Storage Characteristics Of Call Data Records In Column Store Databases


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Storage Characteristics Of Call Data Records In Column Store Databases

  2. 2. OVERVIEW •  This presentation gives a brief overview of the storage characteristics of Call Data Records in Column Store Databases •  It discusses •  What are Call Data Records (CDRs)? •  What is a Column Store Database? •  How efficient is a column store database for storing CDR and other (similar) machine generated data? •  It does not: •  Examine performance in any detail •  Compare column store to traditional row-basedJan 2012 © 2012 Data Management & Warehousing 2
  3. 3. WHAT ARE CALL DATA RECORDS (CDRs) ? •  Every time a telephone call is made data about that call is recorded. At its most basic this will include: •  The Calling Number (who made the call) •  The Called Number (who was called) •  The Start Time •  The End Time (or the duration) •  Various pieces of technical information (which network switch was used, mobile handset identifier, call direction, is it a free x800 type call etc.)Jan 2012 © 2012 Data Management & Warehousing 3
  4. 4. CDRs AT MULTIPLE LEVELS •  A CDR is created at the switch, each switch involved in a call creates its own CDRs, these are often called Network CDRs •  The Network CDRs are joined together into a record of an end to end call record through a process known as mediation. These are Unrated CDRs •  Finally the cost of the call is calculated and added to the Unrated CDRs to create Rated CDRsJan 2012 © 2012 Data Management & Warehousing 4
  5. 5. MORE CDR COMPLEXITY •  There are CDRs that are used for billing the subscriber, often called Retail CDRs •  There are also CDRs that are used to charge other operators when their call travels over your network (e.g. when you make a mobile call that finishes on land line from another operator) These are known as Interconnect CDRs or Wholesale CDRs •  There are also differences between Mobile and Fixed (Land) Line CDRs •  Finally each Switch Manufacturer (there are over 60) and each Mediation and/or Billing system (again at least 50) uses their own formatJan 2012 © 2012 Data Management & Warehousing 5
  6. 6. FOR THIS EXERCISE … •  We are using a European Telephone Company (Telco) Mobile Rated Interconnect CDRs •  We have 12,902 files, containing 435,242,447 CDRs over a 181 day period from 482,883 subscribers •  Each CDR has 80 fields and 583 characters in a fixed length record format file. In addition we have added an additional mandatory field to hold the source file name from which the record cameJan 2012 © 2012 Data Management & Warehousing 6
  7. 7. DATA DISTRIBUTION IN THE CDR RECORDS (1) •  The structure of the data in the record has a massive impact on its storage. There are a number of factors to look at: •  Data Types, Padding, Place Holders and Data Cardinality •  The example data we are using has 2 Datetime fields, 11 Char fields, 10 Numeric fields, 33 Integer fields and 25 Varchar fields which is a fairly typical mix for this type of machine generated data. In the source file these are all held as ASCII text.Jan 2012 © 2012 Data Management & Warehousing 7
  8. 8. DATA DISTRIBUTION IN THE CDR RECORDS (2) •  Fixed length records are padded. In our data set the ‘Calling Number’ fixed length field is defined as 24 characters long however the maximum field length in the actual data is only 11 characters long. This means that there always 13 space characters of padding afterwards •  24 of our 80 fields have no information in them at all, 43 of the fields are mandatory and are 100% populated. The remaining 13 fields have between 25% and 75% of the records filled.Jan 2012 © 2012 Data Management & Warehousing 8
  9. 9. DATA DISTRIBUTION IN THE CDR RECORDS (3) •  Finally the number of discreet values (cardinality) a field has affects storage. One flag field has possible values of 0 or 1 and therefore a (low) cardinality of 2, another field has a nearly unique value for every record and therefore a very high cardinality. Of the 57 fields with data there are 20 fields with high cardinality, 5 fields with medium cardinality and the remaining 32 fields have a low cardinalityJan 2012 © 2012 Data Management & Warehousing 9
  10. 10. WHAT IS A COLUMN STORE DATABASE? •  Traditionally databases are ‘row-based’ i.e. each field of data in a record is stored next to each other. Forename Surname Gender David Walker Male Helen Walker Female Sheila Jones Female •  Column store databases store the values in columns and then hold a mapping to form the record •  This is transparent to the user, who queries a table with SQL in exactly the same way as they would a row-based databaseJan 2012 © 2012 Data Management & Warehousing 10
