The document discusses Solr Compute Cloud (SC2), an elastic Solr infrastructure developed by BloomReach to address challenges of scaling search platforms for big data applications. SC2 dynamically provisions Solr clusters in the cloud for pipelines and indexing jobs, providing isolation. It ensures latency guarantees, dynamic scaling, high availability and disaster recovery. SC2 addresses issues BloomReach faced with a shared cluster approach like throughput limitations, stability problems and indexing challenges.

2. Solr Compute Cloud – An Elastic
Solr Infrastructure
Nitin Sharma
- Member of technical staff, BloomReach
- nitin.sharma@bloomreach.com

3. Abstract
Scaling search platforms is an extremely hard problem
• Serving hundreds of millions of documents
• Low latency
• High throughput workloads
• Optimized cost.
At BloomReach, we have implemented SC2, an elastic Solr infrastructure for big data applications
that:
• Supports heterogeneous workloads while hosted in the cloud.
• Dynamically grows/shrinks search servers
• Application and Pipeline level isolation, NRT search and indexing.
• Offers latency guarantees and application-specific performance tuning.
• Provides high-availability features like cluster replacement, cross-data center support, disaster
recovery etc.

4. About Us
BloomReach
BloomReach has developed a personalized discovery platform that features applications that analyze
big data to makes our customers’ digital content more discoverable, relevant and profitable.
Myself
I work on search platform scaling for BloomReach’s big data. My relevant experience and background
includes scaling real-time services for latency sensitive applications and building performance and search-quality
metrics infrastructure for personalization platforms.

5. The
BloomReach
Personalized
Discovery
Platform

6. BloomReach’s Applications
Organic
Search
Content understanding
What it does
Content optimization,
management and measurement
Benefit
Enhanced discoverability and
customer acquisition in organic search
What it does
Personalized onsite search and
navigation across devices
Benefit
Relevant and consistent onsite
experiences for new and known users
What it does
Merchandising tool that understa
nds products and identifies oppo
rtunities
Benefit
Prioritize and optimize
online merchandising
SNAP
Compass

7. Agenda
• BloomReach search use cases and architecture
• Old architecture and issues
• Scaling challenges
• Elastic SolrCloud architecture and benefits
• Lessons learned

8. BloomReach Search Use Cases
1. Front-end (serving) queries – Uptime and Latency sensitive
2. Batch search pipelines – Throughput sensitive
3. Time bound indexing requirements – Customer Specific
4. Time bound Solr config updates

9. BloomReach Search Architecture
Zookeeper Ensemble Map Reduce
Solr
Cluster
Pipelines (Reads)
Indexing Pipelines
Pipeline 1
Pipeline 2
Pipeline n
Indexing 1
Indexing 2
Indexing n
Heavy Load
Moderate Load
Light Load
Legend
Public API
Search Traffic
Search Traffic

10. Throughput Issues…
Zookeeper Ensemble
Solr
Cluster
Pipeline 1
Pipeline 2
Pipeline n
Indexing 1
Indexing 2
Indexing n
Public API
Search Traffic
● Heterogeneous read
workload
● Same collection - different
pipelines, different query
patterns, different schedule
● Cache tuning is virtually
impossible
● Larger pipeline starving the
small ones
● Machine utilization
determines throughput and
stability of a pipeline at any
point
● No isolation among jobs

11. Stability and Uptime Issues…
Zookeeper Ensemble
Solr
Cluster
Pipeline 1
Pipeline 2
Pipeline n
Indexing 1
Indexing 2
Indexing n
Public API
Search Traffic
● Bad clients – bring down
the cluster/degrade
performance
● Bad queries (with heavy
load) – render nodes
unresponsive
● Garbage collection issues
● ZK stability issues (as we
scale collections)
● CPU /Load Issues
● Higher number of
concurrent pipelines,
higher number of issues

12. Indexing Issues…
Zookeeper Ensemble
Solr
Cluster
Pipeline 1
Pipeline 2
Pipeline n
Indexing 1
Indexing 2
Indexing n
Public API
Search Traffic
● Commit frequencies vary
with indexer types
● Indexer run during another
pipeline – performance
● Indexer client leaks
● Too many stored fields
● Non-batch updates

13. Rethinking…
• Shared cluster for pipelines does not scale.
• Guaranteeing an uptime of 99.99+ - non trivial
• Every job runs great in isolation. When you put them together, they fail.
• Running index-heavy load and read-heavy load - cluster performance issues.
• Any direct access to production cluster – cluster stability (client leaks, bad queries etc.).
What if every pipeline had its own cluster?

14. Solr Compute Cloud (SC2)
• Elastic Infrastructure – Provision Solr Clusters on demand, on-the-fly.
• Create, Use, Terminate Model - Create a temporary cluster with necessary data, use it and throw it away.
• Technologies behind SC2 (built in House)
Cluster Management API - Dynamic cluster provisioning and resource allocation.
Solr HAFT – High availability and data management library for SolrCloud.
• Isolation - Pipelines get their own cluster. One cannot disrupt another.
• Dynamic Scaling – Every pipeline can state its own replication requirements.
• Production Safeguard - No direct access. Safeguards from bad clients/access patterns.
• Cost Saving – Provision for the average; withstand peak with elastic growth.

