SolrCloud exceptions with Apache Zookeeper

At the time we speak ( Solr 7.3.1 ) SolrCloud is a reliable and stable distributed architecture for Apache Solr.
But it is not perfect and failures happen.

Apache Zookeeper[1] is the system responsible of managing the communications across the SolrCloud cluster.
It contains the shared collections configurations and it has the view of the cluster status.
It is part of the brain of the cluster, a keeper that maintains the cluster healthy and functional.

It is able to answer questions such as :

• Who is the leader for this shard and collection?
• Is this node down ?
• Is this node recovering ?

The Solr nodes communicate with Zookeeper to understand who to contact when running SolrCloud operations.

This lightening blog post will present some practical tips to follow when your client application encounters some classic exceptions dealing with SolrCloud and Apache Zookeeper.
Special thanks to the Apache Solr user mailing list contributors and the Apache Solr community, this post is an aggregation of recommendations from there and from official code and documentation.

org.apache.solr.common.SolrException: Could not load collection from ZK: <collection name>

If you landed here with just that Exception I assume there is a missing :
“ Caused by: org.apache.zookeeper.KeeperException$SessionExpiredException: KeeperErrorCode = Session expired for /collections/<collection name>/state.json “ ?

Solr’s zkClientTimeout is used to set ZooKeeper’s sessionTimeout, and that’s what is exceeded when a session expires.
When this kind of exception happens, it means something has gone VERY wrong in the Solr-Zookeeper communication, 30 seconds ( the current default[2] ) is a REALLY long time when applications are trying to communicate.

Recommendation : take care of the different time outs around, don’t keep them too small !
i.e. for zkClientTimeout assign a value >= 30 seconds.
maxSessionTimeout (Zookeeper)
New in 3.3.0: the maximum session timeout in milliseconds that the server will allow the client to negotiate. Defaults to 20 times the tickTime.

zkClientTimeout (Solr)
Controls your client timeout.

Once checked the time outs, let’s explore some possible root causes.
A session expiry can be caused by:
1. Garbage collection on Solr node/Zookeeper – extreme GC pauses can happen with the heap being too small or VERY large
2. Slow IO on disk.
3. Network latency


  1. set up a JVM profiler to monitor closely your Solr and Zookeeper nodes, take particular attention to the garbage collection cycles and the memory usage in general : you don’t want Zk to swap too much! ( GCViewer[3] could be a nice tool for this)
  2. Verify that the Zookeeper node has a fast writing access to the disk : Zookeeper needs fast writes and ideally a separate disk allocated.
  3. Monitor your network and make sure the the solr nodes can talk effectively to the Zookeeper nodes

In case the suggestions are not solving your problem, you may be experiencing a Solr bug.
One of them is[4] which unfortunately has not been fixed yet.

org.apache.solr.client.solrj.SolrServerException: No live SolrServers available to handle this

From the official JavaDoc :

“Tries to query a live server from the list provided in Req. Servers in the dead pool are skipped.
* If a request fails due to an IOException, the server is moved to the dead pool for a certain period of
* time, or until a test request on that server succeeds.
* Servers are queried in the exact order given (except servers currently in the dead pool are skipped).
* If no live servers from the provided list remain to be tried, a number of previously skipped dead servers will be tried.
* Req.getNumDeadServersToTry() controls how many dead servers will be tried.
* If no live servers are found a SolrServerException is thrown.”

What was the status of the cluster at the moment the exception happened ?
Was any Solr server UP and running according to Zookeeper knowledge ?

The recommendation is to check the clusterstate.json when the exception happens.
From the Solr admin UI you can open Cloud->Tree and verify which nodes are up and running.

It could be very much related a node failure ( that could be related to any possible reason including GC)
I’ve seen situations where it was caused by a specific query, the real exception got hidden by a “No live SolrServers…” client exception.
Solr logs should help to identify the inner Solr problem and JVM monitoring could discard any memory/gc problem.
Some people saw this with wildcard queries (when every shard reported a “too many expansions…”
type error, but the exception in the client response was “No live SolrServers…”.

org.apache.solr.common.SolrException: Could not find a healthy node to handle the request

Pretty much same considerations as the “No Live Solr Server”.
This happens when the load balancer SolrJ side is unable to retrieve an alive node, from the cluster ( based on Zookeeper state).
This happens before the previous exception, so the request doesn’t even reach the LoadBalancinghttpSolrClient.