  11. 11. COLUMN STORAGE First Name F Token Note: To the user this appears as a conventional row-based table that can be queried by standard Value SQL, it is only the underlying storage that is different David PPP Helen QQQ F Token S Token G Token Sheila RRR PPP YYY BBB Surname Value S Token QQQ YYY AAA Jones XXX RRR XXX AAA Walker YYY Gender Value G Token Female AAA Male BBBJan 2012 © 2012 Data Management & Warehousing 11
  12. 12. EFFICIENCIES OF COLUMN STORE DATABASES •  Column store databases offer significant storage optimisation opportunities especially where there is low or medium cardinality character strings (e.g. the telephone numbers and reference data) because long strings are not repeatedly stored •  In addition it is possible to compress the data column stores very efficiently •  It is possible, in some column store implementations, that the column storage holds additional metadata that can be used to speed up specific queries (e.g. the number of records associated with each value in a column) •  Reduced the data volume stored means reduced I/O when querying the database, this consequently gives query performance improvementsJan 2012 © 2012 Data Management & Warehousing 12
  13. 13. INEFFICIENCIES OF COLUMN STORE DATABASES •  In general manipulating individual rows for updates is expensive as it has to go to each of the columns and then update the mapping table •  Some column store databases have specific technologies to limit the impact of this by caching updates •  Consequently Column Store Databases are not efficient at OLTP type applications – however they are very efficient for DWH/BI/Archive type applications because the data is bulk loaded rather than individual row inserts, it is not frequently updated and used in large set based queriesJan 2012 © 2012 Data Management & Warehousing 13
  14. 14. HOW EFFICIENT IS IT TO STORE THIS DATA? •  What hardware was used and what would be needed for a production environment? •  How was the data loaded? •  What was the storage characteristics?Jan 2012 © 2012 Data Management & Warehousing 14
  15. 15. THE TEST ENVIRONMENT •  The test environment was designed to measure storage and not system performance •  This test was done using Sybase IQ 15.4 •  Sybase has had a column storage database called IQ since 1996 and is one of the most established of the 25 or so currently listed on Wikipedia •  The server was running CentOS 5.7 x64, a Redhat Linux derivative •  The hardware consisted of: •  Intel Xeon Quad-Core X3363 •  16GB Memory •  Adaptec 5405 RAID Controller with 2x 1TB 7200rpm Hard Disk (RAID1) •  The database was built on file systems rather than raw devices •  Total hardware cost was less than US$3000 •  Software licences were provided on evaluationJan 2012 © 2012 Data Management & Warehousing 15
  16. 16. A PRODUCTION ENVIRONMENT? •  To make this into a production environment would depend on the volume of data per month and the number of months data to be held and the type of CDR •  The biggest performance driver would be to have more disk spindles adding more (faster) drives or using solid state disks. This would improve performance as well as adding greater capacity •  e.g. 16 1Tb drives in RAID10 configuration would provide around 7.75Tb of space and store 75 Billion of these CDRs •  Using raw devices instead or file systems would also improve performance •  Other performance enhancements would include •  Moving from 1 to 2 or 4 Quad Core CPUs •  Adding another 16Gb of memoryJan 2012 © 2012 Data Management & Warehousing 16
  17. 17. LOADING THE DATA •  The data was loaded using PELT, an ETL tool written and used by Data Management & Warehousing •  The loading was done to production level quality •  Data is loaded into a load table (CDR_LOAD) which has a view (CDR_CONVERT) over it that applies data quality checks. The data is then selected from the view and inserted into the main table (CDRs) •  Each step is fully logged and auditedJan 2012 © 2012 Data Management & Warehousing 17
