Flume-Cassandra Log Processor

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Gemini Mobile Technologies ("Gemini") released a Real-Time Log Processing System based on Flume and Cassandra ("Flume-Cassandra Log Processor") as open source. The Flume-Cassandra Log Processor enables massive volumes of production system logs to be collected and processed into graphical reports, in real-time. In addition, logs from multiple data centers can be simultaneously aggregated and analyzed in a single database.

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Flume-Cassandra Log Processor

  1. 1. Real-­‐Time,  High  Volume  Log  Processing   with  Flume  &  Cassandra   Gemini Mobile Technologies Gemini Mobile Technologies, Inc.
  2. 2. Overview  1.  Log  CollecAon  &  Storage  in  DB     Reliably  and  efficiently  moves  logs  from  mul6ple  applica6on  nodes  using  Flume     Store  raw  and  processed  log  data  in  Cassandra  DB  2.  Real  Time  and  On  Demand  reports     Via  Web  GUI  to  query  against  Cassandra.  E.g.  Transac6ons  Per  Second  (TPS)  vs  Time,  search  user’s  records.    3.  Summary  reports  by  Map-­‐Reduce     E.g.  Monthly  usage  by  category  (voice,  data,  mail,  etc)  for  groups  of  users.   Applica6on   Applica6on   …   Applica6on   Node   Node   Node   Log   Log   OA&M   Aggregator   Aggregator   Reports   (Web  GUI)   Cassandra   Cassandra     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  3. 3. Key  Benefits  1.  Real  Time,  Up  to  Date  Business  Intelligence     Dynamic,  near-­‐real-­‐6me  reports.      2.  Flexible  Analysis  on  Large  Historical  Data     Instant  Query  by  6me  range,  raw  log  fields,  processed  log  fields  (Data  is  stored  in  a   Database  for  fast  querying,  not  flat  Log  Files)     Create  On  Demand  Custom  Summary  Reports  by  Map-­‐Reduce  3.  MulAple  Data  Center  Support     Collect  and  Store  in  local  Data  Center,  Query  and  Analyze  across  Data  Centers  4.  Reliable,  Easy  OperaAon,  Maintenance,  and  Scalability     No  Data  Loss  if  network  and  PCs  fail     As  Data  Volume  (size  of  data  stored)  or  Velocity  (speed  of  new  data  arrival)  grow,  scale   to  100s  of  nodes,  TBs  of  data/day  by  adding  PCs  horizontally       Easy  to  setup,  configure,  and  monitor  for  a  large  network  5.  Easy  CustomizaAon     Open  source,  easy  to  change  for  custom  log  format,  custom  reports,  and  queries     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  4. 4. Log  CollecAon:  Flume       Open-­‐source  log  collec6on  system:  h^p://archive.cloudera.com/cdh/3/flume/UserGuide.html     Flume  Agent:  Reads  logs  at  configurable  interval  (e.g.,  100ms)  and  sends  to   Collector  nodes.     Flume  Collector:  Parses  logs  and  inserts  to  Cassandra.     Flume  Master:  Monitors  health  and  processing  state  of  Agents  and  Collectors.   App Node 1 Flume   agent1_src1   Log Aggregator Flume   Flume     collector_src1   agent1_src2   Cassandra   Flume   App Node 2 collector_src2   Flume   agent2_src1   Cassandra   Flume   Flume   Master   agent2_src2       Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.
