The problem

When documentCache is present in solrconfig.xml after the first time a field is retrieved from Lucene Solr will cache its value along with the document and store it in the documentCache. This can be very expensive, especially for large stored fields – image a situation when you have the documents OCRed from a book and you show the content of the pages. If you don’t reuse such data, so basically a lot hit ration in the documentCache, will result in more garbage produced by Solr itself and thus JVM garbage collector having harder time to clean that up. That can lead to higher CPU usage and worse performance of Solr in general. Let’s look at what we can do with such large, stored fields.

Marking the field as large

Starting with Solr 6.5 we got the ability to add additional property to the field definition, one called large which takes a value of true or false by default being false. Field that we want to mark as large should be set as stored=”true” and multiValued=”false”. In such cases, setting the large=”true” property on the field definition will make the field value not cached inside the documentCache.

Noticing the difference

Because this is a quick look type of post, I don’t want to get into too much specifics, but I would like to compare two collections with the same data. Each collection have the same set of fields:

• id – identifier of the document,
• name – name of the document,
• body – text of the document, which can be very, very large.

One collection will have the large=”true” for the body field and the other won’t have that property set. We will also index a few large documents and see how documentCache behaves.

So here are the commands to setup those two collections using Solr.pl Github account (https://github.com/solrpl/). First setup one collection and gather statistics and then remove all the files, restart Solr, create the second collection and gather statistics. The commands are as follows:

$ mkdir /tmp/solr
$ mkdir /tmp/solr/collection_with_large
$ mkdir /tmp/solr/collection_without_large
$ wget https://github.com/solrpl/blog/tree/master/posts/large_field/data.xml /tmp/solr/data.xml
$ wget https://github.com/solrpl/blog/tree/master/posts/large_field/collection_with_large/managed-schema /tmp/solr/collection_with_large/managed-schema
$ wget https://github.com/solrpl/blog/tree/master/posts/large_field/collection_with_large/solrconfig.xml /tmp/solr/collection_with_large/solrconfig.xml
$ wget https://github.com/solrpl/blog/tree/master/posts/large_field/collection_without_large/managed-schema /tmp/solr/collection_without_large/managed-schema
$ wget https://github.com/solrpl/blog/tree/master/posts/large_field/collection_without_large/solrconfig.xml /tmp/solr/collection_without_large/solrconfig.xml
$ bin/solr zk upconfig -z localhost:9983 -n config_with_large -d /tmp/collection_with_large
$ bin/solr create_collection -c collection_with_large -n config_with_large -shards 1 -replicationFactor 1
$ curl -XPOST 'localhost:8983/solr/collection_with_large/update?commit=true' -H 'Content-Type:application/xml' --data-binary @/tmp/solr/data.xml
$ curl 'localhost:8983/solr/collection_with_large/select?q=*:*'

And now let’s create the second collection using the downloaded data:

$ bin/solr zk upconfig -z localhost:9983 -n config_without_large -d /tmp/collection_without_large
$ bin/solr create_collection -c collection_without_large -n config_without_large -shards 1 -replicationFactor 1
$ curl -XPOST 'localhost:8983/solr/collection_without_large/update?commit=true' -H 'Content-Type:application/xml' --data-binary @/tmp/solr/data.xml
$ curl 'localhost:8983/solr/collection_without_large/select?q=*:*'

And now, let’s check the usage of the documentCache that we’ve gathered. So we have this for the collection with the body field marked as large=”true”:

And we have this for the collection with the body field without the large=”true” property:

As you can see, the field marked with large=”true” was not put into the documentCache directly, but only as a lazy loaded large field, which is what we were aiming for. This means, that we can still use the documentCache and not worry about Solr putting the large, stored fields there, which was the case in the second example.

Assumptions

Assumptions were quite simple – the user should be able to find photos by their file name, author and other data available in EXIF, like aperture, shutter speed, focal length or ISO value. Another thing was that Solr should take care of grabbing the meta data from JPEG files, so this was definitely something we wanted use Solr cell for. As You can see, those assumptions were simple.

