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Solr: Past, Present & Future
Yonik Seeley
LucidWorks
Origins	
  of	
  Solr	
  
•  CNET	
  driven	
  to	
  find	
  alterna6ves	
  to	
  discon6nued	
  
commercial	
  enterprise	
  search	
  product	
  
•  Plan	
  A:	
  ATOMICS	
  (Apache	
  TO	
  MySQL	
  In	
  CNET	
  
Search)	
  	
  	
  
– Standalone	
  server	
  speaking	
  XML	
  over	
  HTTP	
  
– Meet	
  majority	
  of	
  “search”	
  needs	
  
–  hLp://conferences.oreillynet.com/cs/mysqluc2005/view/e_sess/7066	
  
•  Plan	
  B:	
  “Something	
  based	
  on	
  Lucene”	
  
– Started	
  Summer	
  2004	
  
– First	
  prototype	
  called	
  “Fusion”,	
  later	
  renamed	
  SOLAR	
  
(Search	
  On	
  Lucene	
  And	
  Resin)	
  
Origins	
  of	
  the	
  first	
  Solr	
  admin	
  UI	
  
New	
  admin	
  UI	
  
Timeline	
  
(up	
  to	
  1.4)	
  
Ini6al	
  
prototype	
  
CNET	
  
produc6on	
  
CNET	
  
contributes	
  
Solr	
  to	
  ASF	
  
Solr	
  
graduates	
  
from	
  
Incubator	
  
Simple	
  
face6ng	
  
replica6on	
  
highligh6ng,	
  
dismax	
  
Spellchecking,	
  
CSV,	
  Luke	
  
MLT,	
  Update	
  
Request	
  
Processors	
  
QParsers	
   Search	
  
Components	
  
Mul6-­‐core	
  
Distributed	
  
Search	
  
Data	
  Import	
  
Handler	
  
JMX	
  
1.3	
  
1.4	
  
Sta6s6cs	
  
Component	
  
Java	
  
Replica6on	
  
Terms	
  and	
  
TermVector	
  
Components	
  
Mul6-­‐select	
  
face6ng	
  
Dynamic	
  
Clustering	
  
1.1	
  
1.0	
  
1.2	
  
4.0	
  
3.1	
  
Solr	
  4	
  
•  Solr	
  Cloud	
  
– Distributed	
  Indexing	
  
– No	
  single	
  points	
  of	
  failure	
  
– Near	
  Real	
  Time	
  friendly	
  (push	
  replica6on)	
  
•  NoSQL	
  feature	
  set	
  
– Update	
  Durability	
  
– Real-­‐6me	
  get	
  
– Atomic	
  Updates	
  
– Op6mis6c	
  Concurrency	
  
•  Pseudo-­‐join,	
  Pivot	
  Face6ng,	
  Pseudo-­‐fields,	
  etc	
  
What	
  search	
  solu6on/version	
  are	
  you	
  
currently	
  using?	
  
Recent	
  Enhancements	
  
Document	
  Rou6ng	
  
80000000-­‐bfffffff	
  
00000000-­‐3fffffff	
  
40000000-­‐7fffffff	
  
c0000000-­‐ffffffff	
  
shard1	
  shard4	
  
shard3	
   shard2	
  
id	
  =	
  BigCo!doc5	
  
1f27	
   3c71	
  
(MurmurHash3)	
  
q=my_query	
  
shard.keys=BigCo!	
  
