I am using Oracle as a DBMS and Tuxedo for application server.
Customer has the need to export data from Oracle to SAMFILE for interface purpose.
Unfortunately, the total number of records size is huge (over 10 million) so
I was wondering what is the best practice to extract big amounts of data to a file on the database server.
I am used to creating a cursor and fetching a record then writing to file.
Is there a better i.e. faster way to handle this? It is a recurring task.
I suggest you read Adrian Billington's article on tuning UTL_FILE. It covers all the bases. Find it here.
The important thing is buffering records, so reducing the number of file I/O calls. You will need to benchmark the different implementations, to see which works best in your situation.
Pay attention to his advice on query performance. Optimising file I/O is pointless if most of the time is spent on data acquisition.
Related
Can Vertica Database be used for OLTP data?
And if so what are the pros and cons on doing this?
Looking for a Vertica vs Oracle fight :)Since Oracle license is so costly, would Vertica do it job for a better price ?
thx all
Using Vertica as a transactional database is a bad idea. It's designed to be a data warehousing tool. Essentially, it reads and writes data in an optimized fashion. Lots of transactions? That's not what it is designed to do.
I would recommend that you look into VoltDB. Michael Stonebreaker who is the force behind Vertica founded that company as well. His basic philosophy is that Oracle, SQL Server, et al do not do well for high performance since they are designed to do everything. The future is having databases designed for specific tasks.
So he had some concepts for a data warehousing which became Vertica. For transactional databases, there's VoltDB. Not owned by HP, for the record.
For the record, I haven't used VoltDB. From what I know, it isn't as mature as Vertica is as a solution but it looks like it has a ton of promise.
HP Vertica is a column store database. The nature of the way that data is organised within a column store does not lend itself to rapid writes.
HP Vertica gets around this by having a WOS (Write Optimised Store) and ROS (Read Optimised Store which is file based).
Data is moved out of the WOS into the ROS fairly rapidly and the ROS itself has a "merge up" process that takes small ROS files and merges them together to form larger and therefore more easily scanned files.
If you tried to use Vertica for OLTP then what would happen would be that you'd get loads of ROS containers and possibly hit the default limit of 1024 ROS containers very quickly.
If you fronted the store with some form a queuing mechanism to pass through records in larger batches then this would result in fewer and larger ROS files. It would work but if you wanted to take your OLTP system to be reading very close to its writing activity it would not fit the use case.
The WOS/ROS mechanism is a neat work around for the fundamental performance penalty of writes in a column store DB but fundamentally Vertica is not an OLTP DB but rather a data mart technology that can ingest data in near real time
I think there are different ways to read into this question.
Can you use Vertica as an OLTP database?
First I'll define this question a bit. An OLTP database means the database itself is responsible for the transaction processing, not simply receiving somewhat normalized data.
My answer here is absolutely not, unless perhaps it is a single user database. There is practically no RI, no RI locking, table locks on DELETE/UPDATE, and you're likely to accumulate a delete vector in normal OLTP type usage.
You can work around some of these with some extensive middleware programming (distributed locks, heavy avoidance of DELETE/UPDATE, etc). But why? There are tons of options out there that are not Oracle, don't carry a huge price tag but give you everything you need for OLTP.
Can you use Vertica to ingest and query OLTP data?
Yes, definitely. Best to use Vertica towards its strengths, though. Queries in Vertica tend to have a fair amount of overhead, and you can plow through large amounts of data with ease, even normalized. I would not be using Vertica to primary run point queries, grabbing a few rows here and there. It isn't that you can't, but you can't with the same concurrency as other databases that are meant for this purpose.
TL;DR Use the right tool for the right job. I really love using Vertica, but just because I like to swing a hammer doesn't mean that every problem is a nail.
This question is a little old now but i'll share my experience.
I would not suggest vertica as OLTP unless you very carefully consider your workload.
