Presented by Adrien Grand, Software Engineer, Elasticsearch Although people usually come to Lucene and related solutions in order to make data searchable, they often realize that it can do much more for them. Indeed, its ability to handle high loads of complex queries make Lucene a perfect fit for analytics applications and, for some use-cases, even a credible replacement for a primary data-store. It is important to understand the design decisions behind Lucene in order to better understand the problems it can solve and the problems it cannot solve. This talk will explain the design decisions behind Lucene, give insights into how Lucene stores data on disk and how it differs from traditional databases. Finally, there will be highlights of recent and future changes in Lucene index file formats.

2. WHAT IS IN A LUCENE INDEX
Adrien Grand
@jpountz
Software engineer at Elasticsearch

3. About me
•
•
Lucene/Solr committer
Software engineer at Elasticsearch
•
I like changing the index file formats!
– stored fields
– term vectors
– doc values
– ...

4. Why should I
learn about
Lucene internals?

5. Why should I learn about Lucene internals?
•
Know the cost of the APIs
– to build blazing fast search applications
– don’t commit all the time
– when to use stored fields vs. doc values
– maybe Lucene is not the right tool
•
Understand index size
– oh, term vectors are 1/2 of the index size!
– I removed 20% of my documents and index size hasn’t changed
•
This is a lot of fun!

6. Indexing
•
Make data fast to search
– duplicate data if it helps
– decide on how to index based on the queries
•
Trade update speed for search speed
– Grep vs full-text indexing
– Prefix queries vs edge n-grams
– Phrase queries vs shingles
•
Indexing is fast
– 220 GB/hour for 4K docs!
– http://people.apache.org/~mikemccand/lucenebench/indexing.html

7. Let’s create an index
•
Tree structure
– sorted for range queries
– O(log(n)) search
sql
index
data
term
Lucene
Lucene in action
Databases

8. Lucene doesn’t
work this way

9. Another index
•
Store terms and documents in arrays
– binary search
0
data
0,1
1
index
0,1
2
Lucene
0
3
term
0
4
sql
1
0
Lucene in action
1
Databases

10. Another index
•
Store terms and documents in arrays
– binary search
0
0,1
1
Segment
data
index
0,1
2
Lucene
0
3
term
0
4
sql
1
term
ordinal
terms
dict
postings
list
0
Lucene in action
1
Databases
doc id
document

11. Insertions?
•
•
Insertion = write a new segment
Merge segments when there are too many of them
– concatenate docs, merge terms dicts and postings lists (merge sort!)
0
data
0
1
index
0
2
Lucene
0
term
0
0
data
0
1
index
0
2
sql
0
0
Databases
1
index
0,1
Lucene
0
term
0
4
Lucene in action
0,1
2
0
data
3
3
0
sql
1
0
Lucene in action
1
Databases

12. Insertions?
•
•
Insertion = write a new segment
Merge segments when there are too many of them
– concatenate docs, merge terms dicts and postings lists (merge sort!)
0
data
0
1
index
0
2
Lucene
0
term
0
0
data
1
1
index
1
2
sql
1
1
Databases
1
index
0,1
Lucene
0
term
0
4
Lucene in action
0,1
2
0
data
3
3
0
sql
1
0
Lucene in action
1
Databases

13. Deletions?
•
•
•
Deletion = turn a bit off
Ignore deleted documents when searching and merging (reclaims space)
Merge policies favor segments with many deletions
0
data
0,1
1
index
0,1
2
Lucene
0
3
term
0
4
sql
1
0
Lucene in action
1
1
Databases
0
live docs: 1 = live, 0 = deleted

14. Pros/cons
•
•
•
•
•
Updates require writing a new segment
– single-doc updates are costly, bulk updates preferred
– writes are sequential
Segments are never modified in place
– filesystem-cache-friendly
– lock-free!
Terms are deduplicated
– saves space for high-freq terms
Docs are uniquely identified by an ord
– useful for cross-API communication
– Lucene can use several indexes in a single query
Terms are uniquely identified by an ord
– important for sorting: compare longs, not strings
– important for faceting (more on this later)

15. Lucene can use
several indexes
Many databases can’t

16. Index intersection
1
red
shoe
2
4
6
7
9
1, 2, 10, 11, 20, 30, 50, 100
2, 20, 21, 22, 30, 40, 100
3
5
8
Lucene’s postings lists support skipping that
can be use to “leap-frog”
Many databases just pick the most selective
index and ignore the other ones

17. What else?
•
•
We just covered search
Lucene does more
– term vectors
– norms
– numeric doc values
– binary doc values
– sorted doc values
– sorted set doc values

18. Term vectors
•
•
•
Per-document inverted index
Useful for more-like-this
Sometimes used for highlighting
0
Lucene in action
0
data
0
0
data
0,1
1
index
0
1
index
0,1
2
Lucene
0
2
Lucene
0
3
term
0
3
term
0
0
data
0
4
sql
1
1
index
0
2
sql
0
1
Databases

19. Numeric/binary doc values
•
•
•
Per doc and per field single numeric values, stored in a column-stride fashion
Useful for sorting and custom scoring
Norms are numeric doc values
field_a field_b
0
Lucene in action
42
afc
1
Databases
1
gce
2
Solr in action
3
ppy
3
Java
10
ccn

