you implement the data factory, and how do you use new tools such as Oracle Big Data Discovery and Oracle Big Data SQL as part of this architecture? In this masterclass we’re looking to explain the “how” and “why” to go with this new reference architecture, based on experiences working with clients over the past couple of years.

The masterclass will be divided into two sections; the first, led by Jordan Meyer, will focus on the data discovery and “data science” parts of the Information Management architecture, going through initial analysis and discovery of datasets using R and Oracle R Enterprise. Jordan will share techniques he uses from both his work at Rittman Mead and his work with Slacker Radio, a Silicon Valley startup, and will introduce the R and Oracle R Enterprise toolset for uncovering insights, correlations and patterns in sample datasets and productionizing them as database routines. Over his three hours he’ll cover topics including: 

Session #1 – Data exploration and discovery with R (2 hours) 

1.1 Introduction to R 

1.2 Tidy Data  

1.3 Data transformations 

1.4 Data Visualization 

Session #2 – Predictive Modeling in the enterprise (1 hr)

2.1 Classification

2.2 Regression

2.3 Deploying models to the data warehouse with ORE

After lunch, I’ll take the insights and analysis patterns identified in the Discovery Lab and turn them into production big data pipelines and datasets using Oracle Data Integrator 12c, Oracle Big Data Discovery and Oracle Big Data SQL For a flavour of the topics I’ll be covering take a look at this Slideshare presentation from a recent Oracle event, and in the masterclass itself I’ll concentrate on techniques and approaches for ingesting and transforming streaming and semi-structured data, storing it in Hadoop-based data stores, and presenting it out to users using BI tools like OBIEE, and Oracle’s new Big Data Discovery.

Session # 3 – Building the Data Reservoir and Data Factory (2 hr)

3.1 Designing and Building the Data Reservoir using Cloudera CDH5 / Hortonworks HDP, Oracle BDA and Oracle Database 12c

3.2 Building the Data Factory using ODI12c & new component Hadoop KM modules, real-time loading using Apache Kafka, Spark and Spark Streaming

Session #4 – Accessing and visualising the data (1 hr)

4.1 Discovering and Analyzing the Data Reservoir using Oracle Big Data Discovery

4.2 Reporting and Dashboards across the Data Reservoir using Oracle Big Data SQL + OBIEE

You can register for a place at the two masterclasses when booking your BI Forum 2015 place, but you’ll need to hurry as we limit the number of attendees at each event in order to maximise interaction and networking within each group. Registration is open now and the two events take place in May – hopefully we’ll see you there!

Categories: BI & Warehousing

An Introduction to Analysing ODI Runtime Data Through Elasticsearch and Kibana 4

Rittman Mead Consulting - Thu, 2015-03-12 01:39

An important part of working with ODI is analysing the performance when it runs, and identifying steps that might be inefficient as well as variations in runtime against a baseline trend. The Operator tool in ODI itself is great for digging down into individual sessions and load plan executions, but for broader analysis we need a different approach. We also need to make sure we keep the data available for trend analysis, as it’s often the case that tables behind Operator are frequently purged for performance reasons.

In this article I’m going to show how we can make use of a generic method of pulling information out of an RDBMS such as Oracle and storing it in Elasticsearch, from where it can be explored and analysed through Kibana. It’s standalone, it’s easy to do, it’s free open source – and it looks and works great! Here I’m going to use it for supporting the analysis of ODI runtime information, but it is equally applicable to any time-based data you’ve got in an RDBMS (e.g. OBIEE Usage Tracking data).

Kibana is an open-source data visualisation and analysis tool, working with data stored in Elasticsearch. These tools work really well for very rapid analysis of any kind of data that you want to chuck at them quickly and work with. By skipping the process of schema definition and data modelling the time taken to the first results is drastically reduced. It enables to you quickly start “chucking about” data and getting meaning out of it before you commit full-scale to how you want to analyse it, which is what the traditional modelling route can sometimes force you to do prematurely.

