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• Limiting DOPs &ndash; Who rules over whom?

  - by jean-pierre.dijcks

  I've gotten a couple of questions from Dan Morgan and figured I start to answer them in this way. While Dan is running on a big system he is running with Database Resource Manager and he is trying to make sure the system doesn't go crazy (remember end user are never, ever crazy!) on very high DOPs. Q: How do I control statements with very high DOPs driven from user hints in queries? A: The best way to do this is to work with DBRM and impose limits on consumer groups. The Max DOP setting you can set in DBRM allows you to overwrite the hint. Now let's go into some more detail here. Assume my object (and for simplicity we assume there is a single object - and do remember that we always pick the highest DOP when in doubt and when conflicting DOPs are available in a query) has PARALLEL 64 as its setting. Assume that the query that selects something cool from that table lives in a consumer group with a max DOP of 32. Assume no goofy things (like running out of parallel_max_servers) are happening. A query selecting from this table will run at DOP 32 because DBRM caps the DOP. As of 11.2.0.1 we also use the DBRM cap to create the original plan (at compile time) and not just enforce the cap at runtime. Now, my user is smart and writes a query with a parallel hint requesting DOP 128. This query is still capped by DBRM and DBRM overrules the hint in the statement. The statement, despite the hint, runs at DOP 32. Note that in the hinted scenario we do compile the statement with DOP 128 (the optimizer obeys the hint). This is another reason to use table decoration rather than hints. Q: What happens if I set parallel_max_servers higher than processes (e.g. the max number of processes allowed to run on my machine)? A: Processes rules. It is important to understand that processes are fixed at startup time. If you increase parallel_max_servers above the number of processes in the processes parameter you should get a warning in the alert log stating it can not take effect. As a follow up, a hinted query requesting more parallel processes than either parallel_max_servers or processes will not be able to acquire the requested number. Parallel_max_processes will prevent this. And since parallel_max_servers should be lower than max processes you can never go over either...

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• Partition Wise Joins

  - by jean-pierre.dijcks

  Some say they are the holy grail of parallel computing and PWJ is the basis for a shared nothing system and the only join method that is available on a shared nothing system (yes this is oversimplified!). The magic in Oracle is of course that is one of many ways to join data. And yes, this is the old flexibility vs. simplicity discussion all over, so I won't go there... the point is that what you must do in a shared nothing system, you can do in Oracle with the same speed and methods. The Theory A partition wise join is a join between (for simplicity) two tables that are partitioned on the same column with the same partitioning scheme. In shared nothing this is effectively hard partitioning locating data on a specific node / storage combo. In Oracle is is logical partitioning. If you now join the two tables on that partitioned column you can break up the join in smaller joins exactly along the partitions in the data. Since they are partitioned (grouped) into the same buckets, all values required to do the join live in the equivalent bucket on either sides. No need to talk to anyone else, no need to redistribute data to anyone else... in short, the optimal join method for parallel processing of two large data sets. PWJ's in Oracle Since we do not hard partition the data across nodes in Oracle we use the Partitioning option to the database to create the buckets, then set the Degree of Parallelism (or run Auto DOP - see here) and get our PWJs. The main questions always asked are: How many partitions should I create? What should my DOP be? In a shared nothing system the answer is of course, as many partitions as there are nodes which will be your DOP. In Oracle we do want you to look at the workload and concurrency, and once you know that to understand the following rules of thumb. Within Oracle we have more ways of joining of data, so it is important to understand some of the PWJ ideas and what it means if you have an uneven distribution across processes. Assume we have a simple scenario where we partition the data on a hash key resulting in 4 hash partitions (H1 -H4). We have 2 parallel processes that have been tasked with reading these partitions (P1 - P2). The work is evenly divided assuming the partitions are the same size and we can scan this in time t1 as shown below. Now assume that we have changed the system and have a 5th partition but still have our 2 workers P1 and P2. The time it takes is actually 50% more assuming the 5th partition has the same size as the original H1 - H4 partitions. In other words to scan these 5 partitions, the time t2 it takes is not 1/5th more expensive, it is a lot more expensive and some other join plans may now start to look exciting to the optimizer. Just to post the disclaimer, it is not as simple as I state it here, but you get the idea on how much more expensive this plan may now look... Based on this little example there are a few rules of thumb to follow to get the partition wise joins. First, choose a DOP that is a factor of two (2). So always choose something like 2, 4, 8, 16, 32 and so on... Second, choose a number of partitions that is larger or equal to 2* DOP. Third, make sure the number of partitions is divisible through 2 without orphans. This is also known as an even number... Fourth, choose a stable partition count strategy, which is typically hash, which can be a sub partitioning strategy rather than the main strategy (range - hash is a popular one). Fifth, make sure you do this on the join key between the two large tables you want to join (and this should be the obvious one...). Translating this into an example: DOP = 8 (determined based on concurrency or by using Auto DOP with a cap due to concurrency) says that the number of partitions >= 16. Number of hash (sub) partitions = 32, which gives each process four partitions to work on. This number is somewhat arbitrary and depends on your data and system. In this case my main reasoning is that if you get more room on the box you can easily move the DOP for the query to 16 without repartitioning... and of course it makes for no leftovers on the table... And yes, we recommend up-to-date statistics. And before you start complaining, do read this post on a cool way to do stats in 11.