  5. 5. Storage  Layer:  Cassandra       Cassandra,  an  open-­‐source  Apache  project,  is  the  storage  layer.      It’s  a  high   performance,  highly-­‐scalable  distributed  database.     Top-­‐level  Apache  Project    (h^p://cassandra.apache.org/)     Key  Features     Op6mized  for  Fast  Writes  of  Small  Data  (<100KB  each)     Peer-­‐to-­‐peer  nodes,  easy  to  add/remove  nodes  ad-­‐hoc     Scalable  for  clusters  from  2  to  100s  of  nodes.     Mul6ple  data  center  replica6on     Tunable  consistency  level,  per  request  level  
  6. 6. Log  CollecAon  System  Monitoring  (Flume  Master)     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  7. 7. Reports     Search  by  a^ribute:     Date  Range     Log  fields  (e.g.,  userID,  Message  Type)     List  view  (Rows  of  log  data)     Graph  view  (quan6ty  vs.  6me)     Data  downloadable  to  CSV  format.     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  8. 8. Reports  Example:  CDR  Search       Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  9. 9. Reports  Example:  CDR  Search  Results     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  10. 10. Reports  Example:  Graphs     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  11. 11. Sizing  Example  Node  Hardware:      Supermicro  (CPU:  2  quad-­‐core  Intel  E5420,  32GB  RAM,  16x1TB  SATA  HD)    ~$6,000.  Monitoring  Layer:     Nodes  required:  2    (1  Master  +  1  Standby  for  High  Availability)  Collector  Layer:       Nodes  required  =  MAX(2,  Node  Write  Throughput  (MB/S)  /  (log  bytes  per  transac6on  *  transac6ons  per  second  (TPS)))     Example  :  1  MB/sec  write  throughput  per  node,  1  KB/Transac6on,  1000  TPS  system  =  1MB/s  writes.     5 Example Collector 4 nodes required 3 2 2 3 4 5 MB/Sec (log bytes/tx * TPS)Storage  Layer:     Nodes  required  =  MAX(Replica6on  Factor,  Data  Per  Day  *  #  of  Days  to  keep  /  (Effec6ve  Node  Storage  /  Replica6on  Factor)  )     Example:  Data  Per  Day  =  100  GB,  #  of  Days  to  Keep  =  365,  Effec6ve  Node  Storage  =  8  TB,  Replica6on  Factor  =  2;  Then  Nodes  Required  =   100  *  365  /  (8000  /  2)  =  9.125  =  10  nodes   EffecAve  storage replicaAon   Data   #  days  of   #  of  nodes  for  365   (GB)  /  node   factor   (GB)  /  day   data  /  node   days   Example 8000   2   10   400   2   8000   3   10   266   3   8000   2   100   40   10     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  12. 12. Open  Source  Components     Flume  and  Cassandra  are  available  open-­‐source  components.    We  add  the  following   components:   1.  Custom  Flume-­‐Cassandra  Connector:  Reads  our  log  format  and  inserts  into  Cassandra   2.  Cassandra  data  design  including  schemas  and  configura6on   3.  Browser  UI  and  Queries  to  Cassandra     4.  Post-­‐processor  to  generate  custom  log  format  files     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  13. 13. Cassandra  Data  Model   Currently,  Flume  inserts  into  4  tables:   1.    Raw  Data  Table     Func6on:  Store  original  log  data  as  received.     Row  key:  YYYYMMDDHH,  One  for  each  hour.     Column:    Name:  Log  entry  UUID,  Value:  Log  data.       Column •  added for each log entry AAB32431352   ABC32433781   BCD32433901   in that hour •  sorted by Unique Log20110 01S,Market1,12345AA, 04RR,Market1,12345ZZ, 07S,Market1,12345BB, Entry ID (UUID)11107   20110111071200000,10.10.2.9,,10.1 20110111071200005,10.10.2.9,,10.10.2 20110111071200010,10.10.2.9,,10.10.2 0.2.10,09012345673,carrier.ne.jp,car .10,09023456890,carrier.ne.jp,carrier.n .10,09012345673,carrier.ne.jp,carrier.n Row rier.ne.jp,,,,,   e.jp,,,,,   e.jp,,,,,   •  added for each hour 2.    CDR  Entry  Table     Func6on:  Represent  each  log  field  as  a  column.    Useful  for  querying  and  indexing.     Row  key:    Log  entry  UUID.     Column:      Name:  log  data  field  name,  Value:  log  data  field  value.   