It is clear that the current documentation available on the wiki is not enough to fully understand the Solr Suggester : this blog post will describe all the available implementations with examples and tricks and tips.
Introduction
If there’s one thing that months of Solr-user mailing list have taught me is that the autocomplete feature in a Search Engine is vital and around Apache Solr Autocomplete there’s as much hype as confusion.
In this blog I am going to try to clarify as much as possible all the kind of Suggesters that can be used in Solr, exploring in details how they work and showing some real world example.
It’s not scope of this blog post to explore in details the configurations.
Please use the official wiki [1] and this really interesting blog post [2] to integrate this resource.
Let’s start with the definition of the Apache Solr Suggester component.
The Apache Solr Suggester
” The SuggestComponent in Solr provides users with automatic suggestions for query terms. You can use this to implement a powerful auto-suggest feature in your search application.This approach utilizes Lucene’s Suggester implementation and supports all of the lookup implementations available in Lucene.The main features of this Suggester are:
- Lookup implementation pluggability
- Term dictionary pluggability, giving you the flexibility to choose the dictionary implementation
- Distributed support “
For the details of the configuration parameter I suggest you the official wiki as a reference.
Our focus will be the practical use of the different Lookup Implementation , with clear examples.
Term Dictionary
The DocumentDictionary uses the Lucene Index to provide the list of possible suggestions, and specifically a field is set to be the source for these terms.
Suggester Building
- retrieving the terms (source for the suggestions) from the dictionary
- build the data structures that the Suggester requires for the lookup at query time
- Store the data structures in memory/disk
The produced data structure will be stored in memory in first place.
It is suggested to additionally store on disk the built data structures, in this way it will available without rebuilding, when it is not in memory anymore.
For example when you start up Solr, the data will be loaded from disk to the memory without any rebuilding to be necessary.
This parameter is:
“storeDir” for the FuzzyLookup
“indexPath” for theAnalyzingInfixLookup
- the stored content of the configured field is read from the disk ( stored=”true” is required for the field to have the Suggester working)
- the compressed content is decompressed ( remember that Solr stores the plain content of a field applying a compression algorithm [3] )
- the suggester data structure is built
| Parameter | Description |
|---|---|
| buildOnCommit or buildOnOptimize | If true then the lookup data structure will be rebuilt after each soft-commit. If false, the default, then the lookup data will be built only when requested by query parameter suggest.build=true.
Because of the previous observation is quite easy to understand that the buildOnCommit is highly discouraged. |
| buildOnStartup | If true then the lookup data structure will be built when Solr starts or when the core is reloaded. If this parameter is not specified, the suggester will check if the lookup data structure is present on disk and build it if not found.
Again, is highly discouraged to set this to true, or our Solr cores could take really long time to start up. |
A good consideration at this point would be to introduce a delta approach in the dictionary building.
Could be a good improvement, making more sense out of the “buildOnCommit” feature.
I will follow up verifying the technical feasibility of this solution.
Now let’s step to the description of the various lookup implementations with related examples.Note: when using the field type “text_en” we refer to a simple English analyser with soft stemming and stop filter enabled.
The simple corpus of document for the examples will be the following :
[
{
"id":"44",
"title":"Video gaming: the history"},
{
"id":"11",
"title":"Video games are an economic business"},
{
"id":"55",
"title":"The new generation of PC and Console Video games"},
{
"id":"33",
"title":"Video games: multiplayer gaming"}]
And a simple synonym mapping : multiplayer, online
AnalyzingLookupFactory
<lst name="suggester">
<str name=”name”>AnalyzingSuggester</str>
<str name=”lookupImpl”>AnalyzingLookupFactory</str>
<str name=”dictionaryImpl”>DocumentDictionaryFactory</str>
<str name=”field”>title</str>
<str name=”weightField”>price</str>
<str name=”suggestAnalyzerFieldType”>text_en</str>
</lst>
| Description | |
|---|---|
| Data Structure | FST |
| Building | For each Document, the stored content from the field is analyzed according to the suggestAnalyzerFieldType.
The tokens produced are added to the Index FST. |
| Lookup strategy | The query is analysed, the tokens produced are added to the query FST.