ODI writes runtime information to the database, about sessions run, steps executed, time taken and rows processed. This data is important for analysing things like performance issues, and batch run times. Whilst with the equivalent runtime data (Usage Tracking) from OBIEE there is the superb RPD/Dashboard content that Oracle ship in SampleApp v406, for ODI the options aren’t as vast, ultimately being based on home-brew SQL against the repository tables using the repository schema documentation from Oracle. Building an OBIEE metadata model against the ODI schema is one option, but then requires an OBIEE server on which to run it – or merging into an existing OBIEE deployment – which means that it can become more hassle than it’s worth. It also means a bunch of up-front modelling before you get any kind of visualisations and data out. By copying the data across into Elasticsearch it’s easy to quickly build analyses against it, and has the additional benefit of retaining the data as long as you’d like meaning that it’s still available for long-term trend analysis once the data’s been purged from the ODI repository itself.

Let’s take a bit of a walk through the ODI dashboard that I’ve put together. First up is a view on the number of sessions that have run over time, along with their duration. For duration I’ve shown 50th (median), 75th and 95th percentiles to get an idea of the spread of session runtimes. At the moment we’re looking at all sessions, so it’s not surprising that there is a wide range since there’ll always be small sessions and longer ones:

Next up on the dashboard comes a summary of top sessions by runtime, both cumulative and per-session. The longest running sessions are an obvious point of interest, but cumulative runtime is also important; something may only take a short while to run when compared to some singular long-running sessions, but if it runs hundreds of times then it all adds up and can give a big performance boost if time is shaved off it.

Plotting out session execution times is useful to be able to see both when the longest running sessions ran:

The final element on this first dashboard is one giving the detail for each of the top x long-running session executions, including the session number so that it can be examined in further detail through the Operator tool.

Kibana dashboards are interactive, so you can click on a point in a graph to zoom in on that time period, as well as click & drag to select an arbitrary range. The latter technique is sometimes known as “Brushing”, and if I’m not describing it very well have a look at this example here and you’ll see in an instant what I mean.

As you focus on a time period in one graph the whole dashboard’s time filter changes, so where you have a table of detail data it then just shows it for the range you’ve selected. Notice also that the granularity of the aggregation changes as well, from a summary of every three hours in the first of the screenshots through to 30 seconds in the last. This is a nice way of presenting a summary of data, but isn’t always desirable (it can mask extremes and abnormalities) so can be configured to be fixed as well.

Time isn’t the only interaction on the dashboard – anything that’s not a metric can be clicked on to apply a filter. So in the above example where the top session by cumulative time are listed out we might want to find out more about the one with several thousand executions

Simply clicking on it then filters the dashboard and now the session details table and graph show information just for that session, including duration, and rows processed:

Session performance analysis

As an example of the benefit of using a spread of percentiles we can see here is a particular session that had an erratic runtime with great variation, that then stabilised. The purple line is the 95th percentile response time; the green and blue are 50th and 75th respectively. It’s clear that whilst up to 75% of the sessions completed in about the same kind of time each time they ran, the remaining quarter took anything up to five times as long.

One of the most important things in performance is ensuring consistent performance, and that is what happens here from about half way along the horizontal axis at c.February:

But what was causing the variation? By digging a notch deeper and looking at the runtime of the individual steps within the given session it can be seen that the inconsistent runtime was caused by a single step (the green line in this graph) within the execution. When this step’s runtime stabilises, so does the overall performance of the session:

This is performing a port-mortem on a resolved performance problem to illustrate how useful the data is – obviously if there were still a performance problem we’d have a clear path of investigation to pursue thanks to this data.


Data’s pulled from the ODI repository tables using Elasticsearch JDBC river, from where it’s stored and indexed in Elasticsearch, and presented through Kibana 4 dashboards.


The data load from the repository tables into Elasticsearch is incremental, meaning that the solution works for both historical analysis and more immediate monitoring too. Because the data’s actually stored in Elasticsearch for analysis it means the ODI repository tables can be purged if required and you can still work with a full history of runtime data in Kibana.

If you’re interested in finding out more about this solution and how Rittman Mead can help you with your ODI and OBIEE implementation and monitoring needs, please do get in touch.

