The document discusses building a near real-time log search engine and analytics using Solr, focusing on challenges like handling terabytes of unstructured logs and ensuring high indexing performance. It outlines improvements such as using an embedded Solr instance, asynchronous processing, and efficient commit strategies to optimize search and indexing. Additionally, it emphasizes lessons learned on scaling and performance tuning in distributed environments.

1. Building a Near Real time
“Logs Search Engine & Analytics”
using Solr
Lucene/Solr Revolution 2013
May 1st , 2013
Rahul Jain
jainr@ivycomptech.com

2. Who am I?
 Software Engineer
 Member of Core technology @ IVY Comptech,
Hyderabad, India
 6 years of programming experience
 Areas of expertise/interest
 High traffic web applications
 JAVA/J2EE
 Big data, NoSQL
 Information-Retrieval, Machine learning
2

3. Agenda
• Overview
• Indexing
• Search
• Analytics
• Architecture
• Lessons learned
• Q&A
3

4. Overview

5. Issues keep coming in “Production”
5
java.net.ConnectException:
Connection refused
ServerNotRunningException
Too many open files
DBException
NullPointerException
OutOfMemory
Issues
 Hidden Bugs
 DB is down
 Server crashed
 OutOfMemory
 Connection reset
 Nodes go out of cluster
(Due to long GC pause)
 Attack
 DOS (Denial of Service) by
sending a lot of requests
in a short time frame.
5

6. Why Logs Search?
• Enable production support team to immediately check for issues at
“one place”
– Saves time from logging on to multiple servers to check the logs
• Debugging production issues
– Is it a server specific or occurring in all other servers for that application?
• Allows to track user activity across multiple servers/applications.
• Correlation of multiple issues with each other.
– e.g. Logins might be failing on X Node due to OutOfMemory on Y node.
6

7. Key Problems
• Hundreds of servers/services generating logs
• terabytes of unstructured logs/day to index in Near Real time
• Millions of log events (Priority one)
• Full Text search & storage of log content
• High Indexing Rate of 1GB/min
• Search latency in seconds is acceptable
7

8. Logs are different
• varying size
– from few bytes to several KBs
– more no. of documents.
• average 6-8 million log messages in 1 GB logs
– Each line forms one log message except “exception stack trace”.
• different types
– exception stack-trace
– application logs
– http access/error logs
– gclog
• logging format is not uniform across all logs
8

9. Indexing

10. Improving Indexing Performance
10
 Solr in Embedded Mode
 Bypassing XML Marshalling/Unmarshalling
 Moving to an Async Approach
 Route traffic on Alternate Shard once “Commit” starts on Main Shard
 Other optimizations
 Add document does update (add + delete)
 Changing Buffer size in BufferIndexInput and BufferIndexOutput
 Reusing Lucene document object

11. Old Architecture
11
Solr Server
Centralized
Log Collection
Server
Solr Server
Search UIProduction
Server
Logs Transfer

12. Old Architecture
12
Solr Server
Centralized
Log Collection
Server
Solr Server
Search UIProduction
Server
Logs Transfer
Data Copy1 Data Copy2

13. Direct Logs transfer
Indexing ServerProduction
Server
Indexing Server
Indexing Server
Open question :
Since now Indexing system is exposed to production servers
- what if a new Indexing Server is added on the fly or one of them is down
13

14. Solr in Embedded Mode
14
Single JVM
Solrj
(EmbeddedSolrServer)
SolrApplication
Indexing Server
No network latency

15. Improving Indexing Performance
15
 Solr in Embedded Mode
 Bypassing XML Marshalling/Unmarshalling
 Moving to an Async Approach
 Route traffic on Alternate Shard once “Commit” starts on Main Shard
 Other optimizations
 Add document does update (add + delete)
 Changing Buffer size in BufferIndexInput and BufferIndexOutput
 Reusing Lucene document object

16. Message Flow
16
SolrInputDocument
SolrInputDocument
(new object)
Single JVM
XML
Marshalling
(UpdateRequest)
XML
Unmarshalling
(XMLLoader)
<add>
<doc>
<field> </field>
<field> </field>
<doc>
</add>
xml

