Full-text search with distributed search engines Alexander Reelsen @spinscale alex@elastic.co
A presentation at HYF Copenhagen in July 2020 in by Alexander Reelsen
Full-text search with distributed search engines Alexander Reelsen @spinscale alex@elastic.co
Agenda • Overview • Indexing: Analysis, Tokenization, Filtering, on disk data structures • Searching: Scoring, Algorithms & Optimization • Aggregations • Distributed systems and search •Q&A
Overview Full text search introduction
Why is search so important?
SELECT * FROM products WHERE name LIKE = ‘%topf%’
Problem • Scales linearly with the data set size • Relevancy • Spell correction • Synonyms • Phrases
Inverted Index
The quick brown fox jumped over the lazy dog
Tokenize The quick brown fox jumped over the lazy dog 1 1 1 1 1 1 1 1 1
Sort The brown dog fox jumped lazy over quick the 1 1 1 1 1 1 1 1 1
Quick brown foxes leap over lazy dogs in summer
Quick The brown dog dogs fox foxes in jumped lazy leap over quick summer the 2 1 1,2 1 2 1 2 2 1 1,2 2 1,2 1 2 1
Quick The brown dog dogs fox foxes in jumped lazy leap over quick summer the 2 1 1,2 1 2 1 2 2 1 1,2 2 1,2 1 2 1 lazy dog
Quick The brown dog dogs fox foxes in jumped lazy leap over quick summer the 2 1 1,2 1 2 1 2 2 1 1,2 2 1,2 1 2 1 lazy AND dog [1,2] AND [1] = [1]
Quick The brown dog dogs fox foxes in jumped lazy leap over quick summer the 2 1 1,2 1 2 1 2 2 1 1,2 2 1,2 1 2 1 lazy OR dog [1,2] OR [1] = [1,2]
Technologies used today • Apache Lucene (search library) • Elasticsearch (distributed search engine built on top of Apache Lucene)
Indexing Analysis, Tokenization, Filtering Data structures
Quick The brown dog dogs fox foxes in jumped lazy leap over quick summer the 2 1 1,2 1 2 1 2 2 1 1,2 2 1,2 1 2 1 quick 1 hit
Quick The brown dog dogs fox foxes in jumped lazy leap over quick summer the 2 1 1,2 1 2 1 2 2 1 1,2 2 1,2 1 2 1 quicK 0 hits
Analysis: Tokenizer & Token Filters
Tokenization
Tokenization quick brown fox
Tokenization quick_brown_fox
Tokenization quick_brown_fox the lazy, white dog.
Tokenization quick_brown_fox the_lazy_white_dog
Tokenization quick_brown_fox the_lazy_white_dog https://unicode.org/reports/tr29/
Tokenization quick_brown_fox the_lazy_white_dog https://www.jade-hs.de
Tokenization quick_brown_fox the_lazy_white_dog https_www.jade_hs.de
Token Filter
Quick The brown dog dogs fox foxes in jumped lazy leap over quick summer the 2 1 1,2 1 2 1 2 2 1 1,2 2 1,2 1 2 1
Token filter The Quick brown fox
Token filter Lowercase The Quick brown fox the quick brown fox
Token filter The Quick brown fox Lowercase Stopwords the quick brown fox quick brown fox
Token filter The Quick brown fox Lowercase Stopwords the quick brown fox quick brown fox Synonyms quick,fast brown fox
Token filter The Quick brown fox Lowercase Stopwords the quick brown fox quick brown fox Synonyms quick,fast brown fox Tokens can be changed, added, removed
Token filter The Quick brown fox Lowercase Stopwords the quick brown fox quick brown fox Synonyms quick,fast brown fox Queries need to be processed as well!
More analysis strategies • Phonetic analysis: Meyer vs. Meier • Stemming: foxes ⇾ fox • Compounding: Blumentopf ⇾ blumen topf • Folding: Spaß ⇾ Spass
(On-Disk) Data structures
What else is in an inverted index? • Documents: Find documents • Term frequencies: Relevancy • Positions: Positional Queries • Offsets: Highlighting • Stored fields: The original data
Segment: Unit of work • A fully self sufficient inverted index • An index consists of a number of segments • New segments are created for newly added documents • Segments are immutable!
