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Dictionary Based Annotation at scale with Spark, SolrTextTagger and OpenNLPSujit Pal, Elsevier Labs
Introduction• About Me
– Work at Elsevier Labs.
– Interested in Search, NLP and Distributed Processing.
– URL: labs.elsevier.com
– Email: [email protected]
– Twitter: @palsujit
• About Elsevier
– World’s largest publisher of STM Books and Journals.
– Uses Data to inform and enable consumers of STM info.
– And like everybody else, we are hiring!
Agenda• Overview and Background
• Features and API
• Scaling out
• Q&A
Overview/Background
Problem Definition• What is the problem?
– Annotate millions of documents from different corpora.
• 14M docs from Science Direct alone.
• More from other corpora, dependency parsing, etc.
– Critical step for Machine Reading and Knowledge Graph applications.
• Why is this such a big deal?
– Takes advantage of existing linked data.
– No model training for multiple complex STM domains.
– However, simple until done at scale.
Annotation Pipeline
Dictionary Based NE Annotator (SoDA)• Part of Document Annotation Pipeline.
• Annotates text with Named Entities from external Dictionaries.
• Built with Open Source Components
– Apache Solr – Highly reliable, scalable and fault-tolerant search index.
– SolrTextTagger – Solr component for text tagging, uses Lucene FST technology.
– Apache OpenNLP – Machine Learning based toolkit for processing Natural Language Text.
– Apache Spark – Lightning fast, large scale data processing.
• Uses ideas from other Open Source libraries
– FuzzyWuzzy – Fuzzy String Matching like a boss.
• Contributed back to Open Source
– https://github.com/elsevierlabs-os/soda
SoDA Architecture
How does it work (exact/case matching)?• Uses Aho-Corasick algorithm – treats the dictionary as a FST and streams text against
it. Matches all patterns simultaneously. Diagram shows FST for vocabulary {“his”, “he”, “her”, “hers”, “she”}.
• Michael McCandless implemented FSTs in Lucene (blog post).• David Smiley built SolrTextTagger to use Lucene FSTs.• SoDA uses SolrTextTagger for streaming exact and case-insensitive matching.
How does it work (fuzzy matching)?• Pre-normalizes each dictionary entry into various forms
– Original – “Astrocytoma, Subependymal Giant Cell”
– Lowercased – “astrocytoma, subependymal giant cell”
– Punctuation – “astrocytoma subependymal giant cell”
– Sorted – “astrocytoma cell giant subependymal”
– Stemmed – “astrocytoma cell giant subependym”
• Uses OpenNLP to parse input text into phrases, normalizes each phrase into the desired normalization level and matches against corresponding field.
• Caller specifies normalization level.
Features and API
Feature Overview• Provides JSON over HTTP interface
– Compose request as JSON document
– HTTP POST document to JSON endpoint URL (HTTP GET for URL only requests).
– Receive response as JSON document.
• Language-Agnostic and Cross-Platform.
• API can be used from standalone clients, Spark jobs and Databricks notebooks.
• Examples in Scala and Python
Services• Status
– index.json – returns a JSON (suitable for health check monitoring)
• Single Lexicon Services– annot.json – annotates a block of text in streaming manner. Supports different levels of
matching (strict to permissive).
– matchphrase.json – annotates short phrases. Supports same matching levels as annot.json.
• Multi-Lexicon Services– dicts.json – lists all lexicons available.
– coverage.json – returns number of annotations by lexicon found for text across all available lexicons.
• Indexing Services– delete.json – deletes entire lexicon from index.
– add.json – adds an entry to the specified lexicon.
Annotation Service I/OExample annotation request{
“lexicon” : “countries”,
“text” : “Institute of Clean Coal Technology, East China University”,
“matching” : “exact”
}
Example annotated response[ { “id” : “http://www.geonames.org/CHN”, “lexicon” : “countries”, “begin” : 41, “end” : 46, “coveredText” : “China”, “confidence” : 1.0 }]
Calling Annotation Service• Client originally written in Python, using built-in json and requests libraries.
• For Scala client, SoDA JAR provides classes to mimic json and requests functionality in Scala.
• Input to both our (somewhat contrived) examples are: (pii: String, affStr: String) tuples as shown.
• Match against a country lexicon to find country names.