typ market   id   Amestamp   moipaddr mApaddres msisdn   senderdoma recipientdom e   ess   s   in   ain   Row AAB32431 01S   Market1   1235AA   2011011107 10.10.2.9   10.10.2.10   0901234567 carrier.ne.jp   carrier.ne.jp   •  added for 352   1200000   3   each log entry ABC32433 04R Market1   1235ZZ   2011011107 10.10.2.9   10.10.2.10   0902345689 carrier.ne.jp   carrier.ne.jp   781   R   1200005   0   BCD32433 07S   Market1   1235BB   2011011107 10.10.2.9   10.10.2.10   0901234567 carrier.ne.jp   carrier.ne.jp   901   1200010   3     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  14. 14. Cassandra  Data  Model  3.    MSISDN  Timeline  Table     Func6on:  Organize  by  MSISDN  then  6mestamp.     Row  key:  MSISDN.       Column:        name:  6mestamp.    Value:  Log  entry  UUID  to  point  to  CDREntry.   Row 20110111071200000   20110111071200010   Column •  added for each •  added for each log MSISDN 09012345673   AAB32431352   BCD32433901   entry for that MSISDN •  sorted by Time stamp 20110111071200005   09023456890   ABC32433781  4.  HourlyTimeline  Table     Func6on:  Organize  by  6me  (hour)  then  by  6mestamp.     Row  key:  YYYYMMDDHH.       Column:      Name:  6mestamp  value.      Value:  UUID  to  point  to  CDREntry.   20110111071200000   20110111071200005   20110111071200010   Row Column •  added for each 2011011107   AAB32431352   ABC32433781   BCD32433901   •  added for each log hour entry in that hour 20110111081200001   20110111081200010   •  sorted by Time stamp 2011011108   BDB32431352   CDC32431352     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  15. 15. Next  Steps     Gemini  has  open  sourced  the  package  at   h^ps://github.com/geminitech/logprocessing     README,  sample  data,  package     To  try:     Download  and  install  Flume,  Cassandra,  and  Gemini’s  code     Try  with  sample  data     To  use  for  a  Produc6on  System     Get  sample  logs  from  the  actual  system,  Customize  Flume  Plug-­‐in  if  needed     Decide  what  reports  are  needed,  Customize  Cassandra  Table  format  and  UI  if  needed     Test  func6onality  and  performance  with  sampe  logs     Deploy:  Lab  system  first,  then  Produc6on  System     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  16. 16. Backup     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  17. 17. Database  Storage  AlternaAves  Cassandra  is  the  storage  system  used.  Comparisons  to  some  alterna6ves:      SQL.    Cant  insert  so  much  data  at  a  high  rate.    Cannot  scale  horizontally  easily.      Hadoop.    Cannot  query  flexibly  and  manipulate  data  since  it  is  not  in  a  database-­‐ like  system.      HBase  or  Hibari.    Provides  much  of  same  capability  as  Cassandra.    Cassandra  was   chosen  because     Mul6ple  data  center  support     Peer-­‐to-­‐peer  nodes,  easy  to  add/remove  nodes  ad-­‐hoc     Tunable  consistency     Not  currently  used,  but  would  be  useful  with  mul6ple  data  centers,  or  with  different  classes  of   data  (e.g.  Billing  Records  vs  Sta6s6cs  Records)     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  18. 18. FAQ  (Page  1)  From  a  view  of  using  stored  logs,  will  you  please  tell  us  your  know-­‐how,  e.g.,  Q.  What  approach  of  storage  (lumped  storage  of  mulAple  logs  or  concentrated   storage  of  similar  data)  would  be  effecAve  for  analysis  or  parse?  A.  It  depends  on  what  analysis/reports  we  would  like  to  do  later.    In  our  solu6on,  we   have  one  table  which  stores  all  logs,  and  we  have  3  other  tables  which  provide   indexing  based  on  similar  data  (e.g.  MSISDN,  Time  stamp,  Log  ID)  for  fast  queries.     The  exact  table/schema  may  be  customized  depending  on  the  actual  log  and   desired  reports.  Q.  When  logs  are  analyzed  /  parsed  later,  would  it  be  beger  to  use  a  stored   distributed  DB  on  an  as-­‐is  basis,  or  is  it  beger  to  convert  a  data  structure  in  a   certain  way  before  returning  data  into  distributed  DB  and  DWH?  A.  For  real  6me  queries  and  fast  report  genera6on,  it  is  useful  to  convert  the  data  into   certain  tables.    As  shown  in  our  example,  we  store  the  log  both  "as-­‐is"  and  in  table   format.    