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mixi is one of the largest social networking services in Japan, providing various communication services for over 14M monthly active users. The latest internal mixi project is to replace the in-house search engine with Apache Solr. This session covers two topics; a simple packaging system for Solr that eases the installation process and daily operations, and implementation of a "Did you mean" facility for Japanese queries using a log mining tool. These tools have been released as OSS projects.
Citation preview
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Solr Cluster installation tool "Anuenue" and
"Did You Mean?" for Japanese
Takahiko Ito mixi, Inc.
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mixi? £ One of the largest social
networking service in Japan.
£ Many services to promote communication among users. ¢ Blog, news, game
platform etc ¢ Most of the services
come with search £ 15M monthly active users
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Our current (urgent) project … Replace in-house search engines into a up-to-date search platform!
We have ¢ selected Apache Solr as the search platform! ¢ created a simple OSS package (Anuenue) which
wraps Solr Project URL: http://code.google.com/p/anuenue-wrapper/
Reason why we make Anuenue Deployment / daily operations of Solr search cluster is a bit difficult for ordinary engineers.
¢ We need to edit the configuration files for all the Solr instances respectively
¢ Commands for whole clusters are not provided • We need to write client commands by ourselves • Hadoop provides utility commands for clusters E.g., start-all.sh (start processes), fsck (check all
discs), balancer (rebalance the data blocks)
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What does Anuenue provide? £ Handy configuration of search clusters £ Commands for clusters
¢ Simple commands (post, delete, update, commit etc) ¢ Start and stop commands for processes in cluster.
£ Japanese support ¢ Implementation of Japanese Did-You-Mean facilities ¢ Japanese tokenizer (Sen and Kuromoji)
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Today’s Topics £ Anuenue
¢ Handy configuration of search clusters ¢ Commands for search clusters
£ Did-You-Mean facilities for Japanese queries
¢ Common problem in Did-You-Mean implementation ¢ Mining a Japanese Did-You-Mean dictionary from
query log data
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Cluster configuration with Anuenue £ Cluster setup is done with a special configuration file £ Anuenue assigns more than one roles to instances.
¢ Roles are the functions in a cluster ¢ Anuenue supports three roles (Master, Slave,
Merger)
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Role: master £ Index input data. NOTE: Anuenue provides a command to distribute the input data into master instances (build Solr shard indexes) .
Input Data
Master-1 Master-2 Master-3
Build shard indexes
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Role: slave
Has three functions ¢ Copy (replicate) index
from master ¢ Accept queries from
mergers and then search it own index
¢ Return the results to merger instance
Input Data
Slave-1 Slave-2
Merger-1
Submit queries
Replicate index
Master-1 Master-2
Index input data
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Role: merger £ Forwards queries from
clients to slaves. ¢ Note: clients need not
to know the slave instances (merger adds ‘shard’ parameter with slave instances)
£ Merge the results from all the slave instances and returned the merged results.
Slave-1 Slave-2
Merger
Forwards queries
Client-1 Client-2
Submit queries
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Example: Anuenue cluster
The cluster consists of five machines
¢ Each has one Anuenue instance
Instances ¢ Merger: aa ¢ Master: bb, cc ¢ Slave: dd, ee
Input Data
bb ee
cc dd
aa
Forward queries
Index input data
Client-1 Client-2
Replicate index
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How to assign roles to instance?
Edit cluster configuration file, anuenue-nodes.xml. • Add three elements (mergers, slaves and masters) • In each element, add more than one instance
information (machine name and port number).
