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         Preferred Infrastructure, Inc.
                      CTO

       Kazuki Ohta <kzk@preferred.jp>
                 http://kzk9.net/


                        1
Self Introduction
•   Kazuki Ohta, CTO at Preferred Infrastructure, Inc. (http://preferred.jp)
    •   Interested in Data Intensive Computing
    •   Graduated U-Tokyo in 2010 (System Software)
        •   Parallel I/O Middleware for Massively Parallel HPC Environment
        •   Summer Intern @ Argonne National Laboratory
    •   ACM ICPC
    •   Hadoop User Group ( http://hugjp.org/ )
•   Personal Site
    •   http://kzk9.net/, @kzk_mover

                                     2
Agenda




  3
Agenda

• Introduction of Sedue




                    3
Agenda

• Introduction of Sedue
• Problems We Had



                    3
Agenda

• Introduction of Sedue
• Problems We Had
• How MongoDB Solved


                   3
Agenda

• Introduction of Sedue
• Problems We Had
• How MongoDB Solved
• Problems in the Integration Phase

                     3
Agenda

• Introduction of Sedue
• Problems We Had
• How MongoDB Solved
• Problems in the Integration Phase
• Future Insight
                     3
4
Sedue Search Engine
•   Enterprise Distributed Search Engine
    •   Developed at Preferred Infrastructure, Inc.
    •   Multi-threaded C++ Server (0.3 million lines)
    •   Often Handles Midscale Contents
        •   50 million documents/items

•   Around 30 customers
    •   Media, Ad, E-Commerce, Digital Library, etc.

                             5
Sedue Data Model
     •      Fixed Schema over De-Normalized Data
         •    Field Definition + Index Definition
         •    How the data is stored (name? type?)
         •    How the data is indexed




ArticleID       Title             Content             Search             Recommend

 ID123          iPad2         iPad2 is coming!
                                                                Filter
 ID124        MongoDB     Durable in Single Server!

 ID125       MongoTokyo            Today!                      Query
                                        6
Sedue Schema (Sample)
<schema>
   <fields>
      <field name=”article_id” type=”string” />
      <field name=”title”        type=”string” />
      <field name=”contents” type=”string” />
      <field name=”date”          type=”datetime” />
   </fields>
   <indexes>
      <index name=”search” type=”invertedindex”
       target=”content” />
      <index name=”recommend” type=”doc2doc”
       target=”title, content” />
   </indexes>
</schema>
                             7
Sedue Query (Sample)
(search:iPad2)?date<today()?sort=date:desc

 QueryText               Filter                  Sort


ArticleID      Title             Content                date

 ID123        iPad2          iPad2 is coming!           today

 ID124       MongoDB     Durable in Single Server!      today

 ID125      MongoTokyo            Today!             yesterday

                            8
Sedue Query (Sample)
((search:iPad2)&(search:coming))?date<today()?sort=date:desc
         QueryText                          Filter               Sort


       ArticleID      Title             Content               date

        ID123        iPad2          iPad2 is coming!         today

        ID124       MongoDB     Durable in Single Server!    today

        ID125      MongoTokyo            Today!             yesterday

                                   9
Sedue Query (Sample)
(recommend:ID124)?date<today()?sort=date:desc

   QueryText                   Filter                   Sort


   ArticleID      Title             Content               date

    ID123        iPad2          iPad2 is coming!         today

    ID124       MongoDB     Durable in Single Server!    today

    ID125      MongoTokyo            Today!             yesterday

                               10
This Data Model is Mapped to
   The Distributed System



             11
Sedue Architecture   Crawler




        12
Sedue Architecture    Crawler




                     Distributed
                     Repository




        12
Sedue Architecture            Crawler




                             Distributed
                             Repository




                Document
                Repository
                  Proxy

        12
Sedue Architecture                   Crawler




                                    Distributed
                                    Repository




                       Document
             Indexer   Repository
                         Proxy

        12
Sedue Architecture                           Crawler




                                            Distributed
       Distributed                          Repository
       File System
          (DFS)




                               Document
                     Indexer   Repository
                                 Proxy

            12
Sedue Architecture                                  Crawler




                                                   Distributed
              Distributed                          Repository
              File System
                 (DFS)




                                      Document
   Searchar                 Indexer   Repository
                                        Proxy

