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Hadoop use case: A scalable vertical search engine
Iván de Prado Alonso, Datasalt Co-founder Twitter: @ivanprado
Content
§ The problem § The obvious solution § When the obvious solution fails… § … Hadoop comes to the rescue § Advantages & disadvantages § Improvements
¿What is a vertical search engine?
Vertical Search Engine
Provider 1
Provider 2
Feed
Feed
Searches
Searches
Some of them
The “obvious” architecture
The first thing that comes to your mind
Database
Feed
Lucene/SolrIndex
Search Page
Download & Process
Does it exist?Has it changed?Insert/update
Insert/update
How it works
§ Feed download § For every register in the feed
• Check for existence in the DB • If it exists and has changed, update
ª The DB ª The Index
• If it doesn’t exist, insert into ª The DB ª The Index
How it works (II)
§ The Database is used for • Checking for register existence (avoiding
duplicates) • Managing the data with SQL facility
§ Lucene/Solr is used for • Quick searches • Searching by structured fields • Free-text searches • Faceting
But if things go well...…
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Huge jam!
“Swiss army knife of the 21st century”
Media Guardian Innovation Awards
http://www.guardian.co.uk/technology/2011/mar/25/media-guardian-innovation-awards-apache-hadoop
Hadoop
“The Apache Hadoop software library is a
framework that allows for the distributed processing
of large data sets across clusters of computers using
a simple programming model”
From Hadoop homepage
File System
§ Distributed File System (HDFS) • Cluster of nodes exposing their storage
capacity • Big blocks: 64 Mb • Fault tolerant (replication) • Big files storage
MapReduce
§ Two functions (Map y Reduce) • Map(k, v) : [z,w]* • Reduce(z, w*) : [u, v]*
§ Example: word count • Map([document, null]) -> [word, 1]* • Reduce(word, 1*) -> [word, total]
§ MapReduce & SQL • SELECT word, count(*) GROUP BY word
§ Distributed execution on a cluster § Horizontal scalability
Ok, that’s cool, but… ¿How does it solve my problem?
Because…
§ Hadoop is not a Database § Hadoop “apparently” only
processes data § Hadoop does not allow “lookups”
Hadoop is a paradigm shift difficult to assimilate
Architecture
Philosophy
§ Always reprocess everything. ¡EVERYTHING! § ¿Why?
• More bug tolerant • More flexible • More efficient. E.g.:
ª With a 7200 RPM HD – Random IOPS – 100 – Sequencial Read/Write – 40 MB/s – Hypothesis: 5 Kb register size
ª … it is faster to rewrite all data than to perform random updates when more than 1.25% of the registers has changed.
– 1 GB, 200.000 registers » Sequential writing: 25 sg » Random writing: 33 min!
Fetcher
§ MapReduce • Input: [feed_url, null]* • Mapper: identity • Reducer(feed_url,
null*) ª Download the
feed_url and store it in a HDFS folder
Feeds are downloaded and stored in the HDFS.
Reducer Task
Reducer Task
Reducer Task
HDFS
Processor
§ MapReduce • Input: [feed_path, null]* • Map(feed_path, null) : [id, documents]*
ª The feed is parsed and converted into documents
• Reducer(id, [document]*): [id, document] ª Receives a list of documents and keeps the most
recent one (deduplication) ª A unique and global identifier is required
(idProvider + idInternal)
• Output: [id, document]*
Feeds are parsed, converted into documents and deduplicated
Processor (II)
§ Possible problem: • Very large feeds
ª Does not scale, as one task will deal with the full feed.
§ Solution • Write a custom InputFormat that divides
the feed in smaller pieces.
Serialization
§ Writables • Native Hadoop Serialization • Low level API • Basic types: IntWritable, Text, etc.
§ Others • Thrift, Avro, Protostuff • Backwards compatibility
Indexer Production Solr
Reducer Task
Reducer Task
Reducer Task
Index - Shard 1
Index - Shard 2
Index - Shard 3
Index - Shard 1
Index - Shard 2
Index - Shard 3
Hot swap
Hot swap
Hot swap
Web Server
Web Server
Indexer (II)
§ SOLR-1301 • https://issues.apache.org/jira/browse/SOLR-1301 • SolrOutputFormat • 1 index per reducer • A custom Partitioner can be used to control where to
place each document § Another option
• Writing your own indexation code ª By creating a custom output format ª By Indexing at the reducer level. In each reduce call:
– Open an index – Write all incoming registers – Close the index
Search & Partitioning
§ Different partitioning schemas • Horizontal
ª Each search involves all shards
• Vertical: by ad type, country, etc. ª Searches can be restricted to the involved shard
§ Solr for index serving. Possibilities: ª Non federated Solr
– Only for vertical partitioning ª Distributed Solr ª Solr Cloud
Reconciliation
§ ¿How to register changes? • Changes in price, features, etc. • MapReduce:
ª Input: [id, document]* – From last execution – From current processing
ª Map: identity ª Reduce(id, [document]*) : [id, document]
– Documents grouped by ID. New and old documents come together. – New and old documents are compared. – The relevant information is stored in the new document (e.g, the old price) – Only the new document is emited.
§ This is the closest thing in Hadoop to a DB
From Fetcher Reconciliation
Reconciliateddocuments
Last execution !le
Next steps
Advantages of the architecture
§ Horizontal Scalability • If properly programmed
§ High tolerance to failures and bugs • Always everything is reprocessed
§ Flexible • It is easy to do big changes
§ High decoupling • Indexes are the unique interaction between the
back-end and the front-end • Web servers can keep running even if the back-
end is broken.
Disadvantages
§ Batch processing • No real-time or “near” real-time • Update cycles of hours
§ Completely different programming paradigm • High learning curve
Improvements
§ System for images § Fuzzy duplicates detection § Plasam:
• Mixing this architecture with a by-pass system that provides near real time updates to the FE indexes ª Implementing a by-pass to the Solrs ª System for ensuring data consistency
– Without back jumps in time
• That combines the advantages of the proposed architecture but with near real time
• Datasalt has a prototype ready
