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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!