MapR makes Hadoop a more open platform by supporting industry-standard interfaces, including NFS and ODBC. The NFS interface enables users to leverage standard file-based applications, and makes it easier to get data into and out of the cluster, while the ODBC interface enables users to leverage standard BI tools and query builders. This talk covers the motivation for supporting industry-standard interfaces as well as several real-world use cases. In addition, this talk explains the technical details behind these capabilities and how they actually work.

1. Using Standard File-Based
Applications and SQL-Based
Tools with Hadoop
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2. http://info.mapr.com/HUG-7-2012
 Tomer Shiran
 tshiran@maprtech.com
 Director of Product Management, MapR Technologies
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3. The MapR Distribution for Apache Hadoop
 The open, enterprise-grade distribution for Apache Hadoop
– Open source components
• Hive, Pig, Cascading, HBase, ZooKeeper, Oozie, Flume, Sqoop, Whirr, …
– Enhancements to make Hadoop more open and enterprise-grade
 Fastest growing distribution
– Thousands of clusters deployed
 Now available as a service with Amazon Elastic MapReduce (EMR)
– http://aws.amazon.com/elasticmapreduce/mapr
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4. MapR
Make Hadoop Make Hadoop
more open enterprise-grade
This presentation
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5. Not All Applications Use the Hadoop APIs
Applications and libraries
that use files and/or SQL
30 years
100,000s applications
10,000s libraries
10s programming languages
Applications and libraries
that use the Hadoop APIs
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6. Hadoop Needs Industry-Standard Interfaces
Hadoop • MapReduce and HBase applications
API • Mostly custom-built
• File-based applications
NFS • Supported by most operating systems
• SQL-based tools
ODBC • Supported by most BI applications and
query builders
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7. NFS
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8. Your Data is Your Data
 HDFS-based Hadoop distributions do not (cannot)
support NFS
 Your data is your data – make sure you can access it
– Why store your data in a system which cannot be accessed
by 95% of the world’s applications and libraries?
 Access to HDFS source code != access to your data
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9. The NFS Protocol
 RFC 1813 WRITE3res NFSPROC3_WRITE(WRITE3args) = 7;
struct WRITE3args {
nfs_fh3 file;
 Very simple protocol offset3 offset;
count3 count;
stable_how stable;
 Random reads/writes opaque data<>;
– Read count bytes from };
offset offset of file file
– Write buffer data to READ3res NFSPROC3_READ(READ3args) = 6;
offset offset of a file file
struct READ3args {
nfs_fh3 file;
offset3 offset;
 HDFS does not support count3 count;
random writes so it };
cannot support NFS
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10. S3
o.a.h.fs.s3native.NativeS3FileSystem
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HDFS
o.a.h.hdfs.DistributedFileSystem
Storage Layers
Local File System
o.a.h.fs.LocalFileSystem
MapReduce
FTP
o.a.h.fs.ftp.FTPFileSystem
10
MapR storage layer
o.a.h.fs.FileSystem Interface
com.mapr.fs.MapRFileSystem
Hadoop Was Designed to Support Multiple
Hadoop
NFS interface
FileSystem API

11. One NFS Gateway
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12. Multiple NFS Gateways
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13. Multiple NFS Gateways with Load Balancing
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14. Multiple NFS Gateways with NFS HA (VIPs)
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15. Customer Examples: Import/Export Data
 Network security vendor
– Network packet captures from switches are streamed into the cluster
– New pattern definitions are loaded into online IPS via NFS
 Online measurement company
– Clickstreams from application servers are streamed into the cluster
 SaaS company
– Exporting a database to Hadoop over NFS
 Ad exchange
– Bids and transactions are streamed into the cluster
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16. Customer Examples: Productivity and Operations
 Retailer
– Operational scripts are easier with NFS than DFS + MapReduce
• chmod/chown, file system searches/greps, make, tab-complete
– Consolidate object store with analytics
 Credit card company
– User and project home directories on Linux gateways
• Local files, scripts, source code, …
• Administrators manage quotas, snapshots/backups, …
 Large Internet company
– Web server serve MapReduce results (item relationships) directly from cluster
 Email marketing company
– Object store with HBase and NFS
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17. ODBC
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18. ODBC
 ODBC – Open DataBase Connectivity
– Open standard API for accessing a SQL-based backend
– Developed by Microsoft and Simba Technologies in 1992
 Flagship API for SQL-based BI and reporting
– Excel, Tableau, MicroStrategy, Crystal Reports, …
 Advanced ODBC drivers use the latest 3.52 specification
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19. MapR ODBC Driver
 MapR provides a Hive ODBC 3.52 driver
– Developed in partnership with ODBC inventor Simba Technologies
– Compliant with latest ODBC 3.52 specification
• 32- and 64-bit platform support
• Windows and Linux
 Enables direct SQL access to MapR-stored data by translating SQL to
HiveQL
 SQLizer enables seamless connectivity
– Provides ANSI SQL-92 front-end
– Targeted for existing apps that generate standard SQL queries
– Transforms SQL query into HiveQL query
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20. Example: Tableau
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21. Example: Tableau
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22. Example: Open source query builder (Kaimon)
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23. Example: Microsoft Excel
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24. Join MapR
 Join the fastest growing Hadoop company
 Open positions in every discipline
– Engineers
– Solution Architects
– Product Management
 Email jobs@mapr.com
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25. Time for Questions
 Download slides or send me an email
– http://info.mapr.com/HUG-7-2012
 Download MapR to learn more
– www.mapr.com/download
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