                   12
Sedue Architecture                                     Crawler




                                                         Distributed
                    Distributed                          Repository
                    File System
                       (DFS)




                                            Document
Query
         Searchar                 Indexer   Repository
Server
                                              Proxy

                         12
Sedue Architecture                                     Crawler




                                                                Distributed
                           Distributed                          Repository
                           File System
                              (DFS)



User



                                                   Document
       Query
                Searchar                 Indexer   Repository
       Server
                                                     Proxy

                                12
Sedue Architecture                                         Crawler




                                                                    Distributed
                             Distributed                            Repository
                             File System
                                (DFS)



User



                                                       Document
       Query
                Searchar                     Indexer   Repository
       Server
                                                         Proxy

                           Archive 12
                                   Manager
Sedue Architecture
•   “Distributed Index-Query Mechanism”
    •   Create indices, distribute them, query with them
        •   Most types of search/recommendation algorithm fits
            into this architecture
    •   Otherwords: “Distributed Column-Oriented Database”


•   Once put the documents into Sedue, you can use search/
    recommendation in One System
    •   Register/Query is done via REST API

                               13
OK,
now we developed the

    Distributed
Index-Query Engine!


         14
However...




    15
However...
• THE PROBLEM: THE REAL WORLD




                   15
However...
• THE PROBLEM: THE REAL WORLD
 • Schema is changed once a week.




                      15
However...
• THE PROBLEM: THE REAL WORLD
 • Schema is changed once a week.
 • Real data lacks most columns



                      15
However...
• THE PROBLEM: THE REAL WORLD
 • Schema is changed once a week.
 • Real data lacks most columns
   • Especially in building vertical search over many
      sites (each has its own schema)



                          15
However...
• THE PROBLEM: THE REAL WORLD
 • Schema is changed once a week.
 • Real data lacks most columns
   • Especially in building vertical search over many
      sites (each has its own schema)
  • High Availability is required in some cases
                           15
Especially, Cross-Site Search
          BP
ITPro
ITPro
NikkeiBusiness Online
PC Online
TechOn
Kenplatz
ECO Japan
BPNet



                   




   BP          



                        16
ArticleI      Title         Content                date      FlagA   FlagB   FlagC   FlagXX
  D
ID123         iPad2     iPad2 is coming!          today                       1

ID124       MongoDB     Durable in Single         today
                            Server!
ID125      MongoTokyo        Today!              yesterday                             0

ID126        HBase        0.90 is out!                                                 1

ID127       Cassandra                                                 1

ID128       CouchDB

ID129         Ruby                                today

ID130        Python           N/A                                     0

ID131        Haskell          N/A                                             1

ID132        D-Lang           N/A
                                            17
ArticleI      Title         Content                date      FlagA   FlagB   FlagC   FlagXX
  D
ID123         iPad2     iPad2 is coming!          today                       1

ID124       MongoDB     Durable in Single         today
                            Server!
ID125      MongoTokyo        Today!              yesterday                             0

ID126        HBase        0.90 is out!                                                 1

ID127       Cassandra                                                 1
                                                  Sparse!!!
ID128       CouchDB

ID129         Ruby                                today

ID130        Python           N/A                                     0

ID131        Haskell          N/A                                             1

ID132        D-Lang           N/A
                                            18
One Lucky Thing:
“Pluggable Storage Strategy”



             19
Pluggable Storage Strategy
•   Important: We want to focus on developing application servers
        •   we’re the search engine company, not the database company


•   DocumentRepository, DistributedFileSystem is pluggable!
    •   Many, many NoSQL storages are emerging
    •   Prepare the simple interface on top of them
        •   You can select the underlying storage technology by the
            requirements of the system itself
        •   by document volume, availability, consistency, etc.