Read-only data structures • Pro: Write-once, sequentially • Pro: Lock-free reading • Pro: File system cache • Contra: in-place updates & deletes • Contra: Housekeeping • Contra: Transactions
Segment: Deletes • Mark a document as deleted in a special file • Exclude it from searches • No space is freed! 1|2|3|4|5 3 6|7|8
Segment: Merging • Number of segments needs to be kept reasonable • Merge multiple segments into one (smaller index) • Delete expired documents 1|2|3|4|5 3 6|7|8
Segment: Merging • Number of segments needs to be kept reasonable • Merge multiple segments into one (smaller index) • Delete expired documents 1|2|3|4|5 3 6|7|8 1|2|4|5|6|7|8
Searching Precision vs. recall Scoring Algorithms and optimizations
Relevancy
Relevancy • Textbook answer: How well matches a document a query? • Business answer: Are the top search results those that make me the most money? • marketplace • hotel booking website • newspaper website
Scoring
Scoring: lazy dog • Naive: increase a counter if a term is matched • “the lazy dog” => score 2 • “the lazy frog” => score 1 • “the lazy lazy lazy lazy cat” => score 4 or 1?
Scoring: More than term frequency • How about incorporating information about the whole document corpus in scoring? • Are lesser common terms more relevant? • news paper: “dieselgate news”
Scoring: TF-IDF • Term frequency: number of times a term occurs in a field • Inverse document frequency: inverse function of the number of documents in which it occurs
Scoring: Vector space model • Each term is a dimension • The length is based on tf-idf calculation • Similarity is the angle between vectors • Cosine similarity: best match == angle 0°
Scoring: TF-IDF in Lucene score(q,d) = ∑ ( tf(t in d) · 2 idf(t) · t.getBoost() · norm(t,d) )
BM25 • Default in Apache Lucene/Elasticsearch • Works better with stopwords (high TF) • Term frequency saturation • Improved field length normalization (per document)
BM25 https://www.elastic.co/guide/en/elasticsearch/guide/2.x/pluggable-similarites.html
Precision vs. recall
Precision and Recall
Precision and Recall relevant documents irerelevant documents
Precision and Recall relevant documents irerelevant documents
True positives relevant documents irerelevant documents
True negatives relevant documents irerelevant documents
False positives relevant documents irerelevant documents
False negatives relevant documents irerelevant documents
Precision and recall • Precision: How many selected documents are relevant? • Recall: How many relevant documents are selected
Under the hood
Optimizations everywhere • leap frogging, skip lists • top-k • two phase iterations • integer compression
Query two phase iteration
Two phase iteration: Phrase query • Phrase query: “quick fox” • Approximation phase: document contains terms quick and fox • Verification phase: read positions of terms
Two phase iteration: Geo distance query • Geo distance query: Distance from reference point • Approximation phase: bbox around point • Verification phase: exact distance calculation
Two phase iteration: Geo distance query GET /my_locations/_search { “query”: { “bool” : { “filter” : { “geo_distance” : { “distance” : “200km”, “pin.location” : { “lat” : 40, “lon” : -70 } } } } } }
Two phase iteration: several queries • Powerful when several queries are used • “quick fox” AND brown • Approximation: quick AND fox AND brown • Verification: “quick fox” position check for hits
Skip lists & leap frogging
Skip lists • Term dictionary is a sorted skip list • Skip list is a linked list with ‘express lanes’ to leap forward https://en.wikipedia.org/wiki/Skip_list
Leap frogging elasticsearch AND kibana AND logstash
Leap frogging elasticsearch AND kibana AND logstash 266 102 568 98 302 266 60 102 199 18 59 150 5 5 102 1 3 5
Leap frogging logstash AND kibana AND elasticsearch 266 568 102 266 302 98 199 102 60 150 59 18 102 5 5 5 3 1
Leap frogging logstash AND kibana AND elasticsearch 266 568 102 266 302 98 199 102 60 150 59 18 102 5 5 5 3 1
Leap frogging logstash AND kibana AND elasticsearch 266 568 102 266 302 98 199 102 60 150 59 18 102 5 5 5 3 1
Leap frogging logstash AND kibana AND elasticsearch 266 568 102 266 302 98 199 102 60 150 59 18 102 5 5 5 3 1
Leap frogging logstash AND kibana AND elasticsearch 266 568 102 266 302 98 199 102 60 150 59 18 102 5 5 5 3 1
Leap frogging logstash AND kibana AND elasticsearch 266 568 102 266 302 98 199 102 60 150 59 18 102 5 5 5 3 1
Leap frogging logstash AND kibana AND elasticsearch 266 568 102 266 302 98 199 102 60 150 59 18 102 5 5 5 3 1 Hit!
Leap frogging logstash AND kibana AND elasticsearch 266 568 102 266 302 98 199 102 60 150 59 18 102 5 5 5 3 1 Hit!
Leap frogging logstash AND kibana AND elasticsearch 266 568 102 266 302 98 199 102 60 150 59 18 102 5 5 5 3 1 Hit!