17. Bypassing XML
Marshalling/Unmarshalling
17
SolrInputDocument
XML
Marshalling
(UpdateRequest)
XML
Unmarshalling
(XMLLoader)
SolrInputDocument
(referenced object)
Passing the Direct reference of
SolrInputDocument Object
Single JVM
DocContentStream
#getSolrInputDocuments()
RefDocumentLoader
#load()
DocUpdateRequest
#add(List<SolrInputDocument>)
LMEmbeddedSolrServer
#add(List<SolrInputDocument>)

18. Improving Indexing Performance
18
 Solr in Embedded Mode
 Bypassing XML Marshalling/Unmarshalling
 Moving to an Async Approach
 Route traffic on Alternate Shard once “Commit” starts on Main Shard
 Other optimizations
 Add document does update (add + delete)
 Changing Buffer size in BufferIndexInput and BufferIndexOutput
 Reusing Lucene document object

19. Old Architecture
(Sync)
19
Incoming
Message
Log
Event
Solr
unstructured structured
SolrInput
Document
(10K)
Thread Pool with
multiple threads
Once Batch size reaches to
10k, one of the thread adds
documents to Solr as a Sync
call and wait for response
add
UpdateResponse
Batch
Wait for response
Time taken :
- Indexing 1 chunk (10k) takes anywhere between 400ms-3000ms#
- while commit it is from 6000ms-23000ms and even more…
- In 1 GB there are around 600 chunks
- so most of time is just spent in waiting for response
#Indexing time vary based on several factors, for e.g. hardware configurations, application type, nature of data,
number of index fields/stored fields, analyzer type etc.

20. Moving to an Asynchronous
Architecture
20
Incoming
Message
Log Event
Event Pipeline
(BlockingQueue)
Log Event
SolrInput
Document
Log Message
Transformation
(Analyzer Thread Pool)
Log Event to
SolrInputDocument
(Indexer Thread Pool)
Add a Batch of Log
Event to Pipeline
Remove Batch of Log
Event from Pipeline
Solr
Add to
Batch
Remove
from Batch

21. Improving Indexing Performance
21
 Solr in Embedded Mode
 Bypassing XML Marshalling/Unmarshalling
 Moving to an Async Approach
 Route traffic on Alternate Shard once “Commit” starts on Main Shard
 Other optimizations
 Add document does update (add + delete)
 Changing Buffer size in BufferIndexInput and BufferIndexOutput
 Reusing Lucene document object

22. Commit Strategy
22
Solr
20130501_0
20130501_1
20130501_2
Shard
(Single Node)
SolrInputDocument
Indexing
Partition
function
22

23. Indexing traffic on alternate Shard
Once commit starts on “Main Shard”
23
Solr
20130501_0
20130501_1
20130501_2
Main Shard
(Single Node)
20130501_3
20130501_4
20130501_5
Alternate
Shard
SolrInputDocument
Indexing
PairPartition
function
23

24. Commit Strategy
• Merits
– Scales well
– Indexing can run continuously
• De-Merits
– Search needs to be done on both cores
– but end of the day these two can be merged into
one core
24

25. Improving Indexing Performance
25
 Solr in Embedded Mode
 Bypassing XML Marshalling/Unmarshalling
 Moving to an Async Approach
 Route traffic on Alternate Shard once “Commit” starts on Main Shard
 Other optimizations
 Add document does update (add + delete)
 Changing Buffer size in BufferIndexInput and BufferIndexOutput
 Reusing Lucene document object

26. Other Optimizations
• In Solr, Add document does update (add + delete)
– for each add document call, Solr internally creates a delete term with “id” field
for delete
– but log messages are always unique
• Changing Buffer Size in BufferIndexInput and BufferIndexOutput
– Increasing buffer size improves the indexing performance especially if disk is
slow.
– More Process heap is required accordingly as lot of files are created if data
volume is high.
• Reusing Lucene document and Field instances
- Check org/apache/lucene/benchmark/byTask/feeds/DocMaker.java
• Check for more information on Improving Indexing performance
http://rahuldausa.wordpress.com/2013/01/14/scaling-lucene-for-indexing-a-billion-documents/
26

27. The Result
27

28. Data Volume v/s Indexing time
(GB/Minutes)
3
14
38
56
112
0.5 2 4.5
9
22
0
20
40
60
80
100
120
1GB 4GB 8GB 17GB 35GB
IndexingTime
Before
After
28