                                     20
At first... (Repository)
                                           Online
                API   Replication         Column           Sharding
                                          Addition

Tokyo Cabinet
  (Table DB)    ○         ×                     ○             ×


   MySQL        ×         ○

                            Unfortunately, TokyoTyrant
                           doen’t support Table Database
                                    at that time.
                          21
At first... (DFS)

        API      Setup     Availability Performance


NFS    POSIX      ○          costly       costly


       libhdfs
HDFS                                        ○
        sucks


                      22
23
http://www.mongodb.org/

•   OSS Document-Oriented Database
    •   No Schema, BSON, Rich Query + B-TreeIndex
    •   written in C++
        •   C, C++, Java, PHP, Python, Ruby COOL drivers
    •   Embedded JavaScript Engine
        •   db.insert({“category”:”         ”},              MongoDB Sharding

            {“          ”: “          ”})

        •   db.articles.find({“category”: “             ”})

    •   High Availability by ReplicaSet
    •   High Scalability by Auto-Sharding
                                                  24
As Repository
                                         Online
                API   Replication       Column     Sharding
                                        Addition
Tokyo Cabinet
  (Table DB)    ○           ×              ○          ×


   MySQL        ×           ○


 MongoDB        ○           ○              ○       ongoing
                      (master-master)

                            25
GridFS
• MongoDB as Blob-Storage
 • The contents is splitted into 256kb
      chunks, with some metadata.
 • Performance is not as high as HDFS, but
      still useful in mid-scale deployment.

                        Chunk0
  Large Blob                            Metadata
                        Chunk1



                        26
As DFS
          API       Setup     Availability Performance


 NFS     POSIX       ○          costly       costly


         libhdfs
HDFS                                           ○
          sucks


GridFS   C++         ○            ○

                         27
Now Sedue                             MongoDB
                                         •   Use as Multiple Ways

                Repository
                                             •   Repository + DFS

                                             •   Easy setup!!!

                                         •       30million documents


User
                                             •   No Schema change is required
                  DFS
                                             •   Master-Master Replication

                                             •   Backup once a week

        Sedue
                         MongoDB 1.6     •   4 Production Deployments
                        (Master-Master
                          Replication)       •     1 year

                                  28
We had issues, but MongoDB is OSS!

•   SERVER-1408 (Fixed)
    •   C++ Driver GridFS cannot store over 4G object.
•   SERVER-1372 (Fixed)
    •   NULL check for auto_ptr<DBClientConnection> is missing
•   SERVER-1328 (Fixed)
    •   scons install doesn't end with --prefix parameter?
•   SERVER-1232 (Fixed)
    •   C++ GridFS Client should support larger Chunk Size
•   SERVER-2050
    •   Enables ScopedDbConnection to set the timeout.
                                 29
Got the Mug!




     30
How Long?
•   Prototype Version is in One Week
    •   using C++ client API
    •   about 500 lines
•   Production release in about 2 month
    •   including bugfixes
    •   mongo-user ML is really responsible
    •   Eliot Horowitz merged my patch as quick as possible
    •   The product itself is really stable than I expected (sorry)

                                 31
How we store documents?
• Most Straight Forward Way as Document DB
 • 30m documents, 4M limit each...
{
                             Internal DocumentID (Indexed)
    # Internal Fields
                            Internal ShardingID (Indexed)
    “__docid”: 32132,
    “__arcid”: 3,
    # Data Fields
    “title”: “MongoDB 1.8 is released!”,
    “content”: “Single Server Durability is supported”
}
                            32
DocID Numbering


• Counter by Atomic Increment Operation
 • docid++


                   33
Query
•   Query by DocumentID
    •   db.datadb.find({“__docid”: 12345}) = 1 doc
•   Query by ShardingID
    •   db.datadb.find({“__arcid”: 3}) = <3m doc


•   These two fields have index!
    •   Usage is more like K-V lookup, not the complex query
    •   ShardingID query accesses whole disk structure now
        •   Split by collection is ideal, but more hard to maintain
                                 34
Problems...



     35
Problem: Disk Consumption
• MongoDB consumes the disk space a lot
• Allocate some GBs (configurable), for the
  replication logs
• Mostly append architecture
 • In-place modification is supported, if smaller
    than the original size
• No compression scheme
 • want LZO/gzip support!
                         36
Problem: Consistency
•   Fire-and-Forget Write Behavior
    •   Normally, mongodb insert doesn’t ensure the success at
        the server-side
    •   Need to call getLastError() to ensure it, but slower
    •   In replicated environment, you can specify minimum
        number of servers which succeeded the write operation
•   ReplicaSet mechanism is somewhat in the blackbox?
    •   What consistency it provides? Fail-over mechanism?
    •   Finally chose master-master replication. But will be
        obsoleted?
                                  37
1 billion Docs in MongoDB
            38
Sharding
• Scaling without no application modification