[2] SOLR-5565
[4] SOLR-8868

Apache Solr: orchestrating Known item and Full-text search


You’re working as a search engineer for XYZ Ltd, a company which sells electric components. XYZ provided you the application logs of the last six months, and some business requirements.

Two kinds of customers, two kinds of requirements, two kinds of search

The log analysis shows that XYZ has mainly two kinds of customers: a first group, the “expert” users (e.g. electricians, resellers, shops) whose members are querying the system by product identifiers, codes (e.g. SKU, model codes, thinks like Y-M8GB, 140-213/A and ABD9881); it’s clear, at least it seems so, they already know what they want and what they are looking for. However, you noticed a lot of such queries produce no results. After investigating, the problem seems to be that codes and identifiers are definitely hard to remember: queries use a lot of disparate forms for pointing to the same product. For example:

  • y-m8gb (lowercase)
  • YM8GB (no delimiters)
  • YM-8GB (delimiter in a wrong place)
  • Y/M8GB (wrong delimiter)
  • Y M8GB (whitespace instead of delimiter)
  • y M8/gb (a combination of cases above)

This kind of scenario, where there’s only one relevant document in the collection, is usually referred to as “Known Item Search”: our first requirement is to make sure this “product identifier intent” is satisfied.

The other group of customers are end-users, like me and you. Being not so familiar with product specs like codes or model codes, the behaviour here is different: they use a plain keyword search, trying to match products by entering terms which represents names, brands, manufacturer. An here it comes the second requirement which can be summarized as follows: people must be able to find products by entering plain free-text queries.

As you can imagine, in this case search requirements are different from the other scenario: the focus here is more “term-centric”, therefore involving different considerations about the text analysis we’d need to apply.

While the expert group query is supposed to point to one and only one product (we are in a black / white scenario: match or not), the needs on the other side require the system to provide a list of “relevant” documents, according to the terms entered.

An important thing / assumption before proceeding: for illustration purposes we will consider those two queries / user groups as disjoint: that is, a given user belongs only to one of the mentioned groups, not both. Better, a given user query will contain product identifiers or terms, not both. 

Schema & configuration notes

The expert group, and the “Known Item Search”

The “product identifier” intent, which is assumed to be implicit in the query behaviour of this group, can be captured, both at index and query time, by applying the following analyzer, which basically treats the incoming value as a whole, normalizes it to lower case, removes all delimiters and finally collapses everything in a single output token.

<fieldtype name="identifier" class="solr.TextField" omitNorms="true">
        <tokenizer class="solr.KeywordTokenizerFactory" />
        <filter class="solr.LowerCaseFilterFactory" />
        <filter class="solr.WordDelimiterGraphFilterFactory"
                splitOnCaseChange="0" />
<field name="product_id" type="identifier" indexed="true" ... />

In the following table you can see the analyzer in action with some example:

As you can see, the analyzer doesn’t declare a type attribute because it is supposed to be applied both at index and query time. However, there’s a difference in the incoming value: at index time the analyzer is dealing with a field content (i.e. the value of a field of an incoming document), while at query time the value which flows through the pipeline is composed by one or more terms entered by the user (a query, briefly).

While at index time everything works as expected, at query time the analyzer above requires a feature that has been introduced in Solr 6.5: the “Split On Whitespace” flag [1]. When it is set to “false” (as we need here in this context), it causes the incoming query text to be kept as a single whole unit, when sent to the analyzer.

Prior to Solr 6.5 we didn’t have such control, and the analyzers were receiving a “pre-tokenized-by-whitespaces” tokens; in other words, the unit of work of the query-time analysis was the single term: the analyzer chain (including the tokenizer itself) was invoked for each term outputted by that pre-whitespace-tokenization. As consequence of that our analyzer, at query time, couldn’t work as expected: if we take the example #5 and #6 from the table above, you can see the user entered a whitespace. With the “Split on Whitespace” flag set to true (explicitly, or using a Solr < 6.5), the pre-tokenization described above produces two tokens:

  • #5 = {“Y”, ”M8GB”}
  • #6 = {“y”, “M8/gb”}

so our analyzer would receive 2 tokens (for each case) and there won’t be any match with the single term ym8gb stored in the index. So, prior to Solr 6.5 we had two ways for dealing with this requirement:

  • client side: wrapping the whole query with double quotes, escaping whitespaces with “\”, or replacing them with a delimiter like “-“. Easy, but it requires a control on the client code, and this is not always possible.
  • Solr side: applying to the incoming query the same transformations as above but this time at query parser level. Easy, if you know some Lucene / Solr internals. In addition it requires a context where you have permissions for installing custom plugins in Solr. A similar effect could be obtained also using an UpdateRequestProcessor which would create a new field with the same value of the original field but without any whitespace.