Leap frogging logstash AND kibana AND elasticsearch 266 568 102 266 302 98 199 102 60 150 59 18 102 5 5 5 3 1 Hit!
Leap frogging logstash AND kibana AND elasticsearch 266 568 102 266 302 98 199 102 60 150 59 18 102 5 5 5 3 1 Hit! Hit!
Leap frogging logstash AND kibana AND elasticsearch 266 568 102 266 302 98 199 102 60 150 59 18 102 5 5 5 3 1 Hit! Hit!
Leap frogging logstash AND kibana AND elasticsearch 266 568 102 266 302 98 199 102 60 150 59 18 102 5 5 5 3 1 Hit! Hit!
Leap frogging logstash AND kibana AND elasticsearch 266 568 102 266 302 98 199 102 60 150 59 18 102 5 5 5 3 1 Hit! Hit!
Leap frogging logstash AND kibana AND elasticsearch 266 Done! 568 102 266 302 98 199 102 60 150 59 18 102 5 5 5 3 1 Hit! Hit!
Top-k retrieval
Top-k retrieval • elasticsearch OR kibana • top 10 results wanted • maximum score for kibana is 3.0 • maximum score for elasticsearch is 5.0 • collecting documents: when 10th hit has score > 3, then only documents with elasticsearch need to be collected • total hit count is not accurate
Top-k retrieval
Index sorting
Order index by field values • each segment is sorted before write • criteria can be chosen by the user 5|2|3|1|4 retrieve sort top 2 5|2|3|1|4 5|4|3|2|1 5|4
Order index by field values • each segment is sorted before write • criteria can be chosen by the user early termination 5|4|3|2|1 5|4
Aggregations Reducing data
Aggregations
aggregations documents
aggregations documents
Bucketing documents Pumps Sneakers Oxfords Sneakers Boots
Bucketing documents Sneakers Pumps Boots Oxfords
Bucketing documents Sneakers Pumps 2 bucket agg 1 metric agg doc_count Boots Oxfords 1 1
Bucketing documents Sneakers Pumps 50 bucket agg 95 metric agg avg price Boots Oxfords 90 23
Aggregations • bucket: terms, histogram, geo, range, sampler , significant text, nested • metric: value_count, avg, min, max, sum, stats, median deviation, geo, percentile, cardinality, • pipeline: min, max, sum, avg, derivative, stats, percentiles, cumulative sum, moving average, moving function, serial differencing
Distributed systems & search Fanning out a search, reducing the results
Elasticsearch
Elasticsearch in 10 seconds • Search Engine (FTS, Analytics, Geo), near real-time • Distributed, scalable, highly available, resilient • Interface: HTTP & JSON • Centrepiece of the Elastic Stack • Uneducated conservative guess: Tens of thousands of clusters worldwide, hundreds of thousands of instances
Distributed systems
Distributed systems • How do nodes communicate with each other? • Who is taking and executing decisions? • Failure detection? • Replication strategy? • Consistency? • Enter consensus algorithms…
A fundamental problem in distributed computing and multi-agent systems is to achieve overall system reliability in the presence of a number of faulty processes. This often requires processes to agree on some data value that is needed during computation https://en.wikipedia.org/wiki/Consensus_(computer_science) 122
Consensus algorithms • Leader based: Paxos, Raft • Non leader based: BTC, gossip
Consensus in Elasticsearch • Custom consensus algorithm, improving the existing one • Formally verified • Optimized for Elasticsearch use-case (rolling restarts, growing/shrinking clusters, log-ofoperations vs. cluster state)
Consensus in Elasticsearch node 1 node 2 node 3 node 4
Consensus in Elasticsearch node 1 node 2 node 3 node 4
Consensus in Elasticsearch node 1 node 2 node 3 node 4
Consensus in Elasticsearch node 1 node 2 node 3 node 4
Master node tasks • Deciding where data should be stored • Pinging other nodes • Reacting on node leaves/joins • Updating cluster state • Distributing cluster state
Consensus in Elasticsearch node 1 cs1 node 2 cs1 node 3 cs1 node 4 cs1
Consensus in Elasticsearch node 1 cs1 node 2 cs1 node 3 cs1 node 4 cs1
Consensus in Elasticsearch node 1 cs1 node 2 cs1 node 3 cs2 node 4 cs1
Consensus in Elasticsearch node 1 cs1 node 2 cs1 node 3 cs2 node 4 cs1
Consensus in Elasticsearch node 1 cs1 node 2 cs2 node 3 cs2 node 4 cs2
Distributed search
Distributed search in Elasticsearch node 1 node 2 node 3 p0 Primary Shard (Lucene Index) node 4
Distributed search in Elasticsearch node 1 p0 node 2 r0 Replica shard (copy) node 3 node 4