This  allows  the  most  flexible  usage.     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  19. 19. FAQ  (Page  2)  Q.  What  type  of  logs  (logs,  each  of  which  has  a  short  record,  e.g.,  syslog  and  APlog  of   a  system;  a  variety  of  logs  such  as  Lifelog;  or  large  logs  such  as  mulAmedia  data   or  web  pages)  would  best  fit  for  aggregaAon?  A.  All  of  these  fit  well.    In  our  Cassandra  based  system,  we  can  set  expiry  6me  for   each  log  entry.    Then  we  can  have  some  short  lived  records  be  automa6cally   deleted  azer  certain  periods,  while  long  live  logs  can  stay  for  a  long  6me.    So  in  our   system,  its  possible  to  have  different  types  of  logs  in  the  same  database.      Q.  Is  there  any  assumpAon  or  example  such  as  a  BI  tool  for  analysis  /  parse?    A.      No.    Once  the  data  is  in  the  database,  any  BI  tool  can  be  used  for  analysis.    The  BI   tool  would  need  to  be  integrated  to  Cassandra.    There  are  a  variety  of  ways  to  do   this,  and  amount  of  customiza6on  depends  on  the  BI  tool.     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  20. 20. FAQ  (Page  3)  Q.  How  is  older  data  deleted?  A.      Cassandra  has  a  6me-­‐to-­‐live  (TTL)  for  each  column  (in  seconds).    Azer  TTL  is   expired,  data  is  automa6cally  deleted  at  compac6on  6me.  Q.  How  do  we  detect/process  alarms  when  the  data  store  gets  full?    How  do  we   predict  when  data  store  is  full  so  we  can  expand?  B.  SNMP  (netsnmp)  can  be  used  to  monitor  server  disk  usage.    When  it  exceeds  a   certain  threshold,    an  SNMP  trap  is  generated.  S.  How  does  this  compare  with  a  Hadoop-­‐based  log  processing  system?  D.  By  adding  a  database  (i.e.,  Cassandra),  we  can  query  in  real  6me,  issue  complex   queries  and  do  other  database-­‐type  opera6ons.      U.  Do  we  use  Map/Reduce?  A.      A  map-­‐reduce  script  can  be  used  to  post-­‐process  the  log  data  and  to  generate   other  log  formats  or  analysis.    We  haven’t  tested  but  “should  work.”     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  21. 21. FAQ  (Page  4)  Q.    How  real  Ame  is  this  system  (exactly  how  much  delayed  it  would  be  under  the   best  circumstances)?    What  would  it  take  to  make  it  more  real  Ame?  A.  Total  latency  is  A  +  B  +  C  where  A  is  configurable  delay  to  read  log  file,  B  is  6me  to   move  data  from  Agent  node  to  Collector  node,  C  is  insert  into  Cassandra.      As  an   example  scenario  A=100ms,  B=50ms,  C=10ms,  total  is  160ms.  Q.    How  many  lines  of  code?    In  what  language?      A.  Flume  to  Cassandra  plugin  (~40  lines  of  Java),  UI  (~2000  lines  of  Java,  JSP),  Post-­‐ process  log  format  (~250  lines  of  Java).  R.  Areas  to  improve?  C.  1.  Generalize  UI  so  it  can  work  with  any  log  format.                2.    Extensive  load  and  large  system  tes6ng.                3.    Add  Pig  scripts  to  post-­‐process  log  data.     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  
  22. 22. Pig  for  Cassandra     Pig  (h^p://pig.apache.org/)  is  a  high-­‐level,  rela6onal  language  to  write  queries  that   are  then  translated  to  Map/Reduce  jobs.     The  Map/Reduce  jobs  are  supported  by  Cassandra.     Example  Pig  script  that  finds  the  top-­‐100  MSISDNs  that  have  the  highest  number  of   log  records  since  2011-­‐01-­‐01.  msisdn = LOAD cassandra://CDRLogs/MSISDNTimeline USING CassandraStorage();cdrs = FOREACH msisdn GENERATE flatten($1);cdrtime = FOREACH cdrs GENERATE $0;givenhourcdr = FILTER cdrtime BY $0 > 20110101000000msisdnByHour = GROUP givenhourcdr BY $0;msisdnByHourCount = FOREACH msisdnByHour GENERATE COUNT($1), group;orderedMsisdn = ORDER msisdnByHourCount BY $0;topUserAfterNewYear = LIMIT orderedMsisdn 100;dump topUserAfterNewYear;     Gemini  Mobile  Technologies,  Inc.    All  rights  reserved.  

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