29. Search

30. Partition
• Partitioning the data properly improves the Search performance significantly
• Partition Type
– Server based Partition
• Number of documents does not balance out evenly in all shards
– Date and Time based Partition
• Hotspot a single shard
– Least loaded Shard (index)
• By number of documents
• Balances out documents evenly in all shards
• Can’t provide optimal search performance, as all shards needs to be hit
30
Incoming
message
Server Based
Partition
Date & time
Based
Partition
Solr Shard
Hybrid Approach

31. Multi-tier Partition
jacob
Incoming
Message
Date & time
based
Partition
20130501_00_0
mia
Solr Shard
(date_hour_shardId)
20130501_06_0
20130501_00_1
Server based
Partition
Jacob: {
message: hello lucene
time:20130501:11:00:00
}
Indexing ServerProduction Server
mia: {
message: hello solr and lucene
time:20130501:04:00:00
}
31

32. Distributed Search
• One shard is chosen as leader shard
– Forwards request to all other shards and collects response.
• Requires all documents to must have
– Unique key (e.g. “id”) across all shards and should be stored
– Used a approach based on epoch to generate a unique id across cluster
inspired from instagram engineering blog#
• Unique Id
– Combination of epoch time, unique node id and an incremented number
epoch_time unique_node_id incremented_number
Unique Id
#http://instagram-engineering.tumblr.com/post/10853187575/sharding-ids-at-instagram
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33. How Search works
Zookeeper
(zk)
Search Server
(tomcat)
Pushes shard
mapping to zk
Create a watcher on zk node
and update the In-Memory
shard mapping on change
User query QueryParser Indexing Server
(maestro)
Search query with
shards parameter
Shard Mapping
(In Memory structure)
Lookup
33
erverIndexing
Server

34. How Search works (Cont’d)
from: now-24hour
server: jacob from: now-4hour
from: now-11hour
Indexing
server
Indexing
server
Indexing
server
Lookup on shards for today
shard(s) having data for jacob
from last 6hour shard
shard(s) having data for last
12 hours
34
Leader shard
(maestro)

35. Analytics
• Young GC timings/chart
• Full GC timings
• DB Access/Update Timings
– Reveal is there any pattern across all DB servers?
• Real time Exceptions/Issues reporting using
facet query.
• Apache Access/Error KPI
35

36. Analytics
• Custom report based on “Key:Value” Pair
For e.g.
time – key:value
18:28:28, 541 - activeThreadCount:5
18:28:29, 541- activeThreadCount:8
18:28:30, 541 - activeThreadCount:9
18:28:31, 541- activeThreadCount:3
36
0
2
4
6
8
10
activeThreadCount

37. Architecture

38. Data Flows
38
Weird
Log File
Zero Copy
server
Kafka Broker
Indexing
Server
Search UI
Periodic Push
Real time transfer
from In Memory
(Log4jAppender)
o Zero Copy server
- Deployed on each Indexing server for data locality
- Write incoming files to disk as Indexing server doesn’t index with same rate
o Kafka Broker
o Kafka Appender pass the messages from in-Memory

39. Periodic Push
39
Zookeeper
Indexing
Server
Zero copy
server
Node 1
Production
Server
Logs transfer
Daemon
Disk
Node…n
.
.
.

40. Real time transfer
40
Indexing
Server
Kafka
Broker
Indexing
Server
Search UI
Production
Server
(Kafka Appender)
Zookeeper
Indexing
Server
Indexing
Server
Update Consumed
Message offset

41. Conclusion
Lessons Learned
• Always find sweet-spots for
– Number of Indexer threads, that can run in parallel
– Randomize
• Merge factor
• Commit Interval
• ramBufferSize
– Increasing Cache Size helps in bringing down search latency
• but with Full GC penalty
• Index size of more than 5GB in one core does not go well with Search
• Search on a lot of cores does not provide optimal response time
– Overall query response time is limited by slowest shard’s performance
• Solr scales both vertically and horizontally
• Batching of log messages based on message size (~10KB) in a MessageSet
– Kafka adds 10 bytes on each message
– Most of the time Log messages are < 100 bytes
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42. Thank You
jainr@ivycomptech.com
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