                     39
Sharding
•   Test with 2 nodes (8G mem, 1 SATA disk)
    •   150 Doc Register / sec
        •   Upto 50 million documents
    •   Gradually slowing down...
        •   More latency than non-sharding setup
        •   More parallelism, More node?
•   This results is early 1.7 release
    •   Now enhanced a lot?
                           40
Conclusion
• Sedue is “Distributed Index-Query Engine”
 • Headache about Frequently Changing Schema
• Sedue MongoDB
 • As DocumentRepository + Blob Storage
 • MongoDB handles real data well in some cases
 • Future: Sharding for More Large Deployment
                       41
We’re Hiring!
•   Engineers
    •   Core Search Engine Developer
        •   C++ Expert
        •   Distributed Systems Expert
    •   Professional Support and Service
        •   UNIX/Linux Expert
    •   Summer Intern Student
•   Contact Me
    •   kzk@preferred.jp , @kzk_mover
    •   PFI: @preferred_jp
    •   SedueTeam: @nobu_k, @eiichiroi, @repeatedly
                                   42

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MongoDB as Search Engine Repository @ MongoTokyo2011

  • 1. as Search Engine Document Repository Preferred Infrastructure, Inc. CTO Kazuki Ohta <kzk@preferred.jp> http://kzk9.net/ 1
  • 2. Self Introduction • Kazuki Ohta, CTO at Preferred Infrastructure, Inc. (http://preferred.jp) • Interested in Data Intensive Computing • Graduated U-Tokyo in 2010 (System Software) • Parallel I/O Middleware for Massively Parallel HPC Environment • Summer Intern @ Argonne National Laboratory • ACM ICPC • Hadoop User Group ( http://hugjp.org/ ) • Personal Site • http://kzk9.net/, @kzk_mover 2
  • 5. Agenda • Introduction of Sedue • Problems We Had 3
  • 6. Agenda • Introduction of Sedue • Problems We Had • How MongoDB Solved 3
  • 7. Agenda • Introduction of Sedue • Problems We Had • How MongoDB Solved • Problems in the Integration Phase 3
  • 8. Agenda • Introduction of Sedue • Problems We Had • How MongoDB Solved • Problems in the Integration Phase • Future Insight 3
  • 9. 4
  • 10. Sedue Search Engine • Enterprise Distributed Search Engine • Developed at Preferred Infrastructure, Inc. • Multi-threaded C++ Server (0.3 million lines) • Often Handles Midscale Contents • 50 million documents/items • Around 30 customers • Media, Ad, E-Commerce, Digital Library, etc. 5
  • 11. Sedue Data Model • Fixed Schema over De-Normalized Data • Field Definition + Index Definition • How the data is stored (name? type?) • How the data is indexed ArticleID Title Content Search Recommend ID123 iPad2 iPad2 is coming! Filter ID124 MongoDB Durable in Single Server! ID125 MongoTokyo Today! Query 6
  • 12. Sedue Schema (Sample) <schema> <fields> <field name=”article_id” type=”string” /> <field name=”title” type=”string” /> <field name=”contents” type=”string” /> <field name=”date” type=”datetime” /> </fields> <indexes> <index name=”search” type=”invertedindex” target=”content” /> <index name=”recommend” type=”doc2doc” target=”title, content” /> </indexes> </schema> 7
  • 13. Sedue Query (Sample) (search:iPad2)?date<today()?sort=date:desc QueryText Filter Sort ArticleID Title Content date ID123 iPad2 iPad2 is coming! today ID124 MongoDB Durable in Single Server! today ID125 MongoTokyo Today! yesterday 8
  • 14. Sedue Query (Sample) ((search:iPad2)&(search:coming))?date<today()?sort=date:desc QueryText Filter Sort ArticleID Title Content date ID123 iPad2 iPad2 is coming! today ID124 MongoDB Durable in Single Server! today ID125 MongoTokyo Today! yesterday 9
  • 15. Sedue Query (Sample) (recommend:ID124)?date<today()?sort=date:desc QueryText Filter Sort ArticleID Title Content date ID123 iPad2 iPad2 is coming! today ID124 MongoDB Durable in Single Server! today ID125 MongoTokyo Today! yesterday 10
  • 16. This Data Model is Mapped to The Distributed System 11
  • 17. Sedue Architecture Crawler 12
  • 18. Sedue Architecture Crawler Distributed Repository 12