Index structure

Index structure was very simple and contained only most needed fields. The fields section of the schema.xml file looked as follows:

<field name="id" type="string" indexed="true" stored="true" required="true" />
<field name="name" type="text" indexed="true" stored="true" />
<field name="author" type="text" indexed="true" stored="true" />
<field name="iso" type="text" indexed="true" stored="true" multiValued="true" />
<field name="iso_string" type="text" indexed="true" stored="true" multiValued="true" />
<field name="aperture" type="double" indexed="true" stored="true" />
<field name="exposure" type="string" indexed="true" stored="true" />
<field name="exposure_time" type="double" indexed="true" stored="true" />
<field name="focal" type="string" indexed="true" stored="true" />
<field name="focal_35" type="string" indexed="true" stored="true" />
<dynamicField name="ignored_*" type="string" indexed="false" stored="false" multiValued="true" />

The dynamic field was added to ignore the data we weren’t interested in. Also the copyField was introduced to copy the iso field value to iso_string field to enable faceting.

Solr configuration

The following handler definition was added to solrconfig.xml file:

<requestHandler name="/update/extract" class="solr.extraction.ExtractingRequestHandler">
 <lst name="defaults">
  <str name="uprefix">ignored_</str>
  <str name="lowernames">true</str>
  <str name="captureAttr">true</str>
  <str name="fmap.stream_name">name</str>
  <str name="fmap.artist">author</str>
  <str name="fmap.exif_isospeedratings">iso</str>
  <str name="fmap.exif_fnumber">aperture</str>
  <str name="fmap.exposure_time">exposure</str>
  <str name="fmap.exif_exposuretime">exposure_time</str>
  <str name="fmap.focal_length">focal</str>
  <str name="fmap.focal_length_35">focal_35</str>
 </lst>
</requestHandler>

A few words about configuration. The uprefix parameter tells Solr which prefix it should use for the fields that were not mentioned explicitly in the handler configuration. In the above case, the fields which were not mentioned will be prefixed with the ignored_ word. That means that they will be matched by the dynamic field and thus they won’t be indexed (stored=”false” and indexed=”false”). The lowernames parameter with the value of true will cause all the field names to be lowercased. The captureAttr parameter tell Solr, to catch file attributes. The next parameters in the above configuration is mapping definition between fields returned by Tika and fields in the index. For example, fmap.exif_fnumber with the value of aperture says Solr to place the value of Tika exif_fnumber in the aperture index field.

Additional, needed libraries

In order for the above configuration to work we need some additional libraries (similar to the ones described in language identification). From the dist directory that is available in Solr distribution we copy the apache-solr-cell-3.5.0.jar file to tikaDir directory that should be created at the same level as the webapps directory in Solr deployment (of course this is an example). Next we add the following like to the solrconfig.xml file:

<lib dir="../tikaLib/" />

The above tell Solr to include all the libraries from the given directory. Next we need to copy all the jar files from the contrib/extraction/ Solr distribution directory to the created tikaDir directory. Additional solrconfig.xml changes are not needed.

Data indexation

The assumptions were, that there will be about 10.000 new photos a week that will need to be indexed. Those photos will be stored in a shared file system location. A simple bash script was responsible for choosing the files that were needed to be indexed and during its work it run the following command for each file:

curl 'http://solrmaster:8983/solr/photos/update/extract?literal.id=9926&commit=true" -F "myfile=@Wisla_2011_10_10.JPG"

The above command sends a file names Wisla_2011_10_10.JPG to /extract handler and says to run commit command after its processing. In addition to that, the unique id of the file is set (the literal.id parameter).