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• Big Data Accelerator

  - by Jean-Pierre Dijcks

  For everyone who does not regularly listen to earnings calls, Oracle's Q4 call was interesting (as it mostly is). One of the announcements in the call was the Big Data Accelerator from Oracle (Seeking Alpha link here - slightly tweaked for correctness shown below):  "The big data accelerator includes some of the standard open source software, HDFS, the file system and a number of other pieces, but also some Oracle components that we think can dramatically speed up the entire map-reduce process. And will be particularly attractive to Java programmers [...]. There are some interesting applications they do, ETL is one. Log processing is another. We're going to have a lot of those features, functions and pre-built applications in our big data accelerator."  Not much else we can say right now, more on this (and Big Data in general) at Openworld!

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• "Software sources" crashes since unbuntu updated from 12.04 to 12.10

  - by Jean-Sebastien

  First of all, sorry for my English, I is not my native language. I recently updated my PC from ubuntu 12.04 to 12.10. Now “Software sources” crashes when I try to open it directly from Unity or from Ubuntu Software Center ? Software sources. When I try to open “Update manager”, I get the following error message. Note that the internet connection WROKS! W:Failed to fetch http://ppa.launchpad.net/rye/ubuntuone-extras/ubuntu/dists/quantal/main/source/Sources 404 Not Found, ... ... ... E:Some index files failed to download. They have been ignored, or old ones used instead. Please, can somebody help me on this? JS

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• Partition Wise Joins II

  - by jean-pierre.dijcks

  One of the things that I did not talk about in the initial partition wise join post was the effect it has on resource allocation on the database server. When Oracle applies a different join method - e.g. not PWJ - what you will see in SQL Monitor (in Enterprise Manager) or in an Explain Plan is a set of producers and a set of consumers. The producers scan the tables in the the join. If there are two tables the producers first scan one table, then the other. The producers thus provide data to the consumers, and when the consumers have the data from both scans they do the join and give the data to the query coordinator. Now that behavior means that if you choose a degree of parallelism of 4 to run such query with, Oracle will allocate 8 parallel processes. Of these 8 processes 4 are producers and 4 are consumers. The consumers only actually do work once the producers are fully done with scanning both sides of the join. In the plan above you can see that the producers access table SALES [line 11] and then do a PX SEND [line 9]. That is the producer set of processes working. The consumers receive that data [line 8] and twiddle their thumbs while the producers go on and scan CUSTOMERS. The producers send that data to the consumer indicated by PX SEND [line 5]. After receiving that data [line 4] the consumers do the actual join [line 3] and give the data to the QC [line 2]. BTW, the myth that you see twice the number of processes due to the setting PARALLEL_THREADS_PER_CPU=2 is obviously not true. The above is why you will see 2 times the processes of the DOP. In a PWJ plan the consumers are not present. Instead of producing rows and giving those to different processes, a PWJ only uses a single set of processes. Each process reads its piece of the join across the two tables and performs the join. The plan here is notably different from the initial plan. First of all the hash join is done right on top of both table scans [line 8]. This query is a little more complex than the previous so there is a bit of noise above that bit of info, but for this post, lets ignore that (sort stuff). The important piece here is that the PWJ plan typically will be faster and from a PX process number / resources typically cheaper. You may want to look out for those plans and try to get those to appear a lot... CREDITS: credits for the plans and some of the info on the plans go to Maria, as she actually produced these plans and is the expert on plans in general... You can see her talk about explaining the explain plan and other optimizer stuff over here: ODTUG in Washington DC, June 27 - July 1 On the Optimizer blog At OpenWorld in San Francisco, September 19 - 23 Happy joining and hope to see you all at ODTUG and OOW...

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• Data Warehouse Best Practices

  - by jean-pierre.dijcks

  In our quest to share our endless wisdom (ahem…) one of the things we figured might be handy is recording some of the best practices for data warehousing. And so we did. And, we did some more… We now have recreated our websites on Oracle Technology Network and have a separate page for best practices, parallelism and other cool topics related to data warehousing. But the main topic of this post is the set of recorded best practices. Here is what is available (and it is a series that ties together but can be read independently), applicable for almost any database version: Partitioning 3NF schema design for a data warehouse Star schema design Data Loading Parallel Execution Optimizer and Stats management The best practices page has a lot of other useful information so have a look here.

