Building a Large Scale SEO/SEM

Application with Apache SolrRahul Jain

Freelance Big-data/Search Consultant

@rahuldausa

dynamicrahul2020@gmail.com

About Me…

• Freelance Big-data/Search Consultant based out of Hyderabad, India

• Provide Consulting services and solutions for Solr, Elasticsearch and other Big

data solutions (Apache Hadoop and Spark)

• Organizer of two Meetup groups in Hyderabad

• Hyderabad Apache Solr/Lucene

• Big Data Hyderabad

What I am going to talk

Share our experience in working on Search in this application …

• What all issues we have faced and Lessons learned

• How we do Database Import, Batch Indexing…

• Techniques to Scale and improve Search latency

• The System Architecture

• Some tips for tuning Solr

• Q/A

What does the Application do Keyword Research and Competitor Analysis Tool for SEO (Search Engine Optimization) and SEM

(Search Engine Marketing) Professionals

End user search for a keyword or a domain, and get all insights about that.

Aggregate data for the top 50 results of Google and Bing across 3 countries for 80million+ keywords.

Provide key metrics like keywords, CPM (Cost per mille), CPC (Cost per click), competitor’s details etc.

Web crawling

Data Processing & Aggregation

Ad Networks

Apis Databases

Data Collection

*All trademarks and logos belong to their respective owners.

Technology Stack

High level Architecture

Load Balancer (HAProxy)

Managed Cache

Apache Solr

Cache Cluster (Redis)

Apache Solr

Internet

Database (MySQL)

App Server (Tomcat)

Apache Solr

Search Head

Web Server FarmPhp App(Nginx)

Cluster Manager(Custom using

Zookeeper)

Search Head : • Is a Solr Server which does not contain any data.• Make a Distributed Search request and aggregate the Search Results• Also works as a Load Balancer for search queries.

Apache Solr

Search Head (Solr)

21 3

4

8

5

6

7

Ids lookup

Cache

Fetch cluster Mapping for which month’ cluster

Search - Key challenges

After processing we have ~40 billion records every month in MySQL database

including

80+ Million Keywords

110+ Million Domains

1billion+ URLs

Multiple keywords for a Single URL and vice-versa

Multiple tables with varying size from 50million to 12billion

Search is a core functionality, so all records (selected fields) must be Indexed in Solr

Page load time (including all 24 widgets, Max) < 1.5 sec (Critical)

But… we need to load this data only once every month for all countries, so we can

do Batch Indexing and as this data never changes, we can apply caching.

Making Data Import and Batch Indexing Faster

Data Import from MySQL to Solr• Solr’s DataImportHanlder is awesome but quickly become pretty slow for large volume

• We wrote our Custom Data Importer that can read(pull) documents from Database and pushses (Async) these into

Solr.

Data Importer(Custom)

Solr

Solr

Solr

Table

ID (Primary/Unique Key with Index)

Columns

1 Record1

2 Record2

…………

5000 Record 5000

*6000 Record 6000

-------

n… Record n…

Importer batches these database Batches into a Bigger Batch (10k documents) and Flushes to selected Solr servers Asynchronously in a round robin fashion

Rather than using “limit” function of Database, it queries by Range of IDs (Primary Key). Importer Fetches 10

batches at a time from MySQL database, each having 2k Records.

Each call is Stateless.

Downside: • We must need to have a primary key and that can be slow while creating it in database.• This approach required more number of calls, if the IDs are not sequential.

“select * from table t where ID=1 to

ID<=2000″

“select * from table t where ID=2001

to ID<=4000″

………

*Non-sequential Id

Batch Indexing

Indexing All tables into a Single Big Index

• All tables in same Index, distributed on multiple Solr cores

and Solr servers (Java processes)

• Commit on every 120million records or in every 15

minutes whichever is earlier

• Disabled Soft-commit and updates (overwrite=false), as

each call to addDocument calls updateDocument under

the hood

• But still.. Indexing was slow (due to being sequential for all

tables) and we need to stop it after 2 days.

• Search was also awfully slow (order of Minutes)

From cache, after warm-up

Bunch of shards ~100

Creating a Distributed Index for each table

How many shards ?

• Each table have varying number of records from 50million to

12billion

• If we choose 100million per shard (core), it means for 12billion, we

need to query 120 shards, awfully slow.

• Other side If we choose 500million/shard, a table with 500million

records will have only 1 shard, high latency, high memory usage

(Heap) and no distributed search*.

• Hybrid Approach : Determine number of shards based on max

number of records in table.

• Did a benchmarking to find the best sweet spot for max documents

(records) per shard with most optimal Search latency

• Commit at the end for each table.

Records/Max Shards Table

Max Number ofRecords in table

Max number of Shards (cores)

Allowed

<100 million 1

100-300million 2

<500 million 4

< 1 billion 6

1-5 billion 8

>5 billion 16

* Distributed Search improves latency but may not be faster always as search latency is limited by time taken by last shard in responding.

It worked fine but one day suddenly….

java.lang.OutOfMemoryError:

Java heap Space

• All Solr servers were crashed.

• We restarted but they keep crashing randomly after every other day

• Took a Heap dump and realized that it is due to Field Cache

• Found a Solution : Doc values and never had this issue again till date.