15. Solr Compute Cloud
Zookeeper Ensemble
Solr
Cluster
Request: {Collection: A, Replica: 6}
Pipeline 1
Solr
Compute
Cloud
API
Solr Cluster
Collection A
Replicas: 6
1. Read pipeline requests
collection and desired
replicas from SC2 API.
2. SC2 API provisions
cluster dynamically with
needed setup (and
streams Solr data).
3. SC2 calls HAFT service to
replicate data from
production to provisioned
cluster.
4. Pipeline uses this cluster
to run job.
1
4
2
3
Solr
HAFT
Service
3
Read
Replicate

16. Solr Compute Cloud…
Zookeeper Ensemble
Solr
Cluster
Pipeline 1
Solr
Compute
Cloud
API
Solr Cluster
Collection A
Replicas: 6
1. Pipeline finishes running
the job.
2. Pipeline calls SC2 API to
terminate the cluster.
3. SC2 terminates the
cluster.
Terminate: {Cluster}
2
3
Solr
HAFT
Service
1

17. Solr Compute Cloud – Read Pipeline View
Zookeeper Ensemble
Pipeline 1
Solr
Compute
Cloud
API
Solr Cluster
Collection A
Replicas: 6
Request: {Collection: A, Replica: 6}
Pipeline 2
Solr Cluster
Collection B
Replicas: 2
Request: {Collection: B, Replica: 2}
Pipeline n
Solr Cluster
Collection C
Replicas: 1
Request: {Collection: C, Replica: 1}
Solr
HAFT
Service
Production
Solr Cluster

18. Solr Compute Cloud – Indexing
Zookeeper Ensemble
Production
Solr Cluster
Request: {Collection: A, Replica: 2}
Indexing
Solr
Compute
Cloud
API
Solr Cluster
Collection A
Replicas: 6
1. Read pipeline requests
collection and desired
replicas from SC2 API.
2. SC2 API provisions
cluster dynamically with
needed setup (and
streams Solr data).
1. Indexer uses this cluster
to index the data.
2. Indexer calls HAFT
service to replicate the
index from dynamic
cluster to production.
3. HAFT service reads data
from dynamic cluster and
replicates to production
Solr.
1
3
2
Replicate
Solr HAFT Service
4
5
Read

19. Solr Compute Cloud – Global View
Zookeeper Ensemble
Solr
Compute
Cloud
API
Solr HAFT Service
Production
Solr Cluster
Indexing Pipelines 1
Elastic Clusters
Indexing Pipelines n
Read Pipelines 1
Read Pipelines n
Provision: {Cluster}
Terminate: {Cluster}
Replicate Index
Replicate Index
Run Job

20. Solr Compute Cloud API
1. API to provision clusters on demand.
2. Dynamic cluster and resource allocation (includes cost optimization)
3. Track request state, cluster performance and cost.
4. Terminate long-running, runaway clusters.

21. Solr HAFT Service
1. High availability and fault tolerance
2. Home-grown technology
3. Open Source -  (Work in progress)
4. Features
• One push disaster recovery
• High availability operations
• Replace node
• Add replicas
• Repair collection
• Collection versioning
• Cluster backup operations
• Dynamic replica creation
• Cluster clone
• Cluster swap
• Cluster state reconstruction

22. Solr HAFT Service – Functional View
Black Box Recording
Index Management Actions
Custom Commit Node Replacement
Collection Versioning
Solr HAFT Service
Clone Collections
Clone Alias
Node Repair
Clone Cluster
Lucene Segment
Optimize
High Availability Actions
Cluster Backup Operations
Solr Metadata
Zookeeper
Metadata
Dynamic Replica
Creation
Cluster Clone
Cluster Swap
Cluster State
Reconstruction
Verification Monitoring

23. Disaster Recovery in New Architecture
Zookeeper Ensemble
Old
Production
Solr
Cluster
Zookeeper Ensemble
New
Solr
Cluster
Push
Button
Recovery
Solr HAFT Service
Brave Soul on Pager Duty
1
2
3
DNS
1. Guy on Pager clicks the
recovery button
2. Solr HAFT Service
triggers
Cluster Setup
State Reconstruction
Cluster Clone
Cluster Swap
3. Production DNS – New
Cluster

24. SC2 vs Non-SC2 (Stability Features)
Property Non-SC2 SC2
Linear Scalability for Heterogeneous
Workload
Pipeline Level Isolation
Dynamic Collection Scaling
Prevention from Bad Clients
Pipeline Specific Performance
No Direct Access to Production Cluster
Can Sleep at night? 

25. SC2 vs Non-SC2 (Availability Features)
Property Non-SC2 SC2
Cross Data-Center Support
Cluster Cloning
Collection Versioning
One-Push Disaster Recovery
Repair API for Nodes/Collections
Node Replacement

26. Lessons Learned
1. Solr is a search platform. Do not use it as a database (for scans and lookups).
Evaluate your stored fields.
2. Understand access patterns, QPS and queries in detail. Be careful when tuning
caches.
3. Have access control for large-scale jobs that directly talk to your cluster. (Internal
DDOS attacks are hard to track.)
4. Instrument every piece of infrastructure and collect metrics.
5. Build automated disaster recovery (You will need it. )

27. Questions?
Thank You!
Nitin Sharma
nitin.sharma@bloomreach.com
https://www.linkedin.com/in/knitinsharma