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Today’s libraries are curating large digital collections, indexing millions of full-text documents, and preserving Terabytes of data for future generations. This means that libraries must adopt new methods for the processing of large amounts of data. And this is exactly where the SCAPE project (www.scape-project-eu) comes into play. The SCAPE project offers an open source infrastructure, as well as a variety of tools and services for the distributed processing of large data sets with a focus on long-term preservation. In this project context, we are here presenting an open source infrastructure for preserving large collections of digital objects created at the Austrian National Library for quality assurance tasks as part of the management of a large digital book collection. We describe the experimental cluster hardware and the software components used for creating the infrastructure. More concretely, we will show a set of best practices for the data analysis of large document image collections on the basis of Apache Hadoop. Different types of hadoop jobs (Hadoop-Streaming-API, Hadoop MapReduce, and Hive) are used as basic components, and the Taverna workflow description language and execution engine (www.taverna.org.uk) is used for orchestrating complex data processing tasks.
Citation preview
Sven Schlarb Austrian National Library
Elag 2013 Gent, Belgium, May 29, 2013
An open source infrastructure for preserving large collections of digital objects The SCAPE project at the Austrian National Library
• SCAPE project overview • Application areas at the Austrian National Library
• Web Archiving • Austrian Books Online
• SCAPE at the Austrian National Library • Hardware set-up • Open source software architecture
• Application Scenarios • Lessons learnt
2
Overview
This work was partially supported by the SCAPE Project. The SCAPE project is co‐funded by the European Union under FP7 ICT‐2009.4.1 (Grant Agreement number 270137).
• Ability to process large and complex data sets in preservation scenarios
• Increasing amount of data in data centers and memory institutions
Motivation
This work was partially supported by the SCAPE Project. The SCAPE project is co‐funded by the European Union under FP7 ICT‐2009.4.1 (Grant Agreement number 270137).
Volume, Velocity, and Variety of data
1970 2000 2030
cf. Jisc (2012) Activity Data: Delivering benefits from the data deluge. available at http://www.jisc.ac.uk/publications/reports/2012/activity-data-delivering-benefits.aspx
• “Big data” is a buzzword, just a vague idea • No definitive GB, TB, PB, (…) threshold where data
becomes “big data”, depends on the institutional context
• Massive growth of data to be stored and processed • Situation where solutions that are usually employed do
not fulfill new scalability requirements • Pushing the limit of conventional data base solutions • Simple batch processing becomes tedious or even impossible
„Big Data“ in a Library context?
This work was partially supported by the SCAPE Project. The SCAPE project is co‐funded by the European Union under FP7 ICT‐2009.4.1 (Grant Agreement number 270137).
5
• EU-funded FP7 project, lead by Austrian Institute of Technology
• Consortium: 16 Partners • National Libraries • Data Centers and Memory
Institutions • Research institutes and
Universities • Commercial partners
• Started 2011, runs until mid/end-2014
SCAPE Consortium
This work was partially supported by the SCAPE Project. The SCAPE project is co‐funded by the European Union under FP7 ICT‐2009.4.1 (Grant Agreement number 270137).
Testbeds •Data sets •Integration •Evaluation
Preservation Components •Quality Assurance
•Scalable Components •Automation-ready Tools
Planning and Watch •Institutional Policies •Technical Watch
•Automated Planning
Takeup •Stakeholders and Communities
•Dissemination •Training Activities •Sustainability
SCAPE Project Overview
Platform •Automation •Workflows
•Parallelization •Virtualization
Sort
Shuffle
Merge
Input data
Input split 1
Record 1
Record 2
Record 3
Input split 2
Record 4
Record 5
Record 6
Input split 3
Record 7
Record 8
Record 9
MapReduce/Hadoop in a nutshell
7 This work was partially supported by the SCAPE Project. The SCAPE project is co‐funded by the European Union under FP7 ICT‐2009.4.1 (Grant Agreement number 270137).