20. Sorted (set) doc values
•
Ordinal-enabled per-doc and per-field values
– sorted: single-valued, useful for sorting
– sorted set: multi-valued, useful for faceting
0
Lucene in action
1,2
0
distributed
1
Databases
0
1
Java
2
Solr in action
0,1,2
2
search
3
Java
1
Ordinals
Terms dictionary for
this dv field

21. Faceting
•
Compute value counts for docs that match a query
– eg. category counts on an ecommerce website
•
Naive solution
– hash table: value to count
– O(#docs) ordinal lookups
– O(#doc) value lookups
•
2nd solution
– hash table: ord to count
– resolve values in the end
– O(#docs) ordinal lookups
– O(#values) value lookups
Since ordinals are dense,
this can be a simple array

22. How can I use these APIs?
•
These are the low-level Lucene APIs, everything is built on top of these APIs:
searching, faceting, scoring, highlighting, etc.
API
Useful for
Method
Inverted index
Term -> doc ids, positions,
offsets
AtomicReader.fields
Stored fields
Summaries of search results
IndexReader.document
Live docs
Ignoring deleted docs
AtomicReader.liveDocs
Term vectors
More like this
IndexReader.termVectors
Doc values / Norms
Sorting/faceting/scoring
AtomicReader.get*Values

23. Wrap up
•
•
Data duplicated up to 4 times
– not a waste of space!
– easy to manage thanks to immutability
Stored fields vs doc values
– Optimized for different access patterns
– get many field values for a few docs: stored fields
– get a few field values for many docs: doc values
Stored fields
0,A
0,B
0,C
Doc values
0,A
1,A
2,A
0,B
1,B
2,B
0,B
1,B
2,B
1,A
1,B
1,C
2,A
2,B
2,C
At most 1 seek per doc
At most 1 seek per doc per field
BUT more disk / file-system cache-friendly

24. File formats

25. Important rules
•
Save file handles
– don’t use one file per field or per doc
•
Avoid disk seeks whenever possible
– disk seek on spinning disk is ~10 ms
•
BUT don’t ignore the filesystem cache
– random access in small files is fine
•
Light compression helps
– less I/O
– smaller indexes
– filesystem-cache-friendly

26. Codecs
•
File formats are codec-dependent
•
Default codec tries to get the best speed for little memory
– To trade memory for speed, don’t use RAMDirectory:
– MemoryPostingsFormat, MemoryDocValuesFormat, etc.
•
Detailed file formats available in javadocs
– http://lucene.apache.org/core/4_5_1/core/org/apache/lucene/codecs/packagesummary.html
–

27. Compression techniques
•
Bit packing / vInt encoding
– postings lists
– numeric doc values
•
LZ4
– code.google.com/p/lz4
– lightweight compression algorithm
– stored fields, term vectors
•
FSTs
– conceptually a Map<String, ?>
– keys share prefixes and suffixes
– terms index

28. What happens
when I run a
TermQuery?

29. 1. Terms index
•
Lookup the term in the terms index
– In-memory FST storing terms prefixes
– Gives the offset to look at in the terms dictionary
– Can fast-fail if no terms have this prefix
r
b/2
l/4
a/1
c
u
y/3
r
br = 2
brac = 3
luc = 4
lyr = 7

30. 2. Terms dictionary
•
•
Jump to the given offset in the terms dictionary
– compressed based on shared prefixes, similarly to a burst trie
– called the “BlockTree terms dict”
read sequentially until the term is found
–
Jump here
Not found
Not found
Found
[prefix=luc]
a, freq=1, offset=101
as, freq=1, offset=149
ene, freq=9, offset=205
ky, frea=7, offset=260
rative, freq=5, offset=323

31. 3. Postings lists
•
•
Jump to the given offset in the postings lists
Encoded using modified FOR (Frame of Reference) delta
– 1. delta-encode
– 2. split into block of N=128 values
– 3. bit packing per block
– 4. if remaining docs, encode with vInt
Example with N=4
1,3,4,6,8,20,22,26,30,31
1,2,1,2,2,12,2,4,4,1
[1,2,1,2] [2,12,2,4] 4, 1
2 bits per value
vInt-encoded
4 bits per value

32. 4. Stored fields
•
•
In-memory index for a subset of the doc ids
– memory-efficient thanks to monotonic compression
– searched using binary search
Stored fields
– stored sequentially
– compressed (LZ4) in 16+KB blocks
docId=3
offset=127
docId=0
offset=42
0
1
16KB
2
docId=4
offset=199
3
16KB
4
5
16KB
6

33. Query execution
•
•
2 disk seeks per field for search
1 disk seek per doc for stored fields
•
It is common that the terms dict / postings lists fits into the file-system cache
•
“Pulse” optimization
– For unique terms (freq=1), postings are inlined in the terms dict
– Only 1 disk seek
– Will always be used for your primary keys

34. Quizz

35. What is happening here?
qps
1
2
#docs in the index

36. What is happening here?
qps
1
Index grows larger than the filesystem
cache: stored fields not fully in the cache
anymore
2
#docs in the index

37. What is happening here?
qps
1
Index grows larger than the filesystem
cache: stored fields not fully in the cache
anymore
2 Terms dict/Postings lists not fully in the
cache
#docs in the index

38. Thank you!