Categories: BI & Warehousing

Announcing the Special Guest Speakers for Brighton & Atlanta BI Forum 2015

Rittman Mead Consulting - Mon, 2015-03-09 08:13

As well as a great line-up of speakers and sessions at each of the Brighton & Atlanta Rittman Mead BI Forum 2015 events in May, I’m very pleased to announce our two guest speakers who’ll give the second keynotes, on the Thursday evening of the two events just before we leave for the restaurant and the appreciation events. This year our special guest speaker in Atlanta is John Foreman, Chief Data Scientist at MailChimp and author of the book “Data Smart: Using Data Science to Transform Information into Insight”; and in Brighton we’re delighted to have Reiner Zimmerman, Senior Director of Product Management at Oracle US and the person behind the Oracle DW & Big Data Global Leaders program.


I first came across John Foreman when somebody recommended his book to me, “Data Smart”, a year or so ago. At that time Rittman Mead were getting more-and-more requests from our customers asking us to help with their advanced analytics and predictive modelings needs, and I was looking around for resources to help myself and the team get to grips with some of the more advanced modelings and statistical techniques Oracle’s tools now support – techniques such as clustering and pattern matching, linear regression and genetic algorithms.

One of the challenges when learning these sorts of techniques is not getting to caught up in the tools and technology – R was our favoured technology at the time, and there’s lots to it – so John’s book was particularly well-timed as it goes through these types of “data science” techniques but focuses on Microsoft Excel as the analysis tool, with simple examples and a very readable style.

Back in his day job, John is Chief Data Scientist at MailChimp and has become a particularly in-demand speaker following the success of his book, and I was very excited to hear from Charles Elliott, our Practice Manager for Rittman Mead America, that he lived near John in Atlanta and had arranged for him to keynote at our Atlanta BI Forum event. His Keynote will be entitled “How Mailchimp used qualitative and quantitative analysis to build their next product” and we’re very much looking forward to meeting him at our event in Atlanta on May 13th-15th 2015.


Our second keynote speaker at the Brighton Rittman Mead BI Forum 2015 event is non-other than Reiner Zimmerman, best known in EMEA for organising the Oracle DW Global Leaders Program. We’ve known Reiner for several years now as Rittman Mead are one of the associate sponsors for the program, which aims to bring together the leading organizations building data warehouse and big data systems on the Oracle Engineered Systems platform.

A bit like the BI Forum (but even more exclusive), the DW Global Leaders program holds meetings in the US, EMEA and AsiaPac over the year and is a fantastic networking and knowledge-sharing group for an exclusive set of customers putting together the most cutting-edge DW and big data systems on the latest Oracle technology. Reiner’s also an excellent speaker and a past visitor to the BI Forum, and his session entitled “Hadoop and Oracle BDA customer cases from around the world” will be a look at what customers are really doing, and the value they’re getting, from building big data systems on the Oracle platform.

Registration is now open for both the Brighton and Atlanta BI Forum 2015 events, with full details including the speaker line-up and how to register on the event website. Keep an eye on the blog for more details of both events later this week including more on the masterclass by myself and Jordan Meyer, and a data visualisation “bake-off” we’re going to run on the second day of each event. Watch this space…!

Categories: BI & Warehousing

Extracting Data from Cloud Apps

Dylan's BI Notes - Thu, 2015-03-05 15:20
I think that it would be easier if the cloud application can be aware of the data integration needs and publish the interfaces proactively. Here are some basic requirements for the applications that can be considered as data integration friendly: 1. Publish the object data model This is required for source analysis. For example, here is […]
Categories: BI & Warehousing

Rittman Mead BI Forum 2015 Now Open for Registration!

Rittman Mead Consulting - Wed, 2015-03-04 09:46

I’m very pleased to announce that the Rittman Mead BI Forum 2015, running in Brighton and Atlanta in May 2015, is now open for registration.

Back for its seventh successful year, the Rittman Mead BI Forum once again will be showcasing the best speakers and presentations on topics around Oracle Business Intelligence and data warehousing, with two events running in Brighton, UK and Atlanta, USA in May 2015. The Rittman Mead BI Forum is different to other Oracle tech events in that we keep the numbers attending limited, topics are all at the intermediate-to-expert level, and we concentrate on just one topic – Oracle Business Intelligence Enterprise Edition, and the technologies and products that support it.