Task1
Map Reduce
Task 2
Task 3
Output data
Aggregated Result
Aggregated Result
Experimental Cluster Job Tracker Task Trackers
Data Nodes
Name Node
CPU: 1 x 2.53GHz Quadcore CPU (8 HyperThreading cores) RAM: 16GB DISK: 2 x 1TB DISKs configured as RAID0 (performance) – 2 TB effective • Of 16 HT cores: 5 for Map; 2 for Reduce; 1 for operating system. 25 processing cores for Map tasks and 10 cores for Reduce tasks
CPU: 2 x 2.40GHz Quadcore CPU (16 HyperThreading cores) RAM: 24GB DISK: 3 x 1TB DISKs configured as RAID5 (redundancy) – 2 TB effective
• Access via REST API • Workflow engine for complex
jobs • Hive as the frontend for
analytic queries • MapReduce/Pig for
Extraction, Transform, and Load (ETL)
• „Small“ objects in HDFS or HBase
• „Large “ Digital objects stored on NetApp Filer
9
Platform Architecture
This work was partially supported by the SCAPE Project. The SCAPE project is co‐funded by the European Union under FP7 ICT‐2009.4.1 (Grant Agreement number 270137).
Digital Objects Storage
hOCR/Text/METS/(W)ARC in HDFS
MapReduce
Hive (SQL) Pig (ETL) HBase
Taverna Workflow engine
REST API
• Web Archiving • Scenario 1: Web Archive Mime Type Identification
• Austrian Books Online • Scenario 2: Image File Format Migration • Scenario 3: Comparison of Book Derivatives • Scenario 4: MapReduce in Digitised Book Quality Assurance
Application scenarios
• Physical storage 19 TB • Raw data 32 TB • Number of objects
1.241.650.566
• Domain harvesting • Entire top-level-domain
.at every 2 years
• Selective harvesting • Important websites that
change regularly
• Event harvesting • Special occasions and
events (e.g. elections)
Key Data Web Archiving
(W)ARC Container
JPG
GIF
HTM
HTM
MID
(W)ARC InputFormat (W)ARC RecordReader
based on HERITRIX
Web crawler read/write (W)ARC
MapReduce
JPG Apache Tika detect MIME
Map Reduce
image/jpg
image/jpg 1 image/gif 1 text/html 2 audio/midi 1
Scenario 1: Web Archive Mime Type Identification
Tool integration pattern Throughput (GB/min) TIKA detector API call in Map phase 6,17 GB/min FILE called as command line tool from map/reduce 1,70 GB/min TIKA JAR command line tool called from map/reduce 0,01 GB/min
Amount of data Number of ARC files Throughput (GB/min) 1 GB 10 x 100 MB 1,57 GB/min 2 GB 20 x 100 MB 2,5 GB/min 10 GB 100 x 100 MB 3,06 GB/min 20 GB 200 x 100 MB 3,40 GB/min 100 GB 1000 x 100 MB 3,71 GB/min
TIKA 1.0 DROID 6.01
Scenario 1: Web Archive Mime Type Identification
• Public private partnership with Google • Only public domain • Objective to scan ~ 600.000 Volumes
• ~ 200 Mio. pages
• ~ 70 project team members • 20+ in core team
• ~ 130K physical volumes scanned so far • ~ 40 Mio pages
Key Data Austrian Books Online
Digitisation Download & Storage
Quality Control Access
15
ADOCO (Austrian Books Online Download & Control)
This work was partially supported by the SCAPE Project. The SCAPE project is co‐funded by the European Union under FP7 ICT‐2009.4.1 (Grant Agreement number 270137).