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Configuration example Case: there is one merger instance in machine, aa (port 7000) <mergers> <merger> <host>aa</host> <port>7000</port>
</merger> </mergers>
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Specify the index to replicate <masters> <master iname=“master1”> <host>aaaa</host> <port>8983</port> </master> </masters> <slaves>
<slave > <host>bbbb</host> <port>8983</port>
<replicate>master1</replicate> </slave>
</slaves>
Add name of master instance by iname attribute
Specify the master instance to copy the index adding replicate element
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Example: simple cluster settings
Input Data
bb
cc
aa
Forward queries
Index input data
Client-1 Client-2 <mergers> <merger> <host>aa</host> <port>8983</port> </merger> </mergers> <masters> <master iname=“master1”> <host>bb</host> <port>8983</port> </master> </masters> <slaves> <slave> <host>cc</host> <port>8983</port> <replicate>master1</replicate> </slave> </slaves>
Replicate index
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Cluster setup with Anuenue £ Flexible and support various types of search cluster.
£ For example…
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Assign multiple roles
Input Data
instance
Client1 Client2
Index input data
Submit queries
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Large clusters to handle huge data with high QPS
Input Data
Client1
Slave1
Client2
Merger1
Slave3 Slave2 Slave4
Master1 Master2
Slave5 Slave6
Master3 Master4 Master5 Master6
Merger2 Merger3
Client3 ClientN …
After setting up cluster We can make use of commands for clusters.
Anuenue provides ¢ start / stop commands ¢ commands to manipulate the index
Start and stop clusters Users can start / stop clusters by a command (anuenue-distdaemon.sh). Usage: $sh bin/anuenue-distdaemon.sh [start|stop]
Simple commands for clusters
Anuenue also provides basic commands (‘post’, ‘delete’, ‘commit’, ‘optimize’ and ‘update’) for search cluster
¢ The commands are implemented in multi-thread
E.g., $sh bin/anuenue-distcommands.sh post -arg inputDir
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Today’s Topics £ Anuenue
¢ Handy cluster configuration of search clusters ¢ Commands for search clusters
£ Did-You-Mean facilities for Japanese queries
¢ Common problem in Did-You-Mean implementation ¢ Mining a Japanese Did-You-Mean dictionary from
query log data
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What is Did-You-Mean service? £ Suggest correct spelling when users submit queries with
mistakes £ Increase the usability of search service
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Example: Did-You-Mean service
(English: Ugly Betty)
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Common implementation
Many search engines (including Solr) apply distance measures such as Edit Distance [Levenshtein, 1965]
Edit Distance: measure of distance between two sequences. Simply speaking, when two sequences have more common characters, the distance is smaller.
E.g., like 1 likes (small distance) like 1 foobar (large distance)
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Common procedure: Did-You-Mean When a user submits a query, 1. Did-You-Mean service computes edit distance between
input query and words in index. 2. If there is a word whose distance is small,
è Did-You-Mean handler suggests
E.g., when a user submit a query, “pthon”, Did-You-Mean service suggests a word in the index with small distance “python”.
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Problem: Japanese queries
Simple application of edit distance does not work for Japanese è Misspelled queries are sometimes totally different from
the correct one (large distance). E.g., ¢ 墨ともふどうさん (correct: 住友不動産) ¢ 米事案セット (correct: ベイジアンセット)
è These cases are derived from Japanese input method.
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Typing in Japanese query
We input Japanese (query) words with two steps. 1. Type the reading of the Japanese word in Latin
alphabet. 2. Select a desired word from the list of candidates
This step cause a spelling mistake, too large distance to correct spelling
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Example: Typing in Japanese queries
Assume a user wants to submit a query: オバマ (Obama) 1. Type in the reading in Latin alphabet.
reading: obama 2. Select correct spelling.
Possible candidates: オバマ (correct), おばま, 小浜 etc.
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Japanese Did-You-Mean dictionary
£ Because of the large distance problem, simple distance measures (edit distance) do not work.
£ To handle this problem, Anuenue supports a special dictionary for Japanese Did-You-Mean service.
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Dictionary for Japanese Did-You-Mean service
Dictionary has two columns 1. Query with mistakes 2. Correct queries
Query with mistakes
Correct Query
墨ともふどうさん 住友不動産
歌だ光る 宇多田ヒカル
米事案セット ベイジアンセット
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Implementing Did-You-Mean service with the dictionary
When users submit the query with mistakes in dictionary, è Did-You-Mean service
suggests the correct query
NOTE: Anuenue provides handlers for the dictionary format.