The end-users group, and the full-text search query

In this case we are within a “plain” full-text search context, where the analysis identified a couple of target fields: product names and brands.

Differently from the previous scenario, here we don’t have a unique and deterministic way to satisfy the search requirement. It depends on a lot of factors: the catalog, the terms distribution, the implementor experience, the customer expectations in terms of user search experience. All these things can lead to different answers. Just for example, here’s a possible option:

<fieldType name="brand" class="solr.TextField" omitNorms="true">
        <tokenizer class="solr.StandardTokenizerFactory"/>
        <filter class="solr.LowerCaseFilterFactory"/>
        <filter class="solr.StopFilterFactory" 

<fieldType name="name" class="solr.TextField">
        <tokenizer class="solr.StandardTokenizerFactory"/>
        <filter class="solr.LowerCaseFilterFactory"/>
        <filter class="solr.EnglishPossessiveFilterFactory"/>
        <filter class="solr.EnglishMinimalStemFilterFactory"/>
        <filter class="solr.RemoveDuplicatesTokenFilterFactory"/>
        <filter class="solr.LengthFilterFactory" min="2" max="50" />

The focus here is not on the schema design itself: the important thing to underline is that this requirement needs a completely different configuration from the “Known Item Search” previously described.

Specifically, let’s assume we ended up following a “term-centric” approach for satisfying the second requirement. The approach requires a different value for the “Split on Whitespace” parameter, which has to be set to true, in this case.

The “sow” parameter can be set at SearchHandler level, so it is applied at query time. It can be declared within the solrconfig.xml and, depending on the configuration, it can be overridden using a named (HTTP) query parameter.

A “split on whitespace” pre-tokenisation leads us on a scenario which is really different from the “Known Item Search”, where instead we “should” be in a field-centric search; “should” is double-quoted because if, from one side, we are actually using a field-centric search, on the other side we are on an edge case where we’re querying one single field with one single query term (the first analyzer in this post always outputs one term).

The implementation


Although one could think the first thing is about how to combine those two different query strategies, prior to that, the question we need to answer is where to implement the solution? Clearly, regardless the way we will decide to follow, we will have to implement a (search) workflow, which can be summarised in the following diagram:

Known Item Search in Apache Solr

On Solr side, each “search” task needs to be executed in a different SearchHandler, so returning to our question: where do we want to implement such workflow? We have three options: outside, between or inside Solr.

#1: Client-side implementation

The first option is to implement the flow depicted above in the client application. That assumes you have the required control and programming skills on that side. If this assumption is true, then it’s relatively easy to code the workflow: you can choose one of the client API binding available for your language and then implement the double + conditional search illustrated above.

  • Pros: easy to implement. It requires a minimal Solr (functional) knowledge.
  • Cons: the search workflow / logic is moved on the client side. Programming is required, so you must be in a context where this can be done and where the client application code is under your control.

#2: Man-in-the-middle

Moving things outside the client sphere, another popular option, which can be still seen as a client-side alternative (from the Solr perspective), is a proxy / adapter / facade. Whatever is the name you want to give to this stuff, this is a new module which sits between the client application and Solr; it would intercept all requests and it would implement the custom logic by orchestrating the search endpoints exposed in Solr.

Being a new module, it has several advantages:

  • it can be coded using your preferred language
  • it is completely decoupled from the client application, and from Solr as well

but for the same reason, it has also some disadvantages:

  • it must be created: designed, implemented, tested, installed and maintained
  • it is a new piece in your system, which necessarily increases the overall complexity of the architecture
  • Solr exposes a lot of (index & search) services. With this option, all those services should be proxied, therefore resulting in a lot of unnecessary delegations (i.e. delegate services that don’t add any value to the execution chain).

#3: In Solr

The last option moves the workflow implementation (and the search logic) in the place where, in my opinion, it should be: in Solr.