https://confluence.ucop.edu/display/Curation/PairTree
Google Public Private Partnership
ADOCO
• Task: Image file format migration • TIFF to JPEG2000 migration
• Objective: Reduce storage costs by reducing the size of the images
• JPEG2000 to TIFF migration • Objective: Mitigation of the JPEG2000 file
format obsolescense risk
• Challenges: • Integrating validation, migration, and
quality assurance • Computing intensive quality
assurance
Scenario 2: Image file format migration
• Task: Compare different versions of the same book • Images have been manipulated (cropped, rotated) and stored
in different locations • Images come from different scanning sources or were subject
to different modification procedures
• Challenges: • Computing intensive (Average runtime per book on a single
quad-core server ~ 4,5 hours) • 130.000 books, ~320 pages each
• SCAPE tool: Matchbox
Scenario 2: Comparison of book derivatives
• ETL Processing of 60.000 books, ~ 24 Million pages • Using Taverna‘s „Tool service“ (remote ssh execution) • Orchestration of different types of hadoop jobs
• Hadoop-Streaming-API • Hadoop Map/Reduce • Hive
• Workflow available on myExperiment: http://www.myexperiment.org/workflows/3105
• See Blogpost: http://www.openplanetsfoundation.org/blogs/2012-08-07-big-data-processing-chaining-hadoop-jobs-using-taverna
Scenario 3: MapReduce in Quality Assurance
19
• Create input text files containing file paths (JP2 & HTML)
• Read image metadata using Exiftool (Hadoop Streaming API)
• Create sequence file containing all HTML files
• Calculate average block width using MapReduce
• Load data in Hive tables • Execute SQL test query
Scenario 3: MapReduce in Quality Assurance
20
find
/NAS/Z119585409/00000001.jp2 /NAS/Z119585409/00000002.jp2 /NAS/Z119585409/00000003.jp2 … /NAS/Z117655409/00000001.jp2 /NAS/Z117655409/00000002.jp2 /NAS/Z117655409/00000003.jp2 … /NAS/Z119585987/00000001.jp2 /NAS/Z119585987/00000002.jp2 /NAS/Z119585987/00000003.jp2 … /NAS/Z119584539/00000001.jp2 /NAS/Z119584539/00000002.jp2 /NAS/Z119584539/00000003.jp2 … /NAS/Z119599879/00000001.jp2l /NAS/Z119589879/00000002.jp2 /NAS/Z119589879/00000003.jp2 ...
...
NAS
reading files from NAS
1,4 GB 1,2 GB
60.000 books (24 Million pages): ~ 5 h + ~ 38 h = ~ 43 h
Jp2PathCreator HadoopStreamingExiftoolRead
Z119585409/00000001 2345 Z119585409/00000002 2340 Z119585409/00000003 2543 … Z117655409/00000001 2300 Z117655409/00000002 2300 Z117655409/00000003 2345 … Z119585987/00000001 2300 Z119585987/00000002 2340 Z119585987/00000003 2432 … Z119584539/00000001 5205 Z119584539/00000002 2310 Z119584539/00000003 2134 … Z119599879/00000001 2312 Z119589879/00000002 2300 Z119589879/00000003 2300 ...
Reading image metadata
21
find
/NAS/Z119585409/00000707.html /NAS/Z119585409/00000708.html /NAS/Z119585409/00000709.html … /NAS/Z138682341/00000707.html /NAS/Z138682341/00000708.html /NAS/Z138682341/00000709.html … /NAS/Z178791257/00000707.html /NAS/Z178791257/00000708.html /NAS/Z178791257/00000709.html … /NAS/Z967985409/00000707.html /NAS/Z967985409/00000708.html /NAS/Z967985409/00000709.html … /NAS/Z196545409/00000707.html /NAS/Z196545409/00000708.html /NAS/Z196545409/00000709.html ...
Z119585409/00000707
Z119585409/00000708
Z119585409/00000709
Z119585409/00000710
Z119585409/00000711
Z119585409/00000712
NAS
reading files from NAS
1,4 GB 997 GB (uncompressed)
60.000 books (24 Million pages): ~ 5 h + ~ 24 h = ~ 29 h
HtmlPathCreator SequenceFileCreator
SequenceFile creation
22
Z119585409/00000001
Z119585409/00000002
Z119585409/00000003
Z119585409/00000004
Z119585409/00000005 ...