An intersection happens between the Index FST and the query FST. The suggestions are identified starting at the beginning of the field content. |
| Suggestions returned | The entire content of the field . |
This suggester is quite powerful as it allows to provide suggestions at the beginning of a field content, taking advantage of the analysis chain provided with the field.
It will be possible in this way to provide suggestions considering synonyms, stop words, stemming and any other token filter used in the analysis.Let’s see some example:
| Query to autocomplete | Suggestions | Explanation |
|---|---|---|
| “Video gam” |
|
The suggestions coming are simply the result of the prefix match. No surprises so far. |
| “Video Games” |
|
The input query is analysed, and the tokens produced are the following : “video” “game”.
The analysis was applied at building time as well, producing the same stemmed terms for the beginning of the titles. “video gaming” -> “video” “game” “video games” -> “video” “game” So the prefix match applies.
|
| “Video game econ” |
|
In this case we can see that the stop words were not considered when building the index FST. Note :
position increments MUST NOT be preserved for this example to work, see the configuration details. |
| “Video games online ga” |
|
Synonym expansion has happened and the match is returned as online and multiplayer are considered synonyms by the suggester, based on the analysis applied. |
FuzzyLookupFactory
<lst name="suggester">
<str name=”name”>FuzzySuggester</str>
<str name=”lookupImpl”>FuzzyLookupFactory</str>
<str name=”dictionaryImpl”>DocumentDictionaryFactory</str>
<str name=”field”>title</str>
<str name=”weightField”>price</str>
<str name=”suggestAnalyzerFieldType”>text_en</str>
</lst>
| Description | |
|---|---|
| Data Structure | FST |
| Building | For each Document, the stored content from the field is analyzed according to the suggestAnalyzerFieldType.
The tokens produced are added to the Index FST. |
| Lookup strategy | The query is analysed, the tokens produced are then expanded producing for each token all the variations accordingly to the max edit configured for the String distance function configured ( default is Levestein Distance[4]).
The finally produced tokens are added to the query FST keeping the variations. An intersection happens between the Index FST and the query FST. The suggestions are identified starting at the beginning of the field content. |
| Suggestions returned | The entire content of the field . |
This suggester is quite powerful as it allows to provide suggestions at the beginning of a field content, taking advantage of a fuzzy search on top of the analysis chain provided with the field.
It will be possible in this way to provide suggestions considering synonyms, stop words, stemming and any other token filter used in the analysis and support also misspelled terms by the user.
It is an extension of the Analysis lookup.IMPORTANT : Remember the proper order of processing happening at query time :
- FIRST, the query is analysed, and tokens produced
- THEN, the tokens are expanded with the inflections based on the Edit distance and distance algorithm configured
Let’s see some example:
| Query to autocomplete | Suggestions | Explanation |
|---|---|---|
| “Video gmaes” |
|
The input query is analysed, and the tokens produced are the following : “video” “gmae”.
Then the FST associated is expanded with new statuses containing the inflections for each token. For example “game” will be added to the query FST because it has a distance of 1 from the original token. And the prefix matching is working fine returning the expected suggestions. |
| “Video gmaing“ |
|
The input query is analysed, and the tokens produced are the following : “video” “gma”.
Then the FST associated is expanded with new statuses containing the inflections for each token. For example “gam” will be added to the query FST because it has a distance of 1 from the original token. So the prefix match applies.
|
| “Video gamign“ |
|
This can seem odd at first, but it is coherent with the Look up implementation.
The input query is analysed, and the tokens produced are the following : “video” “gamign”. Then the FST associated is expanded with new statuses containing the inflections for each token. For example “gaming” will be added to the query FST because it has a distance of 1 from the original token. But no prefix matching will apply because in the Index FST we have “game”, the stemmed token for “gaming” |
AnalyzingInfixLookupFactory
<lst name="suggester">
<str name=”name”>AnalyzingInfixSuggester</str>
<str name=”lookupImpl”>AnalyzingInfixLookupFactory</str>
<str name=”dictionaryImpl”>DocumentDictionaryFactory</str>
<str name=”field”>title</str>
<str name=”weightField”>price</str>
<str name=”suggestAnalyzerFieldType”>text_en</str>
</lst>
| Description | |
|---|---|
| Data Structure | Auxiliary Lucene Index |
| Building | For each Document, the stored content from the field is analyzed according to the suggestAnalyzerFieldType and then additionally EdgeNgram token filtered.