Annotation Service – Python Client
Annotation Service – Scala Client
Annotation Service - Outputs• Each Annotation result provides:
– Entity ID (not shown)– Begin position in text– End position in text– Matched Text– Confidence (not shown)
• Zero or more Annotations possible per input text.
Loading Dictionaries• Dictionary entries represented by:
– Lexicon Name
– Entry ID (unique across lexicons)
– List of possible synonym terms
• JSON Request to add an entry for MeSH dictionary.
{ “id”: http://id.nlm.nih.gov/mesh/2015/M0021699,
“lexicon”: “mesh”,
“names”: [“Baby Tooth”, “Dentitions, Primary”, “Milk Tooth”, ...],
“commit”: false }
• Preferable to commit periodically and after batch.
Loading Dictionaries – Scala Client
Scaling Out
SoDA Performance – Expected• Test: annotate 14M docs in “reasonable time”.
– Approx. 3s/doc with SoDA+Solr on ec2 r3.large box (15.5GB RAM, 32GB SSD, 2vCPU).
– Total estimated time: 16.2 months!
• Questions
– Can we make the process faster?
– Can we scale out the process?
Where is the time being spent?• Majority of time spent in Solr.
• Some time spent in SoDA (decreases slower than Solr as transactions get shorter).
• Almost no additional time spent in Spark.
Optimization #1: Combine Paragraphs• Performance measured using 10K random
articles.
• Time to annotate 1 article: Mean 2.9s, Median 2.1s.
• Annotation done per paragraph, 40 paragraphs/article on average.
• Reduce HTTP network + parsing overhead by sending full document.
• Time to annotate 1 article: Mean 1.4s, Median 0.3s.
• 2x - 7x improvement.
Optimization #2: Tune Solr GC• OOB Solr would GC very frequently, slowing
down Spark and causing timeouts.
• Current Index Size: 2.1 GB
• Need to size box so approximately 75% RAM given to OS and remaining 25% allocated to Solr (Uwe Schindler's Blog Post).
• Heap size should be 3-4x index size (Internal Guideline).
• Current Solr Heap Size = 8 GB
• RAM is 30.5 GB
• CMS (Concurrent Mark-Sweep) Garbage Collection.
Optimization #3: Larger Spark Cluster• Running on cluster Master + 4 Workers.
• Each worker has 1 Executor with 4 Cores.
• Number of simultaneous Solr clients = 16 (4 workers * 1 executor * 4 cores) – measured with lsof –p in a loop on Solr server.
• Throughput increases with number of partitions till about 2x the number of worker cores.
• Best throughput 5 docs/sec with #-partitions=30 for 16 cores.
Optimization #4: Solr Scaleout• Upgrade to r3.xlarge (30.5GB RAM, 80GB SSD,
4vCPU)
– Throughput 7.9 docs/s
• Upgrade to 2x r3.2xlarge (61GB RAM, 160GB SSD, 8vCPU) with c3.large LB (3.75GB RAM, 32GB Disk, 2vCPU) running HAProxy.
#-workers #-requests/server
Throughput (docs/sec)
4 8 8.62
8 16 17.334
12 24 20.64
16 32 26.845
Performance – Did we meet expectations?• At 26 docs/sec and 14M documents, it will take our current cluster little over 6 days to annotate
against our largest dictionary (8M entries).
• Throughput scales linearly @ 1.5 docs/sec per additional worker, as long as Solr servers have enough capacity to serve requests.
• Each Solr box (as configured) can serve sustained loads of up to 30-35 simultaneous requests.
• Number of simultaneous requests approximately equal to number of worker cores.
• Example: annotate 14M documents in 3 days.
– Throughput required: 14M / (3 * 86400) = 54 docs/s
– Number of workers: 54 / 1.5 = 36 workers
– Number of simultaneous requests (4 cores/worker) = 36 * 4 = 144
– Number of Solr servers: 144 / 32 = 4.5 = 5 servers
Future Work• More Lexicons
• Investigate Lexicon-Centric scale out.
– Allows more lexicons.
– Not limited to single index.
• Move to Lucene, eliminate network overhead.
– Asynchronous model
– Use Kafka topic with multiple partitions
– Lucene based tagging consumers
– Write output to S3.
Q&A
Thank you for listening!• Questions?
• SoDA available on GitHub
– https://github.com/elsevierlabs-os/soda
• Contact me