As in previous years, the BI Forum will run on two consecutive weeks, starting in Brighton and then moving over to Atlanta for the following week. Here’s the dates and venue locations:

This year our optional one-day masterclass will be delivered by Jordan Meyer, our Head of R&D, and myself and will be on the topic of “Delivering the Oracle Big Data and Information Management Reference Architecture” that we launched last year at our Brighton event. Details of the masterclass, and the speaker and session line up at the two events are on the Rittman Mead BI Forum 2015 homepage

Each event has its own agenda, but both will focus on the technology and implementation aspects of Oracle BI, DW, Big Data and Analytics. Most of the sessions run for 45 minutes, but on the first day we’ll be holding a debate and on the second we’ll be running a data visualization “bake-off” – details on this, the masterclass and the keynotes and our special guest speakers will be revealed on this blog over the next few weeks – watch this space!

Categories: BI & Warehousing

Creating Real-Time Search Dashboards using Apache Solr, Hue, Flume and Cloudera Morphlines

Rittman Mead Consulting - Wed, 2015-03-04 01:19

Late last week Cloudera published a blog post on their developer site on building a real-time log analytics dashboard using Apache Kafka, Cloudera Search and Hue. As I’d recently been playing around with Oracle Big Data Discovery with our website log data as the data source, and as we’ve also been doing the same exercise in our development labs using ElasticSearch and Kibana I thought it’d be interesting to give it a go; partly out of curiosity around how Solr, Kafka and Hue search works and compares to Elasticsearch, but also to try and work out what extra benefit Big Data Discovery gives you above and beyond free and open-source tools.


In the example, Apache web log data is read from the Linux server via a Flume syslog source, then fed into Apache Kafka as the transport mechanism before being loaded into Solr using a data transformation framework called “morphlines”. I’ve been looking at Kafka as an alternative to Flume for ingesting data into a Hadoop system for a while mainly because of the tireless advocacy of Cloudera’s Gwen Shapira (Oracle ACE, ex-Pythian, now at Cloudera) who I respect immensely and has a great background in Oracle database administration as well as Hadoop, and because it potentially offers some useful benefits if used instead of, or more likely alongside, Flume – a publish-subscribe model vs. push, the ability to have multiple consumers as well as publishers, and a more robust transport mechanism that should avoid data loss when an agent node goes down. Kafka is now available as a parcel and service descriptor that you can download and then install within CDH5, and so I set up a separate VM in my Hadoop cluster as a Kafka broker and also installed Solr at the same time.


Working through the example, in the end I went with a slightly different and simplified approach that swapped the syslog Flume source for an Apache Server file tailing source, as our webserver was on a different host to the Flume agent and I’d previously set this up before for an earlier blog post. I also dropped the Kafka element as the Cloudera article wasn’t that clear to me whether it’d work in its published form or needed amending to use with Kafka (“To get data from Kafka, parse it with Morphlines, and index it into Solr, you can use an almost identical configuration”), and so I went with an architecture that looked like this:


Compared to Big Data Discovery, this approach has got some drawbacks, but some interesting benefits. From a drawback perspective, Apache Solr (or Cloudera Search as it’s called in CDH5, where Cloudera have integrated Solr with HDFS storage) needs some quite fiddly manual setup that’s definitely an IT task, rather than the point-and-click dataset setup that you get with Big Data Discovery. In terms of benefits though, apart from being free it’s potentially more scalable than Big Data Discovery as BDD has to sample the full Hadoop dataset and fit that sample (typically 1m rows, or 1-5% of the full dataset) into BDD’s Endeca Server-based DGraph engine; Solr, however, indexes the whole Hadoop dataset and can store its indexes and log files within HDFS across the cluster – potentially very interesting if it works.

Back to drawbacks though, the first complication is that Solr’s configuration settings in this Cloudera Search incarnation are stored in Apache Zookeeper, so you first have to download a template copy of the collection files (schema, index etc) from Zookeeper using solrctl, the command-line tool for SolrCloud (Solr running on a distributed cluster, as it is with Cloudera Search)

solrctl --zk bda5node2:2181/solr instancedir --generate $HOME/accessCollection

Then – and this again is a tricky part compared to Big Data Discovery – you have to edit the schema.xml file that Solr uses to determine which fields to index, what their datatypes are and so on. The Cloudera blog post points to a Github repo with the required schema.xml file for Apache Combined Log Format input files, I found I had to add an extra entry for the “text” field name before Solr would index properly, added at the end of the file except here:

<field name="id" type="string" indexed="true" stored="true" required="true" multiValued="false" />
   <field name="time" type="tdate" indexed="true" stored="true" />
   <field name="record" type="text_general" indexed="true" stored="false" multiValued="true"/>
   <field name="client_ip" type="string" indexed="true" stored="true" />
   <field name="code" type="string" indexed="true" stored="true" />
   <field name="user_agent" type="string" indexed="true" stored="true" />
   <field name="protocol" type="string" indexed="true" stored="true" />   
   <field name="url" type="string" indexed="true" stored="true" />   
   <field name="request" type="string" indexed="true" stored="true" />
   <field name="referer" type="string" indexed="true" stored="true" />
   <field name="bytes" type="string" indexed="true" stored="true" />
   <field name="method" type="string" indexed="true" stored="true" />
   <field name="extension" type="string" indexed="true" stored="true" />   
   <field name="app" type="string" indexed="true" stored="true" />      
   <field name="subapp" type="string" indexed="true" stored="true" />
   <field name="device_family" type="string" indexed="true" stored="true" />
   <field name="user_agent_major" type="string" indexed="true" stored="true" />   
   <field name="user_agent_family" type="string" indexed="true" stored="true" />
   <field name="os_family" type="string" indexed="true" stored="true" />   
   <field name="os_major" type="string" indexed="true" stored="true" />
   <field name="region_code" type="string" indexed="true" stored="true" />
   <field name="country_code" type="string" indexed="true" stored="true" />
   <field name="city" type="string" indexed="true" stored="true" />
   <field name="latitude" type="float" indexed="true" stored="true" />
   <field name="longitude" type="float" indexed="true" stored="true" />
   <field name="country_name" type="string" indexed="true" stored="true" />
   <field name="country_code3" type="string" indexed="true" stored="true" />
   <field name="_version_" type="long" indexed="true" stored="true"/>
   <field name="text" type="text_general" indexed="true" stored="false" multiValued="true"/>
   <dynamicField name="ignored_*" type="ignored"/>

Then you have to upload the solr configuration settings to Zookeeper, and then configure Solr to use this particular set of Zookeeper Solr settings (note the “—create” before the accessCollection collection name in the second command, this was missing from the Cloudera steps but is needed to be a valid solrctl command)

solrctl --zk bda5node2:2181/solr instancedir --create accessCollection $HOME/accessCollection
solrctl --zk bda5node2:2181/solr --create accessCollection -s 1

At this point you should be able to go to the Solr web admin page within the CDH cluster (, in my case), and see the collection (a distributed Solr index) listed with the updated index schema.


Next I configure the Flume source agent on the RM webserver, using this Flume conf file:

## Local instalation: /etc/flume1.5.0
## configuration file location:  /etc/flume1.5.0/conf/conf
## bin file location: /etc/flume1.5.0/conf/bin
## START Agent: bin/flume-ng agent -c conf -f conf/flume-src-agent.conf -n source_agent
source_agent.sources = apache_server
source_agent.sources.apache_server.type = exec
source_agent.sources.apache_server.command = tail -f /etc/httpd/logs/access_log
source_agent.sources.apache_server.batchSize = 1
source_agent.sources.apache_server.channels = memoryChannel
source_agent.sources.apache_server.interceptors = itime ihost itype
source_agent.sources.apache_server.interceptors.itime.type = timestamp
source_agent.sources.apache_server.interceptors.ihost.type = host
source_agent.sources.apache_server.interceptors.ihost.useIP = false
source_agent.sources.apache_server.interceptors.ihost.hostHeader = host
source_agent.sources.apache_server.interceptors.itype.type = static
source_agent.sources.apache_server.interceptors.itype.key = log_type
source_agent.sources.apache_server.interceptors.itype.value = apache_access_combined
source_agent.channels = memoryChannel
source_agent.channels.memoryChannel.type = memory
source_agent.channels.memoryChannel.capacity = 100
## Send to Flume Collector on Hadoop Node
source_agent.sinks = avro_sink
source_agent.sinks.avro_sink.type = avro = memoryChannel
source_agent.sinks.avro_sink.hostname =
source_agent.sinks.avro_sink.port = 4545

and then I set up a Flume sink agent as part of the Flume service using Cloudera Manager, initially set as “stopped”.