1f27	
   0000	
   1f27	
   ffff	
  to	
  
(hash)	
  
shard1	
  
numShards=4	
  
router=compositeId	
  
Seamless	
  Online	
  Shard	
  Splijng	
  
Shard2_0	
  
Shard1	
  
replica	
  
leader	
  
Shard2	
  
replica	
  
leader	
  
Shard3	
  
replica	
  
leader	
  
Shard2_1	
  
1.  New	
  sub-­‐shards	
  created	
  in	
  “construc6on”	
  state	
  
2.  Leader	
  starts	
  forwarding	
  applicable	
  updates,	
  which	
  
are	
  buffered	
  by	
  the	
  sub-­‐shards	
  
3.  Leader	
  index	
  is	
  split	
  and	
  installed	
  on	
  the	
  sub-­‐shards	
  
4.  Sub-­‐shards	
  apply	
  buffered	
  updates	
  then	
  become	
  
“ac6ve”	
  leaders	
  and	
  old	
  shard	
  becomes	
  “inac6ve”	
  
update	
  
Cloud	
  Enhancements	
  
•  Request	
  forwarding	
  
– In	
  a	
  mul6-­‐collec6on	
  cluster,	
  any	
  node	
  can	
  handle/
forward	
  requests	
  for	
  any	
  collec6on	
  
•  Collec6on	
  Aliases	
  
http://localhost:8983/solr/admin/collections	
  
?action=CREATEALIAS	
  
&name=northeast	
  
&collections=NY,NJ,PA,CT,ME,MA,NH,RI,VT	
  
•  Coming	
  Soon:	
  Shard	
  Aliases	
  
	
  
Schema	
  REST	
  API	
  
•  Restlet	
  is	
  now	
  integrated	
  with	
  Solr	
  
•  Get	
  a	
  specific	
  field	
  
curl	
  http://localhost:8983/solr/schema/fields/price	
  
{"field":{	
  
	
  	
  	
  	
  "name":"price",	
  
	
  	
  	
  	
  "type":"float",	
  
	
  	
  	
  	
  "indexed":true,	
  
	
  	
  	
  	
  "stored":true	
  }}	
  
•  Get	
  all	
  fields	
  
curl	
  http://localhost:8983/solr/schema/fields	
  
•  Get	
  En6re	
  Schema!	
  
curl	
  http://localhost:8983/solr/schema	
  
Dynamic	
  Schema	
  
•  Add	
  a	
  new	
  field	
  (Solr	
  4.4)	
  
curl	
  -­‐XPUT	
  http://localhost:8983/solr/schema/fields/strength	
  -­‐d	
  ‘
{"type":”float",	
  "indexed":"true”}	
  	
  	
  
‘	
  
•  Works	
  in	
  distributed	
  (cloud)	
  mode	
  too!	
  
•  Future:	
  More	
  schemaless	
  
– Reality:	
  there	
  is	
  no	
  such	
  thing	
  for	
  Lucene	
  based	
  systems	
  
– Type	
  guessing	
  for	
  fields	
  we	
  haven’t	
  seen	
  before	
  
	
  
Future	
  
•  Greater	
  scalability	
  
•  More	
  “NoSQL”	
  
– More	
  ways	
  to	
  update	
  &	
  manipulate	
  documents	
  
•  Analy6cs	
  
– More	
  powerful	
  face6ng,	
  func6ons,	
  sta6s6cs	
  
•  Improved	
  Rela6onal	
  queries	
  
•  More	
  dynamic	
  (sejngs	
  &	
  configura6on)	
  
•  Con6nued	
  focus	
  on	
  ease	
  of	
  use	
  
Thank	
  You!	
  

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Solr Past Present Future Search Engine