  • 19. Sedue Architecture Crawler Distributed Repository Document Repository Proxy 12
  • 20. Sedue Architecture Crawler Distributed Repository Document Indexer Repository Proxy 12
  • 21. Sedue Architecture Crawler Distributed Distributed Repository File System (DFS) Document Indexer Repository Proxy 12
  • 22. Sedue Architecture Crawler Distributed Distributed Repository File System (DFS) Document Searchar Indexer Repository Proxy 12
  • 23. Sedue Architecture Crawler Distributed Distributed Repository File System (DFS) Document Query Searchar Indexer Repository Server Proxy 12
  • 24. Sedue Architecture Crawler Distributed Distributed Repository File System (DFS) User Document Query Searchar Indexer Repository Server Proxy 12
  • 25. Sedue Architecture Crawler Distributed Distributed Repository File System (DFS) User Document Query Searchar Indexer Repository Server Proxy Archive 12 Manager
  • 26. Sedue Architecture • “Distributed Index-Query Mechanism” • Create indices, distribute them, query with them • Most types of search/recommendation algorithm fits into this architecture • Otherwords: “Distributed Column-Oriented Database” • Once put the documents into Sedue, you can use search/ recommendation in One System • Register/Query is done via REST API 13
  • 27. OK, now we developed the Distributed Index-Query Engine! 14
  • 29. However... • THE PROBLEM: THE REAL WORLD 15
  • 30. However... • THE PROBLEM: THE REAL WORLD • Schema is changed once a week. 15
  • 31. However... • THE PROBLEM: THE REAL WORLD • Schema is changed once a week. • Real data lacks most columns 15
  • 32. However... • THE PROBLEM: THE REAL WORLD • Schema is changed once a week. • Real data lacks most columns • Especially in building vertical search over many sites (each has its own schema) 15
  • 33. However... • THE PROBLEM: THE REAL WORLD • Schema is changed once a week. • Real data lacks most columns • Especially in building vertical search over many sites (each has its own schema) • High Availability is required in some cases 15
  • 34. Especially, Cross-Site Search BP ITPro ITPro NikkeiBusiness Online PC Online TechOn Kenplatz ECO Japan BPNet BP 16
  • 35. ArticleI Title Content date FlagA FlagB FlagC FlagXX D ID123 iPad2 iPad2 is coming! today 1 ID124 MongoDB Durable in Single today Server! ID125 MongoTokyo Today! yesterday 0 ID126 HBase 0.90 is out! 1 ID127 Cassandra 1 ID128 CouchDB ID129 Ruby today ID130 Python N/A 0 ID131 Haskell N/A 1 ID132 D-Lang N/A 17
  • 36. ArticleI Title Content date FlagA FlagB FlagC FlagXX D ID123 iPad2 iPad2 is coming! today 1 ID124 MongoDB Durable in Single today Server! ID125 MongoTokyo Today! yesterday 0 ID126 HBase 0.90 is out! 1 ID127 Cassandra 1 Sparse!!! ID128 CouchDB ID129 Ruby today ID130 Python N/A 0 ID131 Haskell N/A 1 ID132 D-Lang N/A 18
  • 37. One Lucky Thing: “Pluggable Storage Strategy” 19
  • 38. Pluggable Storage Strategy • Important: We want to focus on developing application servers • we’re the search engine company, not the database company • DocumentRepository, DistributedFileSystem is pluggable! • Many, many NoSQL storages are emerging • Prepare the simple interface on top of them • You can select the underlying storage technology by the requirements of the system itself • by document volume, availability, consistency, etc. 20
  • 39. At first... (Repository) Online API Replication Column Sharding Addition Tokyo Cabinet (Table DB) ○ × ○ × MySQL × ○ Unfortunately, TokyoTyrant doen’t support Table Database at that time. 21
  • 40. At first... (DFS) API Setup Availability Performance NFS POSIX ○ costly costly libhdfs HDFS ○ sucks 22
  • 41. 23
  • 42. http://www.mongodb.org/ • OSS Document-Oriented Database • No Schema, BSON, Rich Query + B-TreeIndex • written in C++ • C, C++, Java, PHP, Python, Ruby COOL drivers • Embedded JavaScript Engine • db.insert({“category”:” ”}, MongoDB Sharding {“ ”: “ ”}) • db.articles.find({“category”: “ ”}) • High Availability by ReplicaSet • High Scalability by Auto-Sharding 24