Query with mistakes
Correct Query
墨ともふどうさん 住友不動産
歌だ光る 宇多田ヒカル
米事案セット ベイジアンセット
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Problem… How we can create the dictionary? è We can make use of a query log mining tool Oluolu.
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Oluolu £ Creates a spelling correction dictionary from query log £ Extracts pairs of queries (query with spelling mistakes,
query with correct spelling) ¢ Support the Japanese spelling mistakes (from version
0.2) £ runs on the Hadoop framework
Project URL: http://code.google.com/p/oluolu/
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Input to Oluolu: query log Three columns
1. User Id 2. Query string 3. Time of query
submission
User Id Query Time
438904 Pthon 2009-11-21 11:16:12
34443 Java 2009-11-21 12:16:13
438904 Python 2009-11-21 12:16:20
8975 Java Tomcat
2009-11-21 12:16:25
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Procedure: creating Japanese Did-You-Mean dictionary with Oluolu
Oluolu extracts the elements of Japanese Did-You-Mean dictionary with 2 steps.
1. Extract all the query pairs in the same session 2. Validate the query pairs
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Step1: extract query pairs £ Oluolu extracts pairs of
queries in the same session. E.g., Oluolu extracts pair (Pthon and Python).
£ Queries in the same session: a set of queries submit by the same user within small time range.
£ Extracted pairs can be misspelled query and correct query.
User ID Query Time
438904 Pthon 2009-11-21 12:16:12
34443 Java 2009-11-21 12:16:13
438904 Python 2009-11-21 12:16:20
8975 Tomcat 2009-11-21 12:16:25
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Step 2: validate candidate pairs £ Oluolu validates all the query pairs extracted step 1. £ In validation phase (step 2), Oluolu makes use of query
readings.
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Reading of Japanese words £ Japanese words can be convert into the readings in Latin
Alphabets. ¢ こんにちは (reading: konnichiha) ¢ 伊藤 (reading: itou)
FACT: even when Japanese query with spelling mistakes can be totally different from correct query,
è the readings are the same or the distance is small!
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Validate candidate pair with reading Given a query pairs, Oluolu validates the queries with 2 steps
1. Convert the queries into readings with Latin Alphabets 2. Compute edit distance with the two readings
è When the distance is small, the two queries are extracted as a element of Did-You-Mean dictionary.
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Example: step 2 Given a pair of queries: (墨ともふどうさん, 住友不動産)
1. Convert them into readings è readings are the same, “sumitomofudousan”.
3. Compute the distance with the readings è Distance is zero è Extracted as a element of Did-You-Mean dictionary
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Creating Japanese Did-You-Mean dictionary with Oluolu £ Installation requirements
¢ Java 1.6.0 or greater ¢ Hadoop 0.20.0 or greater ¢ Oluolu 0.2.0 or greater
£ Copy the input query log into HDFS £ Run spellcheck task of oluolu $ bin/oluolu spellcheck -input testInput.txt -output output -inputLanguage ja
Preliminary experiments £ Experimental settings
¢ Input data: log file from a mixi service (community search).
• 5 GB data
£ Extracted dictionary ¢ number of elements is over 100.000 ¢ succeeded to extract the query pairs with large edit
distance. • (議Ν, ギニュー) • (不動有利, 不動裕理)
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Current status £ Finished functional tests and stress tests. £ Now replacing an in-house search engine in a small
search service with Anuenue. £ In next phase, we will apply Anuenue to the search
service with large data and high QPS.
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Future work £ Integrate SolrCloud and Zookeeper
¢ Support failover, and rebalance the index
£ Kuromoji, a new OSS Japanese tokenizer
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Summary £ Introduction of Anuenue £ Described a Did-You-Mean facility for Japanese query
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Thank you for your attention!