Doc Values (Life Saver)

• Disk based Field Data a.ka. Doc values

• Document to value mapping built at index time

• Store Field values on Disk (or Memory) in a column stride fashion

• Better compression than Field Cache

• Replacement of Field Cache (not completely)

• Quite suitable for Custom Scoring, Sorting and Faceting

References:

• Old article (but Good): http://blog.trifork.com/2011/10/27/introducing-lucene-index-doc-values/

• https://cwiki.apache.org/confluence/display/solr/DocValues

• http://www.elasticsearch.org/guide/en/elasticsearch/guide/current/doc-values.html

Scaling and Making Search Faster…

Partitioning

• 3 Level Partitioning, by Month, Country and Table name

• Each Month has its own Cluster and a Cluster Manager.

• Latency and Throughput are tradeoff, you can’t have both at the same time.

Node n

Web server Farm

Load Balancer

App Server

Search Head(US)

Search Head(UK)

Search Head(AU)

Master Cluster Manager

Internet

Cluster 2 for another

month e.gFeb

Fetch Cluster Mapping and make a request to Search Head with respective Solr cores for that Country, Month and Table

App ServerApp Server

Cluster 1for a Month

e.g. Jan

SolrSolrSolr

Cluster 1

Cluster 2

SolrSolrSolr

Node 1

Node n

SolrSolrSolr

SolrSolr

Node 1

Solr

Cluster 1

Cluster Manager

Cluster Manager

A

P

*A : ActiveP : Passive

Cluster Manager

Cluster Manager

P

A

1 user24 UI widgets, 24 Ajax requests

41 search requests

Search Head(US)

Search Head(UK)

Search Head(AU)

Index Optimization Strategy• Running optimization on ~200+ Solr cores is very-very time consuming

• Solr Cores with bigger Index size (~70GB) have 2500+ segments due to higher Merge Factor while Indexing.

• Can’ t be run in parallel on all Cores in a Single Machine as heavily dependent on Cpu and Disk IO

• Optimizing large segments into a very small number is very very time consuming and can take upto 3x Index size on Disk

• Other side Small number of segments improve performance drastically, so need to have a balance.

*As per our observation for our data, Optimization process takes ~42-46 seconds for 1GBWe need to do it for 4.6TB (including all boxes), the total Solr Index size for a Single Month

Node 1

SolrSolrSolr

Optimizer

Staging Cluster Manager

Production Cluster Manager

Fetches Cluster

Mapping (list of all

cores)

Once optimization and cache

warmup is done, pushes the

Cluster Mapping to Production

Cluster manager, making all

Indices live

Optimizing a Solr core into a very small

number of segments takes a huge time.

so we do it iteratively.

Algo:

Choose Max 3 cores on a

Machine to optimize in

parallel. Start with least size

of IndexIndex Size

Number of docs

Determine Max Segments Allowed

Current Segments

After optimization

Reduce Segments to *.90 in each Run

Node 2

SolrSolrSolr

Finally after optimization and cache warm-up…

A shard look like this.

Max Segments after

optimization

External Caching

• In Distributed search, for a repeated query request, all Solr severs

needs to be hit, even though result is served from Solr’s cache. It

increase search latency with lowering throughput.

• Solution: cache most frequently accessed query results in app layer

(LRU based eviction)

• We use Redis for Caching

• All complex aggregation queries’ results, once fetched from multiple

Solr servers are served from cache on subsequent requests.

Why Redis…

• Advanced In-Memory key-value store

• Insane fast

• Response time in order of 5-10ms

• Provides Cache behavior (set, get) with advance data structures like

hashes, lists, sets, sorted sets, bitmaps etc.

• http://redis.io/

Hardware

• We use Bare Metal, Dedicated servers for Solr due to below reasons

1. Performance gain (with virtual servers, performance dropped by ~18-20%)

2. Better value of computing power/$ spent

• 2.6Ghz, 32 core (4x8 core), 384GB RAM, 6TB SAS 15k (RAID10)

• 2.6Ghz, 16 core (2x8 core), 192GB RAM, 4TB SAS 15k (RAID10)

• Since Index size is 4.6TB/month, we want to cache more data in Disk Cache with bigger RAM.

SSD vs SAS

1. SSD : Indexing rate - peek (MySQL to Solr) : 330k docs/sec (each doc: ~100-125 bytes)

2. SAS 15k: 182k docs/sec (dropped by ~45%)

3. SAS 15k is quite cheaper than SSD for bigger hard disks.

4. We are using SAS 15k, as being cost effective but have plans to move to SSD in future.

Conclusion : Key takeawaysGeneral:

• Understand the characteristics of the data and partition it well.

Cache:

Spend time in analyzing the Cache usage. Tune them. It is 10x-50x faster.

Always use Filter Query (fq) wherever it is possible as that will improve the performance due to Filter cache.

GC :

Keep your JVM heap size to lower value (proportional to machine’s RAM) with leaving enough RAM for kernel as bigger

heap will lead to frequent GC. 4GB to 8GB heap allocation is quite good range. but we use 12GB/16GB.

Keep an eye on Garbage collection (GC) logs specially on Full GC.

Tuning Params:

Don’t use Soft Commit if you don’t need it. Specially in Batch Loading

Always explore tuning of Solr for High performance, like ramBufferSize, MergeFactor, HttpShardHandler’s

various configurations.

Use hash in Redis to minimize the memory usage.

Read the whole experience for more detail:http://rahuldausa.wordpress.com/2014/05/16/real-time-search-on-40-billion-

records-month-with-solr/

Thank you!Twitter: @rahuldausadynamicrahul2020@gmail.com

http://www.linkedin.com/in/rahuldausa