Finally an auxiliary index is built with those tokens. |
| Lookup strategy | The query is analysed according to the suggestAnalyzerFieldType.
Than a phrase search is triggered against the Auxiliary Lucene index The suggestions are identified starting at the beginning of each token in the field content. |
| Suggestions returned | The entire content of the field . |
This suggester is really common nowadays as it allows to provide suggestions in the middle of a field content, taking advantage of the analysis chain provided with the field.
It will be possible in this way to provide suggestions considering synonyms, stop words, stemming and any other token filter used in the analysis and match the suggestion based on internal tokens.Let’s see some example:
| Query to autocomplete | Suggestions | Explanation |
|---|---|---|
| “gaming” |
|
The input query is analysed, and the tokens produced are the following : “game” .
In the Auxiliary Index , for each of the field content we have the EdgeNgram tokens: “v”,”vi”,”vid”… , “g”,”ga”,”gam”,“game” . So the match happens and the suggestion are returned |
| “ga” |
|
The input query is analysed, and the tokens produced are the following : “ga” .
In the Auxiliary Index , for each of the field content we have the EdgeNgram tokens: “v”,”vi”,”vid”… , “g”,“ga”,”gam”,”game” . So the match happens and the suggestion are returned
|
| “game econ” |
|
Stop words will not appear in the Auxiliary Index.
Both “game” and “econ” will be, so the match applies. |
BlendedInfixLookupFactory
We are not going to describe the details of this lookup strategy as it’s pretty much the same of the AnalyzingInfix.
The only difference appears scoring the suggestions, to weight prefix matches across the matched documents. The score will be higher if a hit is closer to the start of the suggestion or vice versa.
FSTLookupFactory
<lst name="suggester">
<str name=”name”>FSTSuggester</str>
<str name=”lookupImpl”>FSTLookupFactory</str>
<str name=”dictionaryImpl”>DocumentDictionaryFactory</str>
<str name=”field”>title</str>
</lst>
| Description | |
|---|---|
| Data Structure | FST |
| Building | For each Document, the stored content is added to the Index FST. |
| Lookup strategy | The query is added to the query FST.
An intersection happens between the Index FST and the query FST. The suggestions are identified starting at the beginning of the field content. |
| Suggestions returned | The entire content of the field . |
This suggester is quite simple as it allows to provide suggestions at the beginning of a field content, with an exact prefix match.Let’s see some example:
| Query to autocomplete | Suggestions | Explanation |
|---|---|---|
| “Video gam” |
|
The suggestions coming are simply the result of the prefix match. No surprises so far. |
| “Video Games” |
|
The input query is not analysed, and no field content in the documents starts with that exact prefix
|
| “video gam” |
|
The input query is not analysed, and no field content in the documents starts with that exact prefix |
| “game” |
|
This lookup strategy works only at the beginning of the field content. So no suggestion is returned. |
For the following lookup strategy we are going to use a slightly modified corpus of documents :
[
{
"id":"44",
"title":"Video games: the history"},
{
"id":"11",
"title":"Video games the historical background"},
{
"id":"55",
"title":"Superman, hero of the modern time"},
{
"id":"33",
"title":"the study of the hierarchical faceting"}]
FreeTextLookupFactory
<lst name=”suggester”>
<str name=”name”>FreeTextSuggester</str>
<str name=”lookupImpl”>FreeTextLookupFactory</str>
<str name=”dictionaryImpl”>DocumentDictionaryFactory</str>
<str name=”field”>title</str>
<str name=”ngrams”>3</str>
<str name=”separator”> </str>
<str name=”suggestFreeTextAnalyzerFieldType”>text_general</str>
</lst>
| Description | |
|---|---|
| Data Structure | FST |
| Building | For each Document, the stored content from the field is analyzed according to the suggestFreeTextAnalyzerFieldType.
As a last token filter is added a ShingleFilter with a minShingle=2 and maxShingle=. The final tokens produced are added to the Index FST. |
| Lookup strategy | The query is analysed according to the suggestFreeTextAnalyzerFieldType.