Queries

I addition to some standard filtering by author or other attributes of the photo it was also desired for the search to work. Yeah, just work We decided, that if we were the users of the application, we would like the fields like author or file name to be important. So, we decided to start with the following query:

q=jan+kowalski+wisla&qf=name^100+author^1000+iso+aperture+exposure_time+focal&defType=dismax

As you can see, the query is simple. Two fields in the index are more valuable then others – name of the photo and its author. The value of those fields were set up by adding query time boosts. The rest of the fields are without boost, so the default boost of 1 applies.

To sum up

The described deployment is really simple. The applications works as so the search The next steps that will have to be done is the JVM and Solr tunning. One of the most important things would be looking at the users behavior and tune up searches to make search experience as good as possible. But let’s leave it for other solr.pl post.

Tie

The tie parameter allows one to control how the lower scoring fields affects score for a given word. If we set the tie parameter to a 0.0 value, during the score calculation, only the fields that were scored highest will matter. However if we set it to 0.99 the fields scoring lower will have almost the same impact on the score as the highest scoring field. So let’s check if that actually works.

Data structure and data example

To test how the tie parameter works I’ve chosen a simple index structure which would describe products in e-commerce shop, of course in a simple mode:

<field name="id" type="string" indexed="true" stored="true" required="true" />
<field name="title" type="text_ws" indexed="true" stored="true" />
<field name="description" type="text_ws" indexed="true" stored="true" />
<field name="author" type="text_ws" indexed="true" stored="true" multiValued="true" />

The text_ws type was defined as follows:

<fieldType name="text_ws" class="solr.TextField" positionIncrementGap="100">
 <analyzer>
  <tokenizer class="solr.WhitespaceTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
 </analyzer>
</fieldType>

The example documents look like this:

<add>
 <doc>
  <field name="id">1</field>
  <field name="title">First test book</field>
  <field name="description">This is a description of the first test book by Joe and Jane Blow</field>
  <field name="author">Joe Blow</field>
  <field name="author">Jane Blow</field>
 </doc>
 <doc>
  <field name="id">2</field>
  <field name="title">Second test book</field>
  <field name="description">This is a description of the second test book by Joe Blow</field>
  <field name="author">Joe Blow</field>
 </doc>
</add>

Tie == 0.01 result

Let’s start the test. The first query was the following one:

defType=dismax&qf=title^1000 description author^10&tie=0.01&fl=id,score&debugQuery=on&indent=true&q=joe blow book

The above resulted in the following Solr results (visualization – http://explain.solr.pl/explains/cf0wnkpj):