  • 1. Solr: Past, Present & Future Yonik Seeley LucidWorks
  • 2. Origins  of  Solr   •  CNET  driven  to  find  alterna6ves  to  discon6nued   commercial  enterprise  search  product   •  Plan  A:  ATOMICS  (Apache  TO  MySQL  In  CNET   Search)       – Standalone  server  speaking  XML  over  HTTP   – Meet  majority  of  “search”  needs   –  hLp://conferences.oreillynet.com/cs/mysqluc2005/view/e_sess/7066   •  Plan  B:  “Something  based  on  Lucene”   – Started  Summer  2004   – First  prototype  called  “Fusion”,  later  renamed  SOLAR   (Search  On  Lucene  And  Resin)  
  • 3. Origins  of  the  first  Solr  admin  UI  
  • 5. Timeline   (up  to  1.4)   Ini6al   prototype   CNET   produc6on   CNET   contributes   Solr  to  ASF   Solr   graduates   from   Incubator   Simple   face6ng   replica6on   highligh6ng,   dismax   Spellchecking,   CSV,  Luke   MLT,  Update   Request   Processors   QParsers   Search   Components   Mul6-­‐core   Distributed   Search   Data  Import   Handler   JMX   1.3   1.4   Sta6s6cs   Component   Java   Replica6on   Terms  and   TermVector   Components   Mul6-­‐select   face6ng   Dynamic   Clustering   1.1   1.0   1.2   4.0   3.1  
  • 6. Solr  4   •  Solr  Cloud   – Distributed  Indexing   – No  single  points  of  failure   – Near  Real  Time  friendly  (push  replica6on)   •  NoSQL  feature  set   – Update  Durability   – Real-­‐6me  get   – Atomic  Updates   – Op6mis6c  Concurrency   •  Pseudo-­‐join,  Pivot  Face6ng,  Pseudo-­‐fields,  etc  
  • 7. What  search  solu6on/version  are  you   currently  using?  
  • 9. Document  Rou6ng   80000000-­‐bfffffff   00000000-­‐3fffffff   40000000-­‐7fffffff   c0000000-­‐ffffffff   shard1  shard4   shard3   shard2   id  =  BigCo!doc5   1f27   3c71   (MurmurHash3)   q=my_query   shard.keys=BigCo!   1f27   0000   1f27   ffff  to   (hash)   shard1   numShards=4   router=compositeId  
  • 10. Seamless  Online  Shard  Splijng   Shard2_0   Shard1   replica   leader   Shard2   replica   leader   Shard3   replica   leader   Shard2_1   1.  New  sub-­‐shards  created  in  “construc6on”  state   2.  Leader  starts  forwarding  applicable  updates,  which   are  buffered  by  the  sub-­‐shards   3.  Leader  index  is  split  and  installed  on  the  sub-­‐shards   4.  Sub-­‐shards  apply  buffered  updates  then  become   “ac6ve”  leaders  and  old  shard  becomes  “inac6ve”   update  
  • 11. Cloud  Enhancements   •  Request  forwarding   – In  a  mul6-­‐collec6on  cluster,  any  node  can  handle/ forward  requests  for  any  collec6on   •  Collec6on  Aliases   http://localhost:8983/solr/admin/collections   ?action=CREATEALIAS   &name=northeast   &collections=NY,NJ,PA,CT,ME,MA,NH,RI,VT   •  Coming  Soon:  Shard  Aliases    
  • 12. Schema  REST  API   •  Restlet  is  now  integrated  with  Solr   •  Get  a  specific  field   curl  http://localhost:8983/solr/schema/fields/price   {"field":{          "name":"price",          "type":"float",          "indexed":true,          "stored":true  }}   •  Get  all  fields   curl  http://localhost:8983/solr/schema/fields   •  Get  En6re  Schema!   curl  http://localhost:8983/solr/schema  
  • 13. Dynamic  Schema   •  Add  a  new  field  (Solr  4.4)   curl  -­‐XPUT  http://localhost:8983/solr/schema/fields/strength  -­‐d  ‘ {"type":”float",  "indexed":"true”}       ‘   •  Works  in  distributed  (cloud)  mode  too!   •  Future:  More  schemaless   – Reality:  there  is  no  such  thing  for  Lucene  based  systems   – Type  guessing  for  fields  we  haven’t  seen  before    
  • 14. Future   •  Greater  scalability   •  More  “NoSQL”   – More  ways  to  update  &  manipulate  documents   •  Analy6cs   – More  powerful  face6ng,  func6ons,  sta6s6cs   •  Improved  Rela6onal  queries   •  More  dynamic  (sejngs  &  configura6on)   •  Con6nued  focus  on  ease  of  use