SolrCloud: Dealing with Large Tenants and Routing

Many Solr users need to handle multi-tenant data. There are different techniques that deal with this situation: some good, some not-so-good. Using routing to handle such data is one of the solutions, and it allows one to efficiently divide the clients and put them into dedicated shards while still using all the goodness of SolrCloud. In this blog post I will show you how to deal with some of the problems that come up with this solution: the different number of documents in shards and the uneven load.

Imagine that your Solr instance indexes your clients’ data. It’s a good bet that not every client has the same amount of data, as there are smaller and larger clients. Because of this it is not easy to find the perfect solution that will work for everyone. However, I can tell one you thing: it is usually best to avoid per/tenant collection creation. Having hundreds or thousands of collections inside a single SolrCloud cluster will most likely cause maintenance headaches and can stress the SolrCloud and ZooKeeper nodes that work together. Let’s assume that we would rather go to the other side of the fence and use a single large collection with many shards for all the data we have.

No Routing At All

The simplest solution that we can go for is no routing at all. In such cases the data that we index will likely end up in all the shards, so the indexing load will be evenly spread across the cluster:

Multitenancy - no routing (index)

However, when having a large number of shards the queries end up hitting all the shards:

Multitenancy - no routing

This may be problematic, especially when dealing with a large number of queries and a large number of shards together. In such cases Solr will have to aggregate results from the large number of shards, which can take time and be performance expensive. In these situations routing may be the best solution, so let’s see what that brings us. Read more of this post

SolrCloud Rebalance API

This is a post of the work done at BloomReach on smarter index & data management in SolrCloud.  

Authors: Nitin Sharma – Search Platform Engineer & Suruchi Shah –  Engineering Intern




In a multi-tenant search architecture, as the size of data grows, the manual management of collections, ranking/search configurations becomes non-trivial and cumbersome. This blog describes an innovative approach we implemented at BloomReach that helps with an effective index and a dynamic config management system for massive multi-tenant search infrastructure in SolrCloud.


The inability to have granular control over index and config management for Solr collections introduces complexities in geographically spanned, massive multi-tenant architectures. Some common scenarios, involving adding and removing nodes, growing collections and their configs, make cluster management a significant challenge. Currently, Solr doesn’t offer a scaling framework to enable any of these operations. Although there are some basic Solr APIs to do trivial core manipulation, they don’t satisfy the scaling requirements at BloomReach.

Innovative Data Management in SolrCloud

To address the scaling and index management issues, we have designed and implemented the Rebalance API, as shown in Figure 1. This API allows robust index and config manipulation in SolrCloud, while guaranteeing zero downtime using various scaling and allocation strategies. It has  two dimensions:


The seven scaling strategies are as follows:

  1. Auto Shard allows re-sharding an entire collection to any number of destination shards. The process includes re-distributing the index and configs consistently across the new shards, while avoiding any heavy re-indexing processes.  It also offers the following flavors:
    • Flip Alias Flag controls whether or not the alias name of a collection (if it already had an alias) should automatically switch to the new collection.
    • Size-based sharding allows the user to specify the desired size of the destination shards for the collection. As a result, the system defines the final number of shards depending on the total index size.
  2. Redistribute enables distribution of cores/replicas across unused nodes. Oftentimes, the cores are concentrated within a few nodes. Redistribute allows load sharing by balancing the replicas across all nodes.
  3. Replace allows migrating all the cores from a source node to a destination node. It is useful in cases requiring replacement of an entire node.
  4. Scale Up adds new replicas for a shard. The default allocation strategy for scaling up is unused nodes. Scale up also has the ability to replicate additional custom per-merchant configs in addition to the index replication (as an extension to the existing replication handler, which only syncs the index files)
  5. Scale Down removes the given number of replicas from a shard.
  6. Remove Dead Nodes is an extension of Scale Down, which allows removal of the replicas/shards from dead nodes for a given collection. In the process, the logic unregisters the replicas from Zookeeper. This in-turn saves a lot of back-and-forth communication between Solr and Zookeeper in their constant attempt to find the replicas on dead nodes.
  7. Discovery-based Redistribution allows distribution of all collections as new nodes are introduced into a cluster. Currently, when a node is added to a cluster, no operations take place by default. With redistribution, we introduce the ability to rearrange the existing collections across all the nodes evenly.

Read more of this post

Solr Presentations from Lucene/Solr Revolution 2014

Thanks to everyone who stopped by the Sematext booth at last week’s Lucene/Solr Revolution event in Washington, DC and attended our two talks:

The attendance, questions and interest are very much appreciated.  As a company that prides itself on its Solr expertise (and Elasticsearch expertise too, for that matter), it was nice to spend a couple days talking about search and Big Data challenges, performance monitoring and logging with fellow experts from around the world. Here are the slides for the two talks we gave (summaries of the talks can be found here):


  Videos of the talks will be posted here soon.  Hope to see everyone again next year!

Podcast: Tools to Monitor Solr, Manage Logs & Analyze Search Trends

Sematext Founder & President Otis Gospodnetic recently spoke with LucidWorks Chief of Product, Will Hayes as part of their SolrCluster podcast series.  Otis and Will discussed tools that Sematext has built to help monitor Solr and other stacks, manage and analyze logs, and analyze search trends.  They also discuss Solr/SolrCloud and Elasticsearch, their APIs, developer friendliness, as well as the general direction that search and big data industry leaders are moving toward around data acquisition and discovery as data increasingly grows.

Go here to listen to the podcast.  It runs about 36 minutes.  Enjoy!

Video: Scaling Solr with SolrCloud

During last  year’s Lucene Revolution conference in Dublin we had the opportunity to give four talks, one of which was Scaling Solr with SolrCloud. Through it we wanted to share our experiences around scaling Solr, especially as we have experience in running Solr internally and as a team of search consultants.  Enjoy the video and/or the slides!

Note: we are looking for engineers passionate about search to join our professional services team.  We’re hiring planet-wide!

Video: Administering and Monitoring SolrCloud Clusters

As you know, at Sematext, we are not only about consulting services, but also about administration, monitoring, and data analysis. Because of that, during last year’s Lucene Revolution conference in Dublin we gave a talk about administration and monitoring of SolrCloud clusters. During the talk, Rafał Kuć discusses some administration procedures for SolrCloud like collection management and schema modifications with the schema API. In addition, he also talks about why monitoring is important and what to pay attention to. Finally, he shows three real life examples of monitoring usefulnesses.  Enjoy the video and/or the slides!

Note: we are looking for engineers passionate about search to join our professional services team.  We’re hiring planet-wide!




Video: Using Solr for Logs with Rsyslog, Flume, Fluentd and Logstash

A while ago we published the slides from our talk at Lucene Revolution about using Solr for indexing and searching logs. This topic is of special interest for us, since we’ve released Logsene and we’re also offering consulting services for logging infrastructure. If you’re also into working with search engines or logs, please note that we’re hiring worldwide.

The video for that talk is now available, and you can watch it below. The talk is made of three parts:

  • one that discusses the general concepts of what a log is, structured logging and indexing logs in general, whether it’s Solr or Elasticsearch
  • one that shows how to use existing tools to send logs to Solr: Rsyslog and Fluentd to send structured events (yes, structured syslog!); Apache Flume and Logstash to take unstructured data, make it structured via Morphlines and Grok, and then send it to Solr
  • one that shows how to optimize Solr’s performance for handling logs. From tuning the commit frequency and merge factor to using time-based collections with aliases

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