  18. 18. THE LOADING STEPS •  Copy a compressed (Unix •  Insert into the main CDR table Compress .Z) flat file (as from the DQ view provided) from the CDR_CONVERT over the incoming directory to the CDR_LOAD table workspace •  Record the size of the CDR •  Record the size of the .Z file table in kilobytes in bytes •  Truncate the CDR_LOAD table •  Uncompress the file •  Compress the source file with •  Record the size in bytes and ‘gzip -9’ (maximum the number of records in compression, longest the uncompressed file execution) •  Use iSQL ‘Load’ command •  Record the size of the .gz file in to insert the data into a bytes CDR_LOAD table •  Move the compressed .gz file •  Record the size of the to an archive directory CDR_LOAD table in kilobytesJan 2012 © 2012 Data Management & Warehousing 18
  19. 19. RESULTS •  12,902 files were loaded •  27.48 Gb of un-indexed with zero data quality storage in the database errors •  8.6:1 Compression Ratio •  435,583,388 CDRs •  41.47 Gb of fully indexed storage in the database •  236.50 Gb of raw files •  5.7:1 Compression Ratio •  20.03 Gb of storage in the •  Loading: 33 hours, 22 original .Z files minutes, 12 second •  11.8:1 Compression Ratio •  Indexing: 2 hours, 13 •  12.42 Gb of storage in the minutes, 9 seconds archive .gz files •  19.0:1 Compression RatioJan 2012 © 2012 Data Management & Warehousing 19
  20. 20. ADDING INDEXES •  By default the table has no indexes •  This is the same in most databases •  For this test every field was indexed •  This added 63 indexes that took up an additional 24Gb •  The total space used was still 5.7 times smaller than the space used by the raw files •  These indexes would significantly improve query performance •  However not all the indexes would be required in a production system as not all fields would be actively queried and this would reduce the space usedJan 2012 © 2012 Data Management & Warehousing 20
  21. 21. DISK SPACE USEDJan 2012 © 2012 Data Management & Warehousing 21
  22. 22. LOAD PERFORMANCE •  The average file had 33,760 records •  The ETL to load an average file took 11 seconds •  2 seconds to copy to the working directory and decompress •  3 seconds import into CDR_LOAD table •  3 seconds copy from CDR_CONVERT table to CDRS table •  2 seconds to gzip -9 and archive •  1 second logging and truncating tables •  None of the tables were indexed during the loadJan 2012 © 2012 Data Management & Warehousing 22
  23. 23. OBSERVATIONS (1) •  The results were approximately in the middle of our expectations and previous experience of other similar data sets where the raw data has been compressed between 5 and 10 times •  Even low end hardware gives acceptable load performance suitable for archive functionality but production scale hardware is needed for BI/DWHJan 2012 © 2012 Data Management & Warehousing 23
  24. 24. OBSERVATIONS (2) •  Some database tuning techniques are needed for truly massive data sets but can be designed in from the outset at low cost (e.g. which indexes/index types) •  It is worth considering putting each month (or some other similar date based partitioning) in separate tables for systems management purposes as it makes it easy to remove the data at the end of the archiving process •  Smaller reference tables added to the schema would have little/no compression but they are also very small and therefore not contribute greatly to the space usedJan 2012 © 2012 Data Management & Warehousing 24
  25. 25. ALTERNATIVE SCENARIOS •  This presentation uses information gathered on specific data used for a specific purpose by a client •  Companies may wonder how their data would work in both storage and performance terms •  Vendors may also wonder how their technologies compare in both storage and performance terms •  If you are interested in finding out please contact us with these or any other Data Warehousing/Business Intelligence enquiriesJan 2012 © 2012 Data Management & Warehousing 25
  26. 26. CONTACT US •  Data Management & Warehousing •  Website: •  Telephone: +44 (0) 118 321 5930 •  David Walker •  E-Mail: •  Telephone: +44 (0) 7990 594 372 •  Skype: datamgmt •  White Papers: 2012 © 2012 Data Management & Warehousing 26
  27. 27. ABOUT US Data Management & Warehousing is a UK based consultancy that has been delivering successful business intelligence and data warehousing solutions since 1995.Our consultants have worked with major corporations around the world including the US, Europe, Africa and the Middle East. We have worked in many industry sectors such as telcos, manufacturing, retail, financial and transport. We providegovernance and project management as well as expertise in the leading technologies.Jan 2012 © 2012 Data Management & Warehousing 27
  28. 28. THANK YOU© 2 0 1 2 - D ATA M A N A G E M E N T & WA R E H O U S I N G H T T P : / / W W W. D ATA M G M T. C O M