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• Auto DOP and Concurrency

  - by jean-pierre.dijcks

  After spending some time in the cloud, I figured it is time to come down to earth and start discussing some of the new Auto DOP features some more. As Database Machines (the v2 machine runs Oracle Database 11.2) are effectively selling like hotcakes, it makes some sense to talk about the new parallel features in more detail. For basic understanding make sure you have read the initial post. The focus there is on Auto DOP and queuing, which is to some extend the focus here. But now I want to discuss the concurrency a little and explain some of the relevant parameters and their impact, specifically in a situation with concurrency on the system. The goal of Auto DOP The idea behind calculating the Automatic Degree of Parallelism is to find the highest possible DOP (ideal DOP) that still scales. In other words, if we were to increase the DOP even more  above a certain DOP we would see a tailing off of the performance curve and the resource cost / performance would become less optimal. Therefore the ideal DOP is the best resource/performance point for that statement. The goal of Queuing On a normal production system we should see statements running concurrently. On a Database Machine we typically see high concurrency rates, so we need to find a way to deal with both high DOP’s and high concurrency. Queuing is intended to make sure we Don’t throttle down a DOP because other statements are running on the system Stay within the physical limits of a system’s processing power Instead of making statements go at a lower DOP we queue them to make sure they will get all the resources they want to run efficiently without trashing the system. The theory – and hopefully – practice is that by giving a statement the optimal DOP the sum of all statements runs faster with queuing than without queuing. Increasing the Number of Potential Parallel Statements To determine how many statements we will consider running in parallel a single parameter should be looked at. That parameter is called PARALLEL_MIN_TIME_THRESHOLD. The default value is set to 10 seconds. So far there is nothing new here…, but do realize that anything serial (e.g. that stays under the threshold) goes straight into processing as is not considered in the rest of this post. Now, if you have a system where you have two groups of queries, serial short running and potentially parallel long running ones, you may want to worry only about the long running ones with this parallel statement threshold. As an example, lets assume the short running stuff runs on average between 1 and 15 seconds in serial (and the business is quite happy with that). The long running stuff is in the realm of 1 – 5 minutes. It might be a good choice to set the threshold to somewhere north of 30 seconds. That way the short running queries all run serial as they do today (if it ain’t broken, don’t fix it) and allows the long running ones to be evaluated for (higher degrees of) parallelism. This makes sense because the longer running ones are (at least in theory) more interesting to unleash a parallel processing model on and the benefits of running these in parallel are much more significant (again, that is mostly the case). Setting a Maximum DOP for a Statement Now that you know how to control how many of your statements are considered to run in parallel, lets talk about the specific degree of any given statement that will be evaluated. As the initial post describes this is controlled by PARALLEL_DEGREE_LIMIT. This parameter controls the degree on the entire cluster and by default it is CPU (meaning it equals Default DOP). For the sake of an example, let’s say our Default DOP is 32. Looking at our 5 minute queries from the previous paragraph, the limit to 32 means that none of the statements that are evaluated for Auto DOP ever runs at more than DOP of 32. Concurrently Running a High DOP A basic assumption about running high DOP statements at high concurrency is that you at some point in time (and this is true on any parallel processing platform!) will run into a resource limitation. And yes, you can then buy more hardware (e.g. expand the Database Machine in Oracle’s case), but that is not the point of this post… The goal is to find a balance between the highest possible DOP for each statement and the number of statements running concurrently, but with an emphasis on running each statement at that highest efficiency DOP. The PARALLEL_SERVER_TARGET parameter is the all important concurrency slider here. Setting this parameter to a higher number means more statements get to run at their maximum parallel degree before queuing kicks in.  PARALLEL_SERVER_TARGET is set per instance (so needs to be set to the same value on all 8 nodes in a full rack Database Machine). Just as a side note, this parameter is set in processes, not in DOP, which equates to 4* Default DOP (2 processes for a DOP, default value is 2 * Default DOP, hence a default of 4 * Default DOP). Let’s say we have PARALLEL_SERVER_TARGET set to 128. With our limit set to 32 (the default) we are able to run 4 statements concurrently at the highest DOP possible on this system before we start queuing. If these 4 statements are running, any next statement will be queued. To run a system at high concurrency the PARALLEL_SERVER_TARGET should be raised from its default to be much closer (start with 60% or so) to PARALLEL_MAX_SERVERS. By using both PARALLEL_SERVER_TARGET and PARALLEL_DEGREE_LIMIT you can control easily how many statements run concurrently at good DOPs without excessive queuing. Because each workload is a little different, it makes sense to plan ahead and look at these parameters and set these based on your requirements.

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• Oracle Database Machine and Exadata Storage Server

  - by jean-marc.gaudron(at)oracle.com

  Master Note for Oracle Database Machine and Exadata Storage Server (Doc ID 1187674.1)This Master Note is intended to provide an index and references to the most frequently used My Oracle Support Notes with respect to Oracle Exadata and Oracle Database Machine environments. This Master Note is subdivided into categories to allow for easy access and reference to notes that are applicable to your area of interest. This includes the following categories: • Database Machine and Exadata Storage Server Concepts and Overview• Database Machine and Exadata Storage Server Configuration and Administration• Database Machine and Exadata Storage Server Troubleshooting and Debugging• Database Machine and Exadata Storage Server Best Practices• Database Machine and Exadata Storage Server Patching• Database Machine and Exadata Storage Server Documentation and References• Database Machine and Exadata Storage Server Known Problems• ASM and RAC Documentation• Using My Oracle Support Effectively

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• My Take on Hadoop World 2011