The Flume configuration file for this sink agent is where the clever stuff happens.

collector.sources = AvroIn
collector.sources.AvroIn.type = avro
collector.sources.AvroIn.bind = bda5node5
collector.sources.AvroIn.port = 4545
collector.sources.AvroIn.channels = mc1 mc2

collector.channels = mc1 mc2
collector.channels.mc1.type = memory
collector.channels.mc1.transactionCapacity = 1000
collector.channels.mc1.capacity = 100000
collector.channels.mc2.type = memory
collector.channels.mc2.capacity = 100000
collector.channels.mc2.transactionCapacity = 1000

collector.sinks = LocalOut MorphlineSolrSink

collector.sinks.LocalOut.type = file_roll = /tmp/flume/website_logs
collector.sinks.LocalOut.sink.rollInterval = 0 = mc1

collector.sinks.MorphlineSolrSink.type = org.apache.flume.sink.solr.morphline.MorphlineSolrSink
collector.sinks.MorphlineSolrSink.morphlineFile = /tmp/morphline.conf = mc2

The interesting bit here is the MorphlineSolrSink flume sink. This Flume sink type routes flume events to a morphline script that in turn copies the log data into the HDFS storage area used by Solr, and passes it to Solr for immediate indexing. Cloudera Morphlines is a command-based lightweight ETL framework designed to transform streaming data from Flume, Spark and other sources and load it into HDFS, HBase or in our case, Solr. Morphlines config files define ETL routines that then call  extensible morphlines Kite SDK functions to perform transformations on incoming data streams such as

  • Split webserver request fields into HTTP protocol, method and URL requested
  • In conjunction with the Maxmind GeoIP database, generate the country, city and geocode for a given IP address
  • Converting dates and times in string format to a Solr-format date and timestamp

with the output then being passed to Solr in this instance, along with the UUID and other metadata Solr needs, for loading to the Solr index, or “collection” as its termed when it’s running across the cluster (note the full log files aren’t stored by this process into HDFS, just the Solr indexes and transaction logs). The morphlines config file I used is below, based on the one provided in the Github repo accompanying the Cloudera blog post – note though that you need to download and setup the Maxmind GeoIP database file, and install the Python pip utility and a couple of pip packages before this will work:

# Specify server locations in a SOLR_LOCATOR variable;
# used later in variable substitutions
# Change the zkHost to point to your own Zookeeper quorum
    # Name of solr collection
    collection : accessCollection
    # ZooKeeper ensemble
    zkHost : "bda5node2:2181/solr"
# Specify an array of one or more morphlines, each of which defines an ETL
# transformation chain. A morphline consists of one or more (potentially
# nested) commands. A morphline is a way to consume records (e.g. Flume events,
# HDFS files or blocks), turn them into a stream of records, and pipe the stream
# of records through a set of easily configurable transformations on it's way to
# Solr (or a MapReduceIndexerTool RecordWriter that feeds via a Reducer into Solr).
morphlines : [
    # Name used to identify a morphline. E.g. used if there are multiple morphlines in a
    # morphline config file
    id : morphline1
    # Import all morphline commands in these java packages and their subpackages.
    # Other commands that may be present on the classpath are not visible to this morphline.
    importCommands : ["org.kitesdk.**", "org.apache.solr.**"]
    commands : [
        ## Read the email stream and break it up into individual messages.
        ## The beginning of a message is marked by regex clause below
        ## The reason we use this command is that one event can have multiple
        ## messages
        readCSV {
    separator:  " "
            columns:  [client_ip,C1,C2,time,dummy1,request,code,bytes,referer,user_agent,C3]
    ignoreFirstLine : false
            quoteChar : "\""
            commentPrefix : ""
            trim : true
            charset : UTF-8
split { 
inputField : request
outputFields : [method, url, protocol]          
separator : " "        
isRegex : false      
#separator : """\s*,\s*"""        
#  #isRegex : true      
addEmptyStrings : false
trim : true          
split { 
inputField : url 
outputFields : ["", app, subapp]          
separator : "\/"        
isRegex : false      
#separator : """\s*,\s*"""        
#  #isRegex : true      
addEmptyStrings : false
trim : true          
userAgent {
inputField : user_agent
outputFields : {
user_agent_family : "@{ua_family}"
user_agent_major  : "@{ua_major}"
device_family     : "@{device_family}"
os_family         : "@{os_family}"
os_major  : "@{os_major}"
#Extract GEO information
geoIP {
            inputField : client_ip
            database : "/tmp/GeoLite2-City.mmdb"
# extract parts of the geolocation info from the Jackson JsonNode Java 
# # object contained in the _attachment_body field and store the parts in
# # the given record output fields:      
extractJsonPaths {
flatten : false
paths : { 
country_code : /country/iso_code
country_name : /country/names/en
                region_code  : /continent/code
#"/subdivisions[]/names/en" : "/subdivisions[]/names/en"     
#"/subdivisions[]/iso_code" : "/subdivisions[]/iso_code"     
city : /city/names/en
#/postal/code : /postal/code
latitude : /location/latitude
longitude : /location/longitude
#/location/latitude_longitude : /location/latitude_longitude
#/location/longitude_latitude : /location/longitude_latitude
      #{logInfo { format : "BODY : {}", args : ["@{}"] } }
    # add Unique ID, in case our message_id field from above is not present
        generateUUID {
    # convert the timestamp field to "yyyy-MM-dd'T'HH:mm:ss.SSSZ" format
       #  21/Nov/2014:22:08:27
        convertTimestamp {
            field : time 
            inputFormats : ["[dd/MMM/yyyy:HH:mm:ss", "EEE, d MMM yyyy HH:mm:ss Z", "yyyy-MM-dd'T'HH:mm:ss.SSS'Z'", "yyyy-MM-dd'T'HH:mm:ss", "yyyy-MM-dd"]
            inputTimezone : America/Los_Angeles
           outputFormat : "yyyy-MM-dd'T'HH:mm:ss.SSS'Z'"
            outputTimezone : UTC
    # Consume the output record of the previous command and pipe another
    # record downstream.
    # This command sanitizes record fields that are unknown to Solr schema.xml
    # by deleting them. Recall that Solr throws an exception on any attempt to
    # load a document that contains a field that isn't specified in schema.xml
        sanitizeUnknownSolrFields {
            # Location from which to fetch Solr schema
            solrLocator : ${SOLR_LOCATOR}
    # load the record into a SolrServer or MapReduce SolrOutputFormat.
        loadSolr {
            solrLocator : ${SOLR_LOCATOR}

Then it’s just a case of starting the target sink agent using Cloudera Manager, and the source agent on the RM webserver using the flume-ng command-line utility, and then (hopefully) watch the web activity log entries start to arrive as documents in the Solr index/collection – which, after a bit of fiddling around and correcting typos, it did:


What’s neat here is that instead of having to use either an ETL tool such as ODI to process and parse the log entries (as I did here, in an earlier blog post series on ODI on Hadoop), or use the Hive-to-DGraph data reload feature in BDD, I’ve instead just got a Flume sink running this morphlines process and my data is added in real-time to my Solr index, and as you’ll see in a moment, a Hue Search dashboard.

To get Hue to work with my Solr service and new index, you first have to add the Solr service URL details to the Hue configuration settings using Cloudera Manager, like this:


Then, you can select the index from the list presented by the Search application within Hue, and start creating your data discovery and faceted search dashboard.


with the end result, after a few minutes of setup, looking like this for me:


So how does Solr, Hue, Flume and Morphlines compare to Oracle Big Data Discovery as a potential search-and-discovery solution on Hadoop? What’s impressive is how little work, once I’d figured it out, it took to set this up including the real-time loading and indexing of data for the dashboard. Compared to a loading HDFS and Hive using ODI, and manually refreshing the BDD DGraph data store, it’s much more lightweight and pretty elegant. But, it’s clearly an IT / developer solution, and I spent a fair few late nights getting it all to work – getting the Solr schema.xml right was a tricky task, and the morphlines / Solr ingestion process was particularly hard to to debug and understand why it wasn’t working.