  • 43. As Repository Online API Replication Column Sharding Addition Tokyo Cabinet (Table DB) ○ × ○ × MySQL × ○ MongoDB ○ ○ ○ ongoing (master-master) 25
  • 44. GridFS • MongoDB as Blob-Storage • The contents is splitted into 256kb chunks, with some metadata. • Performance is not as high as HDFS, but still useful in mid-scale deployment. Chunk0 Large Blob Metadata Chunk1 26
  • 45. As DFS API Setup Availability Performance NFS POSIX ○ costly costly libhdfs HDFS ○ sucks GridFS C++ ○ ○ 27
  • 46. Now Sedue MongoDB • Use as Multiple Ways Repository • Repository + DFS • Easy setup!!! • 30million documents User • No Schema change is required DFS • Master-Master Replication • Backup once a week Sedue MongoDB 1.6 • 4 Production Deployments (Master-Master Replication) • 1 year 28
  • 47. We had issues, but MongoDB is OSS! • SERVER-1408 (Fixed) • C++ Driver GridFS cannot store over 4G object. • SERVER-1372 (Fixed) • NULL check for auto_ptr<DBClientConnection> is missing • SERVER-1328 (Fixed) • scons install doesn't end with --prefix parameter? • SERVER-1232 (Fixed) • C++ GridFS Client should support larger Chunk Size • SERVER-2050 • Enables ScopedDbConnection to set the timeout. 29
  • 49. How Long? • Prototype Version is in One Week • using C++ client API • about 500 lines • Production release in about 2 month • including bugfixes • mongo-user ML is really responsible • Eliot Horowitz merged my patch as quick as possible • The product itself is really stable than I expected (sorry) 31
  • 50. How we store documents? • Most Straight Forward Way as Document DB • 30m documents, 4M limit each... { Internal DocumentID (Indexed) # Internal Fields Internal ShardingID (Indexed) “__docid”: 32132, “__arcid”: 3, # Data Fields “title”: “MongoDB 1.8 is released!”, “content”: “Single Server Durability is supported” } 32
  • 51. DocID Numbering • Counter by Atomic Increment Operation • docid++ 33
  • 52. Query • Query by DocumentID • db.datadb.find({“__docid”: 12345}) = 1 doc • Query by ShardingID • db.datadb.find({“__arcid”: 3}) = <3m doc • These two fields have index! • Usage is more like K-V lookup, not the complex query • ShardingID query accesses whole disk structure now • Split by collection is ideal, but more hard to maintain 34
  • 54. Problem: Disk Consumption • MongoDB consumes the disk space a lot • Allocate some GBs (configurable), for the replication logs • Mostly append architecture • In-place modification is supported, if smaller than the original size • No compression scheme • want LZO/gzip support! 36
  • 55. Problem: Consistency • Fire-and-Forget Write Behavior • Normally, mongodb insert doesn’t ensure the success at the server-side • Need to call getLastError() to ensure it, but slower • In replicated environment, you can specify minimum number of servers which succeeded the write operation • ReplicaSet mechanism is somewhat in the blackbox? • What consistency it provides? Fail-over mechanism? • Finally chose master-master replication. But will be obsoleted? 37
  • 56. 1 billion Docs in MongoDB 38
  • 57. Sharding • Scaling without no application modification 39
  • 58. Sharding • Test with 2 nodes (8G mem, 1 SATA disk) • 150 Doc Register / sec • Upto 50 million documents • Gradually slowing down... • More latency than non-sharding setup • More parallelism, More node? • This results is early 1.7 release • Now enhanced a lot? 40
  • 59. Conclusion • Sedue is “Distributed Index-Query Engine” • Headache about Frequently Changing Schema • Sedue MongoDB • As DocumentRepository + Blob Storage • MongoDB handles real data well in some cases • Future: Sharding for More Large Deployment 41
  • 60. We’re Hiring! • Engineers • Core Search Engine Developer • C++ Expert • Distributed Systems Expert • Professional Support and Service • UNIX/Linux Expert • Summer Intern Student • Contact Me • kzk@preferred.jp , @kzk_mover • PFI: @preferred_jp • SedueTeam: @nobu_k, @eiichiroi, @repeatedly 42

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