  - by Jean-Pierre Dijcks

  I’m sure some of you have read pieces about Hadoop World and I did see some headlines which were somewhat, shall we say, interesting? I thought the keynote by Larry Feinsmith of JP Morgan Chase & Co was one of the highlights of the conference for me. The reason was very simple, he addressed some real use cases outside of internet and ad platforms. The following are my notes, since the keynote was recorded I presume you can go and look at Hadoopworld.com at some point… On the use cases that were mentioned: ETL – how can I do complex data transformation at scale Doing Basel III liquidity analysis Private banking – transaction filtering to feed [relational] data marts Common Data Platform – a place to keep data that is (or will be) valuable some day, to someone, somewhere 360 Degree view of customers – become pro-active and look at events across lines of business. For example make sure the mortgage folks know about direct deposits being stopped into an account and ensure the bank is pro-active to service the customer Treasury and Security – Global Payment Hub [I think this is really consolidation of data to cross reference activity across business and geographies] Data Mining Bypass data engineering [I interpret this as running a lot of a large data set rather than on samples] Fraud prevention – work on event triggers, say a number of failed log-ins to the website. When they occur grab web logs, firewall logs and rules and start to figure out who is trying to log in. Is this me, who forget his password, or is it someone in some other country trying to guess passwords Trade quality analysis – do a batch analysis or all trades done and run them through an analysis or comparison pipeline One of the key requests – if you can say it like that – was for vendors and entrepreneurs to make sure that new tools work with existing tools. JPMC has a large footprint of BI Tools and Big Data reporting and tools should work with those tools, rather than be separate. Security and Entitlement – how to protect data within a large cluster from unwanted snooping was another topic that came up. I thought his Elephant ears graph was interesting (couldn’t actually read the points on it, but the concept certainly made some sense) and it was interesting – when asked to show hands – how the audience did not (!) think that RDBMS and Hadoop technology would overlap completely within a few years. Another interesting session was the session from Disney discussing how Disney is building a DaaS (Data as a Service) platform and how Hadoop processing capabilities are mixed with Database technologies. I thought this one of the best sessions I have seen in a long time. It discussed real use case, where problems existed, how they were solved and how Disney planned some of it. The planning focused on three things/phases: Determine the Strategy – Design a platform and evangelize this within the organization Focus on the people – Hire key people, grow and train the staff (and do not overload what you have with new things on top of their day-to-day job), leverage a partner with experience Work on Execution of the strategy – Implement the platform Hadoop next to the other technologies and work toward the DaaS platform This kind of fitted with some of the Linked-In comments, best summarized in “Think Platform – Think Hadoop”. In other words [my interpretation], step back and engineer a platform (like DaaS in the Disney example), then layer the rest of the solutions on top of this platform. One general observation, I got the impression that we have knowledge gaps left and right. On the one hand are people looking for more information and details on the Hadoop tools and languages. On the other I got the impression that the capabilities of today’s relational databases are underestimated. Mostly in terms of data volumes and parallel processing capabilities or things like commodity hardware scale-out models. All in all I liked this conference, it was great to chat with a wide range of people on Oracle big data, on big data, on use cases and all sorts of other stuff. Just hope they get a set of bigger rooms next time… and yes, I hope I’m going to be back next year!

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• Live from ODTUG - Big Data and SQL session #2

  - by Jean-Pierre Dijcks

  Sitting in Dominic Delmolino's session at ODTUG (KScope 12). If the session count at conferences is any indication then we will see more and more people start to deploy MapReduce in the database. And yes, that would be with SQL and PL/SQL first and foremost. Both Dominic and our own Bryn Llewellyn are doing MapReduce in the database presentations.  Since I have seen both, I would advice people to first look through Dominic's session to get a good grasp on what mappers do and what reducers do, then dive into Bryn's for a bunch of PL/SQL example. The thing I like about Dominic's is the last slide (a recursive WITH statement) to do this in SQL... Now I am hoping that next year we will see tools vendors show off how they work with Hadoop and MapReduce (at least talking about the concepts!!).

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• Serial plans: Threshold / Parallel_degree_limit = 1

  - by jean-pierre.dijcks

  As a very short follow up on the previous post. So here is some more on getting a serial plan and why that happens Another reason - compared to the auto DOP is not on as we looked at in the earlier post - and often more prevalent to get a serial plan is if the plan simply does not take long enough to consider a parallel path. The resulting plan and note looks like this (note that this is a serial plan!): explain plan for select count(1) from sales; SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY()); PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------- Plan hash value: 672559287 -------------------------------------------------------------------------------------- | Id  | Operation            | Name  | Rows  | Cost (%CPU)| Time     | Pstart| Pstop | -------------------------------------------------------------------------------------- PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------- |   0 | SELECT STATEMENT     |       |     1 |     5   (0)| 00:00:01 |       |     | |   1 |  SORT AGGREGATE      |       |     1 |            |          |       |     | |   2 |   PARTITION RANGE ALL|       |   960 |     5   (0)| 00:00:01 |     1 |  16 | |   3 |    TABLE ACCESS FULL | SALES |   960 |     5   (0)| 00:00:01 |     1 |  16 | Note -----    - automatic DOP: Computed Degree of Parallelism is 1 because of parallel threshold 14 rows selected. The parallel threshold is referring to parallel_min_time_threshold and since I did not change the default (10s) the plan is not being considered for a parallel degree computation and is therefore staying with the serial execution. Now we go into the land of crazy: Assume I do want this DOP=1 to happen, I could set the parameter in the init.ora, but to highlight it in this case I changed it on the session: alter session set parallel_degree_limit = 1; The result I get is: ERROR: ORA-02097: parameter cannot be modified because specified value is invalid ORA-00096: invalid value 1 for parameter parallel_degree_limit, must be from among CPU IO AUTO INTEGER>=2 Which of course makes perfect sense...

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• Automatic Storage Management (ASM)

  - by jean-marc.gaudron(at)oracle.com

  Master Note for Automatic Storage Management (ASM) (Doc ID 1187723.1)This Master Note is intended to provide an index and references to the most frequently used My Oracle Support Notes with respect to Oracle Automatic Storage Management (ASM) environments. This Master Note is subdivided into categories to allow for easy access and reference to notes that are applicable to your area of interest. This includes the following categories: Automatic Storage Management (ASM) Concepts and Overview Automatic Storage Management (ASM) Installation Automatic Storage Management (ASM) Configuration Automatic Storage Management (ASM) Administration Automatic Storage Management (ASM) Migration and Upgrade Automatic Storage Management (ASM) Monitoring Automatic Storage Management (ASM) Troubleshooting and Debugging Automatic Storage Management (ASM) Best Practices Automatic Storage Management (ASM) Versions and Patches ASMLIB Database Machine, Exadata Storage Server and RAC Documentation Using My Oracle Support Effectively

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• Explaining Explain Plan Notes for Auto DOP

  - by jean-pierre.dijcks

  I've recently gotten some questions around "why do I not see a parallel plan" while Auto DOP is on (I think)...? It is probably worthwhile to quickly go over some of the ways to find out what Auto DOP was thinking. In general, there is no need to go tracing sessions and look under the hood. The thing to start with is to do an explain plan on your statement and to look at the parameter settings on the system. Parameter Settings to Look At First and foremost, make sure that parallel_degree_policy = AUTO. If you have that parameter set to LIMITED you will not have queuing and we will only do the auto magic if your objects are set to default parallel (so no degree specified). Next you want to look at the value of parallel_degree_limit. It is typically set to CPU, which in default settings equates to the Default DOP of the system. If you are testing Auto DOP itself and the impact it has on performance you may want to leave it at this CPU setting. If you are running concurrent statements you may want to give this some more thoughts. See here for more information. In general, do stick with either CPU or with a specific number. For now avoid the IO setting as I've seen some mixed results with that... In 11.2.0.2 you should also check that IO Calibrate has been run. Best to simply do a: SQL> select * from V$IO_CALIBRATION_STATUS; STATUS        CALIBRATION_TIME ------------- ---------------------------------------------------------------- READY         04-JAN-11 10.04.13.104 AM You should see that your IO Calibrate is READY and therefore Auto DOP is ready. In any case, if you did not run the IO Calibrate step you will get the following note in the explain plan: Note -----    - automatic DOP: skipped because of IO calibrate statistics are missing One more note on calibrate_io, if you do not have asynchronous IO enabled you will see:  ERROR at line 1: ORA-56708: Could not find any datafiles with asynchronous i/o capability ORA-06512: at "SYS.DBMS_RMIN", line 463 ORA-06512: at "SYS.DBMS_RESOURCE_MANAGER", line 1296 ORA-06512: at line 7 While this is changed in some fixes to the calibrate procedure, you should really consider switching asynchronous IO on for your data warehouse. Explain Plan Explanation To see the notes that are shown and explained here (and the above little snippet ) you can use a simple explain plan mechanism. There should  be no need to add +parallel etc. explain plan for <statement> SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY()); Auto DOP The note structure displaying why Auto DOP did not work (with the exception noted above on IO Calibrate) is like this: Automatic degree of parallelism is disabled: <reason> These are the reason codes: Parameter -  parallel_degree_policy = manual which will not allow Auto DOP to kick in  Hint - One of the following hints are used NOPARALLEL, PARALLEL(1), PARALLEL(MANUAL) Outline - A SQL outline of an older version (before 11.2) is used SQL property restriction - The statement type does not allow for parallel processing Rule-based mode - Instead of the Cost Based Optimizer the system is using the RBO Recursive SQL statement - The statement type does not allow for parallel processing pq disabled/pdml disabled/pddl disabled - For some reason (alter session?) parallelism is disabled Limited mode but no parallel objects referenced - your parallel_degree_policy = LIMITED and no objects in the statement are decorated with the default PARALLEL degree. In most cases all objects have a specific degree in which case Auto DOP will honor that degree. Parallel Degree Limited When Auto DOP does it works you may see the cap you imposed with parallel_degree_limit showing up in the note section of the explain plan: Note -----    - automatic DOP: Computed Degree of Parallelism is 16 because of degree limit This is an obvious indication that your are being capped for this statement. There is one quite interesting one that happens when you are being capped at DOP = 1. First of you get a serial plan and the note changes slightly in that it does not indicate it is being capped (we hope to update the note at some point in time to be more specific). It right now looks like this: Note -----    - automatic DOP: Computed Degree of Parallelism is 1 Dynamic Sampling With 11.2.0.2 you will start seeing another interesting change in parallel plans, and since we are talking about the note section here, I figured we throw this in for good measure. If we deem the parallel (!) statement complex enough, we will enact dynamic sampling on your query. This happens as long as you did not change the default for dynamic sampling on the system. The note looks like this: Note ----- - dynamic sampling used for this statement (level=5)

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• Big Data Videos

  - by Jean-Pierre Dijcks

  You can view them all on YouTube using the following links: Overview for the Boss: http://youtu.be/ikJyrmKdJWc Hadoop: http://youtu.be/acWtid-OOWM Acquiring Big Data: http://youtu.be/TfuhuA_uaho Organizing Big Data: http://youtu.be/IC6jVRO2Hq4 Analyzing Big Data: http://youtu.be/2yf_jrBhz5w These videos are a great place to start learning about big data, the value it can bring to your organisation and how Oracle can help you start working with big data today.

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• Big Data Appliance X4-2 Release Announcement

  - by Jean-Pierre Dijcks

  Today we are announcing the release of the 3rd generation Big Data Appliance. Read the Press Release here. Software Focus The focus for this 3rd generation of Big Data Appliance is: Comprehensive and Open - Big Data Appliance now includes all Cloudera Software, including Back-up and Disaster Recovery (BDR), Search, Impala, Navigator as well as the previously included components (like CDH, HBase and Cloudera Manager) and Oracle NoSQL Database (CE or EE). Lower TCO then DIY Hadoop Systems Simplified Operations while providing an open platform for the organization Comprehensive security including the new Audit Vault and Database Firewall software, Apache Sentry and Kerberos configured out-of-the-box Hardware Update A good place to start is to quickly review the hardware differences (no price changes!). On a per node basis the following is a comparison between old and new (X3-2) hardware: Big Data Appliance X3-2 Big Data Appliance X4-2 CPU 2 x 8-Core Intel® Xeon® E5-2660 (2.2 GHz) 2 x 8-Core Intel® Xeon® E5-2650 V2 (2.6 GHz) Memory 64GB 64GB Disk 12 x 3TB High Capacity SAS 12 x 4TB High Capacity SAS InfiniBand 40Gb/sec 40Gb/sec Ethernet 10Gb/sec 10Gb/sec For all the details on the environmentals and other useful information, review the data sheet for Big Data Appliance X4-2. The larger disks give BDA X4-2 33% more capacity over the previous generation while adding faster CPUs. Memory for BDA is expandable to 512 GB per node and can be done on a per-node basis, for example for NameNodes or for HBase region servers, or for NoSQL Database nodes. Software Details More details in terms of software and the current versions (note BDA follows a three monthly update cycle for Cloudera and other software): Big Data Appliance 2.2 Software Stack Big Data Appliance 2.3 Software Stack Linux Oracle Linux 5.8 with UEK 1 Oracle Linux 6.4 with UEK 2 JDK JDK 6 JDK 7 Cloudera CDH CDH 4.3 CDH 4.4 Cloudera Manager CM 4.6 CM 4.7 And like we said at the beginning it is important to understand that all other Cloudera components are now included in the price of Oracle Big Data Appliance. They are fully supported by Oracle and available for all BDA customers. For more information: Big Data Appliance Data Sheet Big Data Connectors Data Sheet Oracle NoSQL Database Data Sheet (CE | EE) Oracle Advanced Analytics Data Sheet

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• Interesting sessions/tips from RMOUG

  - by jean-pierre.dijcks

  One of the sessions I was at at last week's RMOUG was a session on Temp Tablespace Groups. I had a look because I had no experience with this and it seemed to help with parallel processing and the allocation/usage of temp. You can read the excellent write-up at Kellyn Pedersen's blog - who did the session and all the work - here. So for all of those who may be seeing lot's of waits like enq: TS - Contention when you are doing hash joins and sorts, do have a look at the above blog post. I also had the chance to listen in at Stewart Bryson's session on Restartability (he had 3 R-s) where he gave very useful tips about how to deal with your data warehouse loads. Questions like archive log mode - should I or should I not were well covered. Flashback archives, also nice to hear about. Very nice talk, very interesting. Unfortunately he hasn't blogged about it yes, so no pointers to that one. Got to see a couple of other interesting sessions, and as conferences go got to meet some interesting Oracle folks from the region. As usual RMOUG was useful and fun. Off to the drawing boards to design next year's session!

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• Can't print on my brand new Canon Pixma MP495

  - by Jean Ludovic Vandal

  I just purchased a Canon Pixma MP495. It was working perfect, except for the scanner. It won't install the driver for the scanner. Anyway, I was printing using the new printer. After a while, it started saying that the printing job was sent to the printer and then that the print job was completed. But, nothing came out of the printer. I uninstalled the driver and installed it over again. Nothing. Can somebody help me out. Can't work without a printer. Thanks in advance.

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• Conventional Parallel Inserts do Exist in Oracle 11

  - by jean-pierre.dijcks

  Had an interesting chat with Greg about said topic and searching showed the following link to discuss this topic in some detail (no reason for me to repeat this). insert /*+ noappend parallel(t1) */ into t1 select /*+ parallel(t2) */ * from t2 generates a load table conventional and does give you a parallel insert without doing a direct path insert. As this is missing from the official documentation it is probably something few people actually know existed, so kudos to Randolf Geist.

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• Upgrade failed, now impossible to restart

  - by Jean Claude Dispaux

  I have an Aspire One with Ubuntu, that I use only when traveling, i.e. seldom. Yesterday I tried to start it, it informed me that I had to install a new release of Ubuntu. The download went fine, then I left it for the night. In the morning I found error messages. I tried to restart, but nothing works any longer. The only backup I have is two USB keys made by the person who installed Ubuntu, that say Recovery Ubuntu 8 and Ubuntu 9.10 respectively. Right now I plugged the "8", selected F12 and instructed to boot from the USB key. It has been running for an hour, the screen still says ubuntu, the USB key flashes red. By the way, I have no precious data on this machine, I do not care about losing data. Please advise on what to do now. Thanks.

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• Dependancies not met while instaling digikam 2.9

  - by Jean-Mi

  I'm trying to install digikam 2.9 from ttp://ppa.launchpad.net/philip5/kubuntu-backports/ubuntu here is the message I got: Les paquets suivants ont des dépendances non-satisfaites : digikam: Depends: libkdecore5 (>= 4:4.7.0) mais la version 4:4.8.5-0ubuntu0.1 va être installée Depends: libkdeui5 (>= 4:4.7) mais la version 4:4.8.5-0ubuntu0.1 va être installée Depends: libkfile4 (>= 4:4.7) mais la version 4:4.8.5-0ubuntu0.1 va être installée Depends: libkhtml5 (>= 4:4.7) mais la version 4:4.8.5-0ubuntu0.1 va être installée Depends: libkio5 (>= 4:4.7.0) mais la version 4:4.8.5-0ubuntu0.1 va être installée Depends: libkipi9 (>= 4:4.8.80) mais la version 4:4.8.4d-precise~ppa1 va être installée Depends: libknotifyconfig4 (>= 4:4.7) mais la version 4:4.8.5-0ubuntu0.1 va être installée Depends: libkparts4 (>= 4:4.7) mais la version 4:4.8.5-0ubuntu0.1 va être installée Depends: libnepomuk4 (>= 4:4.7) mais la version 4:4.8.5-0ubuntu0.1 va être installée Depends: libphonon4 (>= 4:4.2.0) mais la version 4:4.7.0really4.6.0-0ubuntu1 va être installée Depends: libqt4-dbus (>= 4:4.5.3) mais la version 4:4.8.1-0ubuntu4.2 va être installée Depends: libqt4-qt3support (>= 4:4.5.3) mais la version 4:4.8.1-0ubuntu4.2 va être installée Depends: libqt4-sql (>= 4:4.5.3) mais la version 4:4.8.1-0ubuntu4.2 va être installée Depends: libqt4-xml (>= 4:4.5.3) mais la version 4:4.8.1-0ubuntu4.2 va être installée Depends: libqtcore4 (>= 4:4.8.0) mais la version 4:4.8.1-0ubuntu4.2 va être installée Depends: libqtgui4 (>= 4:4.8.0) mais la version 4:4.8.1-0ubuntu4.2 va être installée Depends: libsolid4 (>= 4:4.7) mais la version 4:4.8.5-0ubuntu0.1 va être installée Depends: digikam-data (= 4:2.9.0-precise~ppa1kde49) mais la version 4:2.9.0-precise~pp Can someone help ? JM

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• Winner of the 2012 Government Big Data Solutions Award

  - by Jean-Pierre Dijcks

  Hot off the press: The winner of the 2012 Government Big Data Solutions Aware is the National Cancer Institute!! Read all the details on CTOLabs.com. A short excerpt to wet your appetite: "... This solution, based on the Oracle Big Data Appliance with the Cloudera Distribution of Apache Hadoop (CDH), leverages capabilities available from the Big Data community today in pioneering ways that can serve a broad range of researchers. The promising approach of this solution is repeatable across many other Big Data challenges for bioinfomatics, making this approach worthy of its selection as the 2012 Government Big Data Solution Award." Read the entire post. Congrats to the entire team!!

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• undefined control sequence in a NOWEB document

  - by Jean Baldraque

  I'm writing a TeX-noweb document. I compile it with noweave -tex -filter "elide comment:*" texcode.nw > documentation.tex but when I try to compile the resulting file with xetex -halt-on-error documentation.tex I obtain the following error message ! Undefined control sequence. <argument> ...on}\endmoddef \nwstartdeflinemarkup \nwenddeflinemarkup It seems that \nwenddeflinemarkup is not recognized. If i delete from the document all the sequences \nwstartdeflinemarkup\nwenddeflinemarkup the document compile without exceptions. What can be the problem?

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• Video Whiteboard on Exadata Smart Flash Cache

  - by jean-pierre.dijcks

  Personally, I like this stuff. It places the unique features of Exadata in perspective with the overall business value. Enjoy the presentation!

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• Big data: An evening in the life of an actual buyer

  - by Jean-Pierre Dijcks

  Here I am, and this is an actual story of one of my evenings, trying to spend money with a company and ultimately failing. I just gave up and bought a service from another vendor, not the incumbent. Here is that story and how I think big data could actually fix this (and potentially prevent some of this from happening). In the end this story should illustrate how big data can benefit me (get me what I want without causing grief) and the company I am trying to buy something from. Note: Lots of details left out, I have no intention of being the annoyed blogger moaning about a specific company. What did I want to get? We watch TV, we have internet and we do have a land line. The land line is from a different vendor then the TV and the internet. I have decided that this makes no sense and I was going to get a bundle (no need to infer who this is, I just picked the generic bundle word as this is what I want to get) of all three services as this seems to save me money. I also want to not talk to people, I just want to click on a website when I feel like it and get it all sorted. I do think that is reality. I want to just do my shopping at 9.30pm while watching silly reruns on TV. Problem 1 - Bad links So, I'm an existing customer of the company I want to buy my bundle from. I go to the website, I click on offers. Turns out they are offers for new customers. After grumbling about how good they are, I click on offers for existing customers. Bummer, it goes to offers for new customers, so I click again on the link for offers for existing customers. No cigar... it just does not work. Big data solutions: 1) Do not show an existing customer the offers for new customers unless they are the same => This is only partially doable without login, but if a customer logs in the application should always know that this is an existing customer. But in general, imagine I do this from my home going through the internet service of this vendor to their domain... an instant filter should move me into the "existing customer route". 2) Flag dead or incorrect links => I've clicked the link for "existing customer offers" at least 3 times in under 5 seconds... Identifying patterns like this is easy in Hadoop and can very quickly make a list of potentially incorrect links. No need for realtime fixing, just the fact that this link can be pro-actively fixed across my entire web domain is a good thing. Preventative maintenance! Problem 2 - Purchase cannot be completed Apart from the fact that the browsing pattern to actually get to what I want is poorly designed, my purchase never gets past a specific point. In other words, I put something into my shopping cart and when I want to move on the application either crashes (with me going to an error page) or hangs or goes into something like chat. So I try again, and again and again. I think I tried this entire path (while being logged in!!) at least 10 times over the course of 20 minutes. I also clicked on the feedback button and, frustrated as I was, tried to explain this did not work... Big Data Solutions: 1) This web site does shopping cart analysis. I got an email next day stating I have things in my shopping cart, just click here to complete my purchase. After the above experience, this just added insult to my pain... 2) What should have happened, is a Hadoop job going over all logged in customers that are on the buy flow. It should flag anyone who is trying (multiple attempts from the same user to do the same thing), analyze the shopping card, the clicks to identify what the customers wants, his feedback provided (note: always own your own website feedback, never just farm this out!!) and in a short turn around time (30 minutes to 2 hours or so) email me with a link to complete my purchase. Not with a link to my shopping cart 12 hours later, but a link to actually achieve what I wanted... Why should this company go through the big data effort? I do believe this is relatively easy to do using our Oracle Event Processing and Big Data Appliance solutions combined. It is almost so simple (to my mind) that it makes no sense that this is not in place? But, now I am ranting... Why is this interesting? It is because of $$$$. After trying really hard, I mean I did this all in the evening, and again in the morning before going to work. I kept on failing, But I really wanted this to work... so an email that said, sorry, we noticed you tried to get a bundle (the log knows what I wanted, where I failed, so easy to generate), here is the link to click and complete your purchase. And here is 2 movies on us as an apology would have kept me as a customer, and got the additional $$$$ per month for the next couple of years. It would also lead to upsell on my phone package etc. Instead, I went to a completely different company, bought service from them. Lost money for company A, negative sentiment for company A and me telling this story at the water cooler so I'm influencing more people to think negatively about company A. All in all, a loss of easy money, a ding in sentiment and image where a relatively simple solution exists and can be in place on the software I describe routinely in this blog... For those who are coming to Openworld and maybe see value in solving the above, or are thinking of how to solve this, come visit us in Moscone North - Oracle Red Lounge or in the Engineered Systems Showcase.

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• Delete button in Ubuntu One Notes

  - by Jean MC13

  For the whishlist... The 'delete button' in Ubuntu One Notes is exactly in the same place as the 'save' button. So I saved a long note, but as my finger clicked two times on the save button, the second click was on the 'delete' one ! Lost without return possible. <:-(( This annoying feature could easily be changed : - put the delete button in another place - before deleting : ask for confirmation - offer a way to cancel ('undelete' function)

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