<?xml version="1.0" encoding="UTF-8"?>
<response>
<lst name="responseHeader">
  <int name="status">0</int>
  <int name="QTime">8</int>
  <lst name="params">
    <str name="fl">id,score</str>
    <str name="debugQuery">on</str>
    <str name="indent">true</str>
    <str name="tie">0.01</str>
    <str name="q">joe blow book</str>
    <str name="qf">title^1000 description author^10</str>
    <str name="defType">dismax</str>
  </lst>
</lst>
<result name="response" numFound="2" start="0" maxScore="0.07342677">
  <doc>
    <float name="score">0.07342677</float>
    <str name="id">2</str>
  </doc>
  <doc>
    <float name="score">0.073365316</float>
    <str name="id">1</str>
  </doc>
</result>
<lst name="debug">
  <str name="rawquerystring">joe blow book</str>
  <str name="querystring">joe blow book</str>
  <str name="parsedquery">+((DisjunctionMaxQuery((author:joe^10.0 | title:joe^1000.0 | description:joe)~0.01) DisjunctionMaxQuery((author:blow^10.0 | title:blow^1000.0 | description:blow)~0.01) DisjunctionMaxQuery((author:book^10.0 | title:book^1000.0 | description:book)~0.01))~3) ()</str>
  <str name="parsedquery_toString">+(((author:joe^10.0 | title:joe^1000.0 | description:joe)~0.01 (author:blow^10.0 | title:blow^1000.0 | description:blow)~0.01 (author:book^10.0 | title:book^1000.0 | description:book)~0.01)~3) ()</str>
  <lst name="explain">
    <str name="2">
0.07342677 = (MATCH) sum of:
  0.07342677 = (MATCH) sum of:
    8.957935E-4 = (MATCH) max plus 0.01 times others of:
      8.9543534E-4 = (MATCH) weight(author:joe^10.0 in 1), product of:
        0.0024097771 = queryWeight(author:joe^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.3715843 = (MATCH) fieldWeight(author:joe in 1), product of:
          1.0 = tf(termFreq(author:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.625 = fieldNorm(field=author, doc=1)
      3.5817415E-5 = (MATCH) weight(description:joe in 1), product of:
        2.4097772E-4 = queryWeight(description:joe), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:joe in 1), product of:
          1.0 = tf(termFreq(description:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=1)
    8.957935E-4 = (MATCH) max plus 0.01 times others of:
      8.9543534E-4 = (MATCH) weight(author:blow^10.0 in 1), product of:
        0.0024097771 = queryWeight(author:blow^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.3715843 = (MATCH) fieldWeight(author:blow in 1), product of:
          1.0 = tf(termFreq(author:blow)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.625 = fieldNorm(field=author, doc=1)
      3.5817415E-5 = (MATCH) weight(description:blow in 1), product of:
        2.4097772E-4 = queryWeight(description:blow), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:blow in 1), product of:
          1.0 = tf(termFreq(description:blow)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=1)
    0.07163518 = (MATCH) max plus 0.01 times others of:
      0.07163482 = (MATCH) weight(title:book^1000.0 in 1), product of:
        0.2409777 = queryWeight(title:book^1000.0), product of:
          1000.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.29726744 = (MATCH) fieldWeight(title:book in 1), product of:
          1.0 = tf(termFreq(title:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=title, doc=1)
      3.5817415E-5 = (MATCH) weight(description:book in 1), product of:
        2.4097772E-4 = queryWeight(description:book), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:book in 1), product of:
          1.0 = tf(termFreq(description:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=1)
</str>
    <str name="1">
0.073365316 = (MATCH) sum of:
  0.073365316 = (MATCH) sum of:
    7.1670645E-4 = (MATCH) max plus 0.01 times others of:
      7.163483E-4 = (MATCH) weight(author:joe^10.0 in 0), product of:
        0.0024097771 = queryWeight(author:joe^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.29726744 = (MATCH) fieldWeight(author:joe in 0), product of:
          1.0 = tf(termFreq(author:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=author, doc=0)
      3.5817415E-5 = (MATCH) weight(description:joe in 0), product of:
        2.4097772E-4 = queryWeight(description:joe), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:joe in 0), product of:
          1.0 = tf(termFreq(description:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=0)
    0.0010134276 = (MATCH) max plus 0.01 times others of:
      0.0010130694 = (MATCH) weight(author:blow^10.0 in 0), product of:
        0.0024097771 = queryWeight(author:blow^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.42039964 = (MATCH) fieldWeight(author:blow in 0), product of:
          1.4142135 = tf(termFreq(author:blow)=2)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=author, doc=0)
      3.5817415E-5 = (MATCH) weight(description:blow in 0), product of:
        2.4097772E-4 = queryWeight(description:blow), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:blow in 0), product of:
          1.0 = tf(termFreq(description:blow)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=0)
    0.07163518 = (MATCH) max plus 0.01 times others of:
      0.07163482 = (MATCH) weight(title:book^1000.0 in 0), product of:
        0.2409777 = queryWeight(title:book^1000.0), product of:
          1000.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.29726744 = (MATCH) fieldWeight(title:book in 0), product of:
          1.0 = tf(termFreq(title:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=title, doc=0)
      3.5817415E-5 = (MATCH) weight(description:book in 0), product of:
        2.4097772E-4 = queryWeight(description:book), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:book in 0), product of:
          1.0 = tf(termFreq(description:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=0)
    </str>
  </lst>
</lst>
</response>

First document

Second document

What can we say about that ?