Z119585409/00000001 2100 Z119585409/00000001 2200 Z119585409/00000001 2300 Z119585409/00000001 2400
Z119585409/00000002 2100 Z119585409/00000002 2200 Z119585409/00000002 2300 Z119585409/00000002 2400
Z119585409/00000003 2100 Z119585409/00000003 2200 Z119585409/00000003 2300 Z119585409/00000003 2400
Z119585409/00000004 2100 Z119585409/00000004 2200 Z119585409/00000004 2300 Z119585409/00000004 2400
Z119585409/00000005 2100 Z119585409/00000005 2200 Z119585409/00000005 2300 Z119585409/00000005 2400
Z119585409/00000001 2250 Z119585409/00000002 2250 Z119585409/00000003 2250 Z119585409/00000004 2250 Z119585409/00000005 2250
Map Reduce HadoopAvBlockWidthMapReduce
SequenceFile Textfile
Calculate average block width using MapReduce
60.000 books (24 Million pages): ~ 6 h
23
HiveLoadExifData & HiveLoadHocrData
jid jwidth Z119585409/00000001 2250
Z119585409/00000002 2150
Z119585409/00000003 2125
Z119585409/00000004 2125
Z119585409/00000005 2250
hid hwidth Z119585409/00000001 1870
Z119585409/00000002 2100
Z119585409/00000003 2015
Z119585409/00000004 1350
Z119585409/00000005 1700
htmlwidth
jp2width
Z119585409/00000001 1870 Z119585409/00000002 2100 Z119585409/00000003 2015 Z119585409/00000004 1350 Z119585409/00000005 1700
Z119585409/00000001 2250 Z119585409/00000002 2150 Z119585409/00000003 2125 Z119585409/00000004 2125 Z119585409/00000005 2250
CREATE TABLE jp2width (hid STRING, jwidth INT)
CREATE TABLE htmlwidth (hid STRING, hwidth INT)
Analytic Queries
24
HiveSelect
jid jwidth Z119585409/00000001 2250
Z119585409/00000002 2150
Z119585409/00000003 2125
Z119585409/00000004 2125
Z119585409/00000005 2250
hid hwidth Z119585409/00000001 1870
Z119585409/00000002 2100
Z119585409/00000003 2015
Z119585409/00000004 1350
Z119585409/00000005 1700
htmlwidth jp2width
jid jwidth hwidth Z119585409/00000001 2250 1870
Z119585409/00000002 2150 2100
Z119585409/00000003 2125 2015
Z119585409/00000004 2125 1350
Z119585409/00000005 2250 1700
select jid, jwidth, hwidth from jp2width inner join htmlwidth on jid = hid
Analytic Queries
• Emergence of new options for creating large-scale storage and processing infrastructures • HDFS as storage master or staging area? • Create a local cluster or rent a cloud infrastructure?
• Apache Hadoop offers a stable core for building a large scale processing platform that is ready to be used in production
• Important to select carefully additional components from the Apache Hadoop Ecosystem (HBase, Hive, Pig, Oozie, Yarn, Ambari, etc.) that fit your needs
25
Lessons learnt
This work was partially supported by the SCAPE Project. The SCAPE project is co‐funded by the European Union under FP7 ICT‐2009.4.1 (Grant Agreement number 270137).
Further information • Project website: www.scape-project.eu • Github repository: www.github.com/openplanets • Project Wiki: www.wiki.opf-labs.org/display/SP/Home SCAPE tools mentioned • SCAPE Platform
• http://www.scape-project.eu/publication/an-architectural-overview-of-the-scape-preservation-platform
• Jpylyzer – Jpeg2000 validation • http://www.openplanetsfoundation.org/software/jpylyzer
• Matchbox – Image comparison • https://github.com/openplanets/scape/tree/master/pc-qa-matchbox
Thank you! Questions?