Distributed search in Elasticsearch node 1 p0 node 2 r0 node 3 node 4 p1 r1 2nd Primary Shard Replica shard (copy)
Distributed search in Elasticsearch • Shard: Lucene index, unit of scale • Primary shard: Write scalability • Replica shard: Read scalability, availability
Distributed search in Elasticsearch node 1 p0 node 2 r0 node 3 node 4 p1 r1
Distributed search in Elasticsearch 2. Execute query against shards node 1 p0 node 2 r0 node 3 node 4 p1 r1
Distributed search in Elasticsearch 3. top-k search results are returned to coordinating node node 1 p0 node 2 r0 node 3 node 4 p1 r1
Distributed search in Elasticsearch 4. Create real top-k result list node 1 p0 node 2 r0 node 3 node 4 p1 r1
Distributed search in Elasticsearch 5. Fetch original documents node 1 p0 node 2 r0 node 3 node 4 p1 r1
Distributed search in Elasticsearch 5. Fetch original documents node 1 p0 node 2 r0 node 3 node 4 p1 r1 6. Return data to the client
Aggregations
Aggregations - cardinality node 1 p0 node 2 p1 POST /sales/_search?size=0 { “aggs” : { “type_count” : { “cardinality” : { “field” : “type” } } } }
Aggregations - cardinality node 1 p0 25 node 2 p1 40 How many distinct elements are in my index? What is the total? 40? 65? Naive solution: merge data to single dataset and count. Doesn’t scale! Solution: Use HyperLogLog++
HyperLogLog++ • Hash based counting • Trades in memory for accuracy • Fixed memory usage, based on configurable precision • Result: Small mergeable data structure, can easily be sent over the network
Aggregations - percentile node 1 p0 node 2 p1 GET latency/_search { “size”: 0, “aggs” : { “load_time_outlier” : { “percentiles” : { “field” : “load_time” } } } }
T-Digest • Extreme percentiles are more accurate than the Median • Percentiles are divided into buckets • When buckets grow over a boundary, approximation kicks in, saving memory in the process • The exact level of inaccuracy is difficult to generalize • Alternative: HDR histograms
Probabilistic data structures • bloom/cuckoo/quotient filters (membership check) • HyperLogLog++ (cardinality) • T-Digest, DDSketch, HDR histogram (percentile) • Count-Min sketch (frequency, top-k) • Hashing (similarity)
Demo Try it out yourself! https://ela.st/jade-hochschule-samples
Elastic Cloud Free 30 day trial https://ela.st/hack-your-future-2020
Upcoming trends & summary … or why you should take a closer look at search
Search is not just google… • “Just google it” does not cut it • Enterprise search: Intranet/G-Drive/Dropbox • Ecommerce search • SIEM • Observability: Logging, APM & Metrics
Search is not ‘done’ • Constant improvement • Data structures & algorithms (BKD tree for geo shapes) • Academic research moves to industry thanks to Apache Lucene
Search is still tough • Language specific analysis • Smart query parsing (nike red hoodie xl) • Geo based search • Anomaly detection • Incoporating feedback loops
Upcoming trends • Learning-to-Rank • Deep Learning • Feedback loop
Summary • Everything is a search problem! • Search is hard… and interesting • Distributed systems are hard… and interesting • Domain knowledge required • Data keeps exploding, good job chances!
Literature Books, books, books
Resources Links, links, links
Links https://lucene.apache.org/core/8_2_0/core/org/apache/lucene/search/similarities/ TFIDFSimilarity.html https://www.elastic.co/blog/whats-new-in-lucene-8 https://www.elastic.co/blog/faster-retrieval-of-top-hits-in-elasticsearch-with-block-max-wand https://speakerdeck.com/elastic/amusing-algorithms-and-data-structures https://www.elastic.co/blog/index-sorting-elasticsearch-6-0 https://raft.github.io/ https://github.com/elastic/elasticsearch-formal-models https://gist.github.com/spinscale/b62c8b357fae7db3f14b7d3127758951
Links - probabilistic data structures https://github.com/addthis/stream-lib https://github.com/DataDog/sketches-java https://github.com/HdrHistogram/HdrHistogram https://github.com/JohnStarich/java-skip-list https://github.com/addthis/stream-lib https://static.googleusercontent.com/media/research.google.com/fr/ pubs/archive/40671.pdf
Q&A Alexander Reelsen alex@elastic.co @spinscale