As you can see, when we passed the 0.01 value to the tie parameter, only those fields that have the highest score for the given query word are most influential. Good example of that behavior is the book word in the first document on the results list. Score for that word is 0.07163518, which was calculated as the sum of the highest scored field (the title field) and the sum of the rest of the fields multiplied by tie.

Tie == 0.99 result

The second query sent to Solr looked as follows:

defType=dismax&qf=title^1000 description author^10&tie=0.99&fl=id,score&debugQuery=on&indent=true&q=joe blow book

Which resulted in the following Solr results: (visualization – http://explain.solr.pl/explains/1w7b06lv):

<?xml version="1.0" encoding="UTF-8"?>
<response>
<lst name="responseHeader">
  <int name="status">0</int>
  <int name="QTime">15</int>
  <lst name="params">
    <str name="fl">id,score</str>
    <str name="debugQuery">on</str>
    <str name="indent">true</str>
    <str name="tie">0.99</str>
    <str name="q">joe blow book</str>
    <str name="qf">title^1000 description author^10</str>
    <str name="defType">dismax</str>
  </lst>
</lst>
<result name="response" numFound="2" start="0" maxScore="0.07352995">
  <doc>
    <float name="score">0.07352995</float>
    <str name="id">2</str>
  </doc>
  <doc>
    <float name="score">0.0734685</float>
    <str name="id">1</str>
  </doc>
</result>
<lst name="debug">
  <str name="rawquerystring">joe blow book</str>
  <str name="querystring">joe blow book</str>
  <str name="parsedquery">+((DisjunctionMaxQuery((author:joe^10.0 | title:joe^1000.0 | description:joe)~0.99) DisjunctionMaxQuery((author:blow^10.0 | title:blow^1000.0 | description:blow)~0.99) DisjunctionMaxQuery((author:book^10.0 | title:book^1000.0 | description:book)~0.99))~3) ()</str>
  <str name="parsedquery_toString">+(((author:joe^10.0 | title:joe^1000.0 | description:joe)~0.99 (author:blow^10.0 | title:blow^1000.0 | description:blow)~0.99 (author:book^10.0 | title:book^1000.0 | description:book)~0.99)~3) ()</str>
  <lst name="explain">
    <str name="2">
0.07352995 = (MATCH) sum of:
  0.07352995 = (MATCH) sum of:
    9.308678E-4 = (MATCH) max plus 0.99 times others of:
      8.9540955E-4 = (MATCH) weight(author:joe^10.0 in 1), product of:
        0.0024097078 = queryWeight(author:joe^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.3715843 = (MATCH) fieldWeight(author:joe in 1), product of:
          1.0 = tf(termFreq(author:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.625 = fieldNorm(field=author, doc=1)
      3.581638E-5 = (MATCH) weight(description:joe in 1), product of:
        2.4097077E-4 = queryWeight(description:joe), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:joe in 1), product of:
          1.0 = tf(termFreq(description:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=1)
    9.308678E-4 = (MATCH) max plus 0.99 times others of:
      8.9540955E-4 = (MATCH) weight(author:blow^10.0 in 1), product of:
        0.0024097078 = queryWeight(author:blow^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.3715843 = (MATCH) fieldWeight(author:blow in 1), product of:
          1.0 = tf(termFreq(author:blow)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.625 = fieldNorm(field=author, doc=1)
      3.581638E-5 = (MATCH) weight(description:blow in 1), product of:
        2.4097077E-4 = queryWeight(description:blow), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:blow in 1), product of:
          1.0 = tf(termFreq(description:blow)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=1)
    0.071668215 = (MATCH) max plus 0.99 times others of:
      0.07163276 = (MATCH) weight(title:book^1000.0 in