As a last token filter is added a ShingleFilter with a minShingle=2 and maxShingle=. Only the latest “ngrams” tokens will be evaluated to produce |
| Suggestions returned | ngram tokens suggestions |
This lookup strategy is completely different from the others seen so far, its main difference is that the suggestions are ngram tokens ( and NOT the full content of the field).
We must take extra care in using this suggester as it is quite easily prone to errors, some guidelines :
- Don’t use an heavy Analyzers, the suggested terms will come from the index, so be sure they are meaningful tokens. A really basic analyser is suggested, stop words and stemming are not
- Be sure you use the proper separator(‘ ‘ is suggested), the default will be encoded in “#30;”
- ngrams parameter will set the last n tokens to be considered from the query
Let’s see some example:
| Query to autocomplete | Suggestions | Explanation |
|---|---|---|
| “video g” |
|
The input query is analysed, and the tokens produced are the following : “video g” “g”
The analysis was applied at building time as well, producing 2-3 shingles. “video g” matches by prefix 2 shingles from the index FST . “g” matches by prefix 1 shingle from the index FST. |
| “games the h” |
|
The input query is analysed, and the tokens produced are the following : “games the h” “the h””h”
The analysis was applied at building time as well, producing 2-3 shingles. “games the h” matches by prefix 2 shingles from the index FST . “the h” matches by prefix 1 shingle from the index FST. “h” matches by prefix 1 shingle from the index FST. |
[2] Solr suggester
hi there!
Thanks for your post – very helpful info that I couldnt find elsewhere! I wonder if you can help me with a problem. I am trying to use FreeTextLookupFactory lookup to provide suggestions that are part of the actual indexed content field. But I keep getting solr errors like:
IllegalArgumentException: tokens must not contain separator byte
Would it be possible for you to provide an example field and type definition that can be used with this? Perhaps my field set up is incorrect. Thanks very much!
Hi Unkwown, unfortunately i missed this comment!
Have your solved your problem ?
What was the solution ?
This kind of suggester is actually not using the field type, but the specific analysis you specify in the suggester conf .
Be careful to the note about the separator, it was a tricky one !
Cheers
Hi, a very good analysis on different suggesters. Can you please explain about 'context filtering' in AnalyzingInfixSuggester. Just curious about how the filtering happens in this case over auxiliary lucene index.
Hey Arsha,
thanks for the comment 🙂
For the context filtering, what happens is we actually add to the auxiliary index data structure, the field we want to filter later on.
Then it is possible to configure a query and filter the results ( suggestions) by the content of that field.
I will take a note and add a deep analysis of the feature in the blog post 🙂
Thanks for the feedback !
http://jirasearch.mikemccandless.com/search.py?index=jira uses context param to uses both AnalyzingInfixSuggester “context” feature, to only show suggestions for the project you've drilled into, and its “payload” feature, to hold the metadata behind each suggestion
Hi Alex, thanks for the detailed information on suggesters with examples. Solr Suggestor Wiki is confusing and misleading – https://cwiki.apache.org/confluence/display/solr/Suggester. They should link to this page on that page.
About getting matches for “Video gamign” using FuzzyLookupFactory, what if we apply analysis on spelling correction of “gamign”, i.e., “gaming” to get stemmed tokens. This way we get results.
Hi Shyamsunder,
you are correct, the context filtering is used in Michael portal 🙂
But what about the “payload” ?
Which metadata are you referring to ? I can see only the title in the suggestions ( but I just quickly played with it)
Cheers
Thank you very much Shyamsunder!
Much appreciated!
Hi Shyamsunder, you mean using an analyzer that performs spell correction ( dictionary based ? ) and then stemming ?
It could be possible.
First we define a TokenFilter that does the spell correction based on a dictionary ( it is actually a good idea, but I think it doesn't exist out of the box).
Then we can specify a stemming token filter, and the game is done.
This is actually a good idea, and can be potentially useful is a number of use cases :
https://issues.apache.org/jira/browse/SOLR-9429
You got it. Thanks for considering my idea.
This comment has been removed by the author.
Hi, in case of AnalyzingInfixSuggester if the auxiliary index build is in progress when “suggest.build = true” will the suggestions work? during this interval?