Note that this option is usually not only a “philosophical” choice: if you are a search engineer, most probably you will be hired for designing, implementing and tuning the “search-side of the cake”. That means it’s perfectly possible that, for a lot of reasons, you must think to the client application as an external (sub)system, where you don’t have any kind of control.

The main drawback of this approach is that, as you can imagine, it requires programming skills plus a knowledge about the Solr internals.

In Solr, a search request is consumed by a SearchHandler, a component which is in charge of executing the logic associated with a given search endpoint. In our example, we would have the following search handlers matching the two requirements:

<!-- Known Item search -->
<requestHandler name="/known_item_search" class="solr.SearchHandler">
   <lst name="invariants">
        <str name="defType">lucene</str>
        <bool name="sow">false</bool> <!-- No whitespace split -->
        <str name="df">product_id</str>

<!-- Full-text search -->
<requestHandler name="/full-text-search" class="solr.SearchHandler">
    <lst name="invariants">
         <bool name="sow">true</bool> <!--Whitespace split -->
         <str name="defType">edismax</str>
         <str name="df">product_name</str>
         <str name="qf">

On top of that, we would need a third component, which would be in charge to orchestrate the two search handlers above. I’ll call this component a “Composite Request Handler”.

The composite handler would also provide the public search endpoint called by clients. Once a request is received, the composite request handler implements the search workflow: it invokes all the handlers that compose its chain, and it will stop when one the invocation target produces the expected result.

The composite handler configuration looks like this:

<requestHandler name="/search" class=".....">
    <str name="chain">/know_item_search,/full_text_search</str>

On the client side, that would require only one request because the entire workflow will be implemented in Solr, by means of the composite request handler. In other words, imagining a GUI with a search bar, the client application, when the search button is pressed, would have to retrieve the term(s) entered by the user and send just one request (to the composite handler endpoint), regardless the intent of the user (i.e. regardless the group the user belongs to).

The composite request handler introduced in this section has been already implemented, you can find it in our Github account, here[2].

Enjoy and, as usual, any feedback is warmly welcome!



SolrCloud Leader Election Failing

At the time we speak ( Solr 7.3.0 ) SolrCloud is a reliable and stable distributed architecture for Apache Solr.
But it is not perfect and failures happen.
This lightening blog post will present some practical tips to follow when a specific shard of a collection is down with no leader and the situation is stuck.
The following problem has been experienced with the following Solr versions :

  • 4.10.2
  • 5.4.0

Steps to solve the problem may involve manual interaction with the Zookeeper Ensemble[1].
The following steps are extracted from an interesting thread of the Solr User mailing list[2] and practical experience on the field.
In particular, thanks to Jeff Wartes for the suggestions, that proved useful for me in a couple of occasions.


  • All nodes for a Shard in a Collection are up and running
  • There is no leader for the shard
  • All the nodes are in a “Recovering” / “Recovery Failed” state
  • Search is down and the situation persist after many minutes (> 5)


A possible explanation for this problem to occur is when the node-local version of the Zookeeper clusterstate has diverged from the centralized Zookeeper cluster state.
One possible cause for the leader election to break is a Zookeeper failure : for example you lose >=50% of the ensemble nodes or the connectivity among the ensemble nodes for a certain period of time ( this is the scenario I experimented directly)
This failure, even if resolved later, can bring a corruption to the Zookeeper file system.
Some of the SolrCloud collections may remain in a not consistent status.

It may be necessary to manually delete corrupted files from Zookeeper :
Let’s start from :

An healthy SolrCloud cluster presents as many core_nodeX as the total replicas for the shard.
You don’t want duplicates or missing nodes here.
If you’re having trouble getting a sane election, you can try deleting the lowest-numbered entries (as well as any lower-numbered duplicates) and try to foce the election again. Possibly followed by restarting the node with that lowest-numbered entry.

Make sure that this folder exists and has the expected replica as a leader.

This folder can be informative too, this represents replicas that the *leader* thinks are out of sync, usually due to a failed update request.

After having completed the verification above, there a couple of Collection API endpoints that may be useful :

Force Leader Election

Force Leader Rebalance

N.B. rebalancing all the leader will affect all the shards


[1] Apache Zookeeper Solr Cli

[2] Solr Mailing List Thread

[3] Solr Collection API