As mentioned in other answers, Vertica has 2 types of storage. ROS is the Read Optimized Storage and WOS is the Write Optimized Storage. WOS is purely in memory so it performs better for inserts but queries slower as all the small updates need to be queried and unioned. Vertica can handle small loads in theory but in practice it didn't work out very well for us performance wise. Also there are drawbacks to WOS namely being that when the database fails WOS is not necessarily preserved when it rolls back to last good epoch. (ROS isn't either but in practice you lose a lot less from ROS).
ROS is a lot more reliable and gives better read performance but you will never be able to handle more than a certain number of queries without a careful design. Although vertica is horizontally scalable, in practice large tables get segmented across all nodes and therefore queries must run on all nodes. So adding more nodes doesn't mean handling more concurrent queries it just means less work per query. If your tables are small enough to be unsegmented then this might not be an issue for you.
Also worth noting is the OLTP typically implies lots concurrent transactions so you'll need to plan resource pools very carefully. By default vertica has a planned concurrency for the general resource pool of the minimum of number of cores per server or RAM/2GB. Essentially what this value does is determine the default memory allocation PER NODE for a segmented query. Therefore by default vertica will not let you run more queries than cores. You can adjust this value but once you hit a cap on memory theres no much you can do because the memory is allocated per node so adding more nodes doesn't even help. If you hit any errors at all for resource pool memory allocations that is the first config your should look at.
Additionally, Vertica is bad with deletes and updates (which resolve to a delete and an insert in the background) so if these are a regular part of your workload then Vertica is probably a bad choice. Personally we use MySQL for our dimension tables that require deletes/updates and then sync that data periodically into vertica to use for joins.
Personally I use Vertica as an OLTP-ish realtime-ish database. We batch our loads into 5 minute intervals which makes vertica happy in terms of how many/large the inserts are. These batches are inserted using COPY DIRECT so that they avoid WOS entirely (only do this if they are large batches as this forces ROS container creation and can be bad if you do it too often). As many projections as we can have are unsegmented to allow better scale out since this makes queries hit only 1 node and allocate memory on only 1 node. It has worked well for us so far and we load about 5 billion rows a day with realtime querying from our UI.
Up_one - considering the telecom use-case - are you doing CDR or something else?
To answer your original question yes Vertica may be a great fit but it depends on how you are loading the data, how you are doing updates, what your data size is and what your SLA is. I am really familiar in this space because I implemented Vertica at a telecom that I worked for at the time.
I've got a Winform app that will be used in the US and China. The SQL Server 2005 database is in the US, so the data access is going to be slower for the people in China. I'm deciding between using a DataReader and a Dataset for best performance. The data will immediately be loaded into business objects upon retrieval.
Question: Which performs better (DataReader/DataSet) pulling data from a database that's far away? I have read that the DataReader goes back to the database for each .Read(), so if the connection is slow to begin with, will the DataSet be a better choice here?
Thanks
The performance of datareader vs a dataset will barely be measurable compared to the database roundtrips if you're expecting long distance/slow links.
DataSets might use more memory though, which might be a concern if you're dealing with a lot of data.
depend of amount of data. You cannot store in memory (dataset) a too large amount.
Two ways for your problem at my mind :
- parallelisation (System.Thread)
- backgroundworkers
The first can improve performance in linq to sql cases. The second can help end users have a better experience (non bloqued UI).
I think it doesn't matter since the connection is the bottleneck.
Both use the same mechanism to fetch the data (ADO.NET/Datareader).
If you can do it you might compress the query result on the server en THEN send it to the client. That would improve performance.
Depends on what database it is. Bad situation if it is Access.
Depends on how much data is moved around, what is the usage style? will users from China just read or do read/write on common data? do they need to see all data?
The idea is, separate the data (if it helps the scenario) and merge it back.
It doesn't matter which you choose since the DataSet uses the DataReader to fill. Try to avoid calling the db whereever possible, by caching results or by getting extra data. A few calls that get extra data will probably outperform alot of small pecks at the tables. Maybe a BackgroundWorker could preload some data that you know you will be using.