Oracle Big Data Discovery, by contrast, makes the data loading, transformation and enrichment process available to the business or data analyst, and provides much richer tools for cataloging and exploring the full universe of datasets on the Hadoop cluster. Morphlines compares well to the Groovy transformations provided by Big Data Discovery and Solr is extensible to add functionality such as sentiment analysis and text parsing, but again these are IT tasks and not something the average data analyst will want to do.

In summary then – Hue, Solr and the Morphlines transformation framework can be an excellent tool in the hands of IT professionals and can create surprisingly featureful and elegant solutions with just a bit of code and process configuration – but where Big Data Discovery comes into its own is putting significant parts of this capability in the hands of the business and the data analyst, and providing tools for data upload and wrangling, combining that data with other datasets, analyzing that whole dataset (or “data reservoir”) and then collaborating with others around the organization.

Categories: BI & Warehousing

Australian March Training Offer

Rittman Mead Consulting - Fri, 2015-02-27 21:31

Autumn is almost upon us here in Australia so why not hold off  going into hibernation and head into the classroom instead.

For March and April only, Rittmanmead courses in Australia* are being offered at significantly discounted prices.

Heading up this promotion is the popular TRN202 OBIEE 11g Bootcamp course which will be held in Melbourne, Australia* on March 16th-20th 2015.

This is not a cut down version of the regular course but the entire 5 day content. Details

To enrol for this specially priced course, visit the Rittmanmead website training page. Registration is only open between March 1st – March 9th 2015 so register quickly to secure a spot.

Further specially priced courses will be advertised in the coming weeks.

*This offer is only available for courses run in Australia.
Registration Period: 01/03/2015 12:00am – 09/03/2015 11:59:59pm
Further Terms and Conditions can be found during registration

Categories: BI & Warehousing

Introducing Oracle Big Data Discovery Part 3: Data Exploration and Visualization

Rittman Mead Consulting - Thu, 2015-02-26 17:08

In the first two posts in this series, we looked at what Oracle Big Data Discovery is and how you can use it to sample, cleanse and then catalog data in your Hadoop-based data reservoir. At the end of that second post we’d loaded some webserver log data into BDD, and then uploaded some additional reference data that we then joined to the log file dataset to provide descriptive attributes to add to the base log activity. Once you’ve loaded the datasets into BDD you can do some basic searching and graphing of your data directly from the “Explore” part o the interface, selecting and locating attribute values from the search bar and displaying individual attributes in the “Scratchpad” area.


With Big Data Discovery though you can go one step further and build complete applications to search and analyse your data, using the “Discover” part of the application. Using this feature you can add one or more charts to a dashboard page that go much further than the simple data visualisations you get on the Explore part of the application, based on the chart types and UI interactions that you first saw in Oracle Endeca Information Discovery Studio.


Components you can add include thematic maps, summary bars (like OBIEE’s performance tiles, but for multiple measures), various bar, line and bubble charts, all of which can then be faceted-searched using an OEID-like search component.


Each visualisation component is tied to a particular “view” that points to one or more underlying BDD datasets – samples of the full dataset held in the Hadoop cluster stored in the Endeca Server-based DGraph engine. For example, the thematic map above was created against the post comments dataset, with the theme colours defined using the number of comments metric and each country defined by a country name attribute derived from the calling host IP address.


Views are auto-generated by BDD when you import a dataset, or when you join two or more datasets together. You can also use the Endeca EQL language to define your own views using a SQL-type language, and then define which columns represent attributes, which ones are metrics (measures) and how those metrics are aggregated.


Like OEID before it, Big Data Discovery isn’t a substitute for a regular BI tool like OBIEE – beyond simple charts and visualizations its tricky to create more complex data selections, drill-paths in hierarchies, subtotals and so forth, and users will need to understand the concept of multiple views and datatypes, when to drop into EQL and so on – but for non-technical users working in an organization’s big data team it’s a great way to put a visual front-end onto the data in the data reservoir without having to understand tools like R Studio.

So that’s it for this three-part overview of Oracle Big Data Discovery and how it works with the Hadoop-based data reservoir. Keep an eye on the blog over the next few weeks as we get to grips with this new tool, and we’ll be covering it as part of the optional masterclass at the Brighton and Atlanta Rittman Mead BI Forum 2015 events this May.

Categories: BI & Warehousing