Just for other readers: the DataReader is MUCH more performant. Obviously, these users have not tried using both and actually tested the difference. Load 1,000 records with a DataReader and 1,000 with a DataSet. Then try limiting the records for the DataSet to 10 records (using the adapter's Fill method so that the 1,000 are loaded, but only 10 are populated/filled into the DataSet).
I really don't know why DataSets are so bad performance-wise during the fill operation, but the difference is huge. Its much faster to create your own collection and fill them with a DataReader than use the very bloated and slow DataSet.
I am going to start on with a new project. I need to deal with hundred gigs of data in a .NET application. It is very early stage now to give much detail about this project. Some overview is follows:
Lots of writes and Lots of reads on same tables, very realtime
Scaling is very important as the client insists expansion of database servers very frequently, thus, the application servers as well
Foreseeing, lots and lots of usage in terms of aggregate queries could be implemented
Each row of data may contains lots of attributes to deal with
I am suggesting/having following as a solution:
Use distributed hash table sort of persistence (not S3 but an inhouse one)
Use Hadoop/Hive likes (any replacement in .NET?) for any analytical process across the nodes
Impelement GUI in ASP.NET/Silverlight (with lots of ajaxification,wherever required)
What do you guys think? Am i making any sense here?
Are your goals performance, maintainability, improving the odds of success, being cutting edge?
Don't give up on relational databases too early. With a $100 external harddrive and sample data generator (RedGate's is good), you can simulate that kind of workload quite easily.
Simulating that workload on a non-relational and cloud database and you might be writing your own tooling.
"Foreseeing, lots and lots of usage in terms of aggregate queries could be implemented"
This is the hallmark of a data warehouse.
Here's the trick with DW processing.
Data is FLAT. Facts and Dimensions. Minimal structure, since it's mostly loaded and not updated.
To do aggregation, every query must be a simple SELECT SUM() or COUNT() FROM fact JOIN dimension GROUP BY dimension attribute. If you do this properly so that every query has this form, performance can be very, very good.
Data can be stored in flat files until you want to aggregate. You then load the data people actually intend to use and create a "datamart" from the master set of data.
Nothing is faster than simple flat files. You don't need any complexity to handle terabytes of flat files that are (as needed) loaded into RDBMS datamarts for aggregation and reporting.
Simple bulk loads of simple dimension and fact tables can be VERY fast using the RDBMS's tools.
You can trivially pre-assign all PK's and FK's using ultra-high-speed flat file processing. This makes the bulk loads all the simpler.
Get Ralph Kimball's Data Warehouse Toolkit books.
Modern databases work very well with gigabytes. It's when you get into terabytes and petabytes that RDBMSes tend to break down. If you are foreseeing that kind of load, something like HBase or Cassandra may be what the doctor ordered. If not, spend some quality time tuning your database, inserting caching layers (memached), etc.
"lots of reads and writes on the same tables, very realtime" - Is integrity important? Are some of those writes transactional? If so, stick with RDBMS.
Scaling can be tricky, but it doesn't mean you have to go with cloud computing stuff. Replication in DBMS will usually do the trick, along with web application clusters, load balancers, etc.
Give the RDBMS the responsibility to keep the integrity. And treat this project as if it were a data warehouse.
Keep everything clean, you dont need to go using a lot of third parties tools: use the RDBMS tools instead.
I mean, use all tools that the RDBMS has, and write an GUI that extract all data from the Db using well written stored procedures of a well designed physical data model (index, partitions, etc).
Teradata can handle a lot of data and is scalable.
Calling all database guys...
The situation is this:
I have a DB2 database that is being written to and read from. I need to do some performance testing on programmatically executed read/writes.
I know how to write a program to read/write to this database, but I am not sure as to what factors I should consider in my performance test.
Do I need to worry about the difference between one session reading/writing vs multiple sessions?
What is the best way to interact with DB2 itself to get the amount of time these executions take?
The process I am testing is basically like a continuous batch proccess, constantly taking messages and persisting them. There will probably only be one or two sessions max on the DB at any given time.
Is time it takes to read/write really the best metric?
I am sure there are plenty of tools for this sort of testing. Any advice is appreciated.
Further info:
One thing I am considering is to try is to run X number of reads/writes with my database API (homebrew) and try to "time" how long it takes. Unfortuneately DB2 will buffer these messages. Is there any way to get DB2 to do a callback when it is done with a read/write? Or some way to externally measure the time these operations take? (tool, etc)
What is the goal for your performance testing?. Is it to test the performance for concurrent users or is it to test the load for batch process. Based on this there are tools available to test this. You may want to look jmeter from Apache.
In that case, you may want to trigger couple of concurrent processes to simaltaneously CRUD the data and monitor the activity using performance expert or something similar to that. While you do that you may want to use larger output so that you would be able to find any bottlenecks with larger sets of data. search for performance tuning in IBM redbooks site and you will find some case studies for this.
One huge factor in DB2 performance is how Buffer Pools are configured. e.g. http://www.ibm.com/developerworks/data/library/techarticle/0212wieser/0212wieser.html
I have a large amount of data I need to store, and be able to generate reports on - each one representing an event on a website (we're talking over 50 per second, so clearly older data will need to be aggregated).
I'm evaluating approaches to implementing this, obviously it needs to be reliable, and should be as easy to scale as possible. It should also be possible to generate reports from the data in a flexible and efficient way.
I'm hoping that some SOers have experience of such software and can make a recommendation, and/or point out the pitfalls.
Ideally I'd like to deploy this on EC2.
Wow. You are opening up a huge topic.
A few things right off the top of my head...
think carefully about your schema for inserts in the transactional part and reads in the reporting part, you may be best off keeping them separate if you have really large data volumes
look carefully at the latency that you can tolerate between real-time reporting on your transactions and aggregated reporting on your historical data. Maybe you should have a process which runs periodically and aggregates your transactions.
look carefully at any requirement which sees you reporting across your transactional and aggregated data, either in the same report or as a drill-down from one to the other
prototype with some meaningful queries and some realistic data volumes
get yourself a real production quality, enterprise ready database, i.e. Oracle / MSSQL
think about using someone else's code/product for the reporting e.g. Crystal/BO / Cognos
as I say, huge topic. As I think of more I'll continue adding to my list.
HTH and good luck
#Simon made a lot of excellent points, I'll just add a few and re-iterate/emphasize some others:
Use the right datatype for the Timestamps - make sure the DBMS has the appropriate precision.
Consider queueing for the capture of events, allowing for multiple threads/processes to handle the actual storage of the events.
Separate the schemas for your transactional and data warehouse
Seriously consider a periodic ETL from transactional db to the data warehouse.
Remember that you probably won't have 50 transactions/second 24x7x365 - peak transactions vs. average transactions
Investigate partitioning tables in the DBMS. Oracle and MSSQL will both partition on a value (like date/time).
Have an archiving/data retention policy from the outset. Too many projects just start recording data with no plans in place to remove/archive it.
Im suprised none of the answers here cover Hadoop and HDFS - I would suggest that is because SO is a programmers qa and your question is in fact a data science question.
If youre dealing with a large number of queries and large processing time, you would use HDFS (a distributed storage format on EC) to store your data and run batch queries (I.e. analytics) on commodity hardware.
You would then provision as many EC2 instances as needed (hundreds or thousands depending on how big your data crunching requirements are) and run map reduce queires against.your data to produce reports.
Wow.. This is a huge topic.
Let me begin with databases. First get something good if you are going to have crazy amounts to data. I like Oracle and Teradata.
Second, there is a definitive difference between recording transactional data and reporting/analytics. Put your transactional data in one area and then roll it up on a regular schedule into a reporting area (schema).
I believe you can approach this two ways
Throw money at the problem: Buy best in class software (databases, reporting software) and hire a few slick tech people to help
Take the homegrown approach: Build only what you need right now and grow the whole thing organically. Start with a simple database and build a web reporting framework. There are a lot of descent open-source tools and inexpensive agencies that do this work.
As far as the EC2 approach.. I'm not sure how this would fit into a data storage strategy. The processing is limited which is where EC2 is strong. Your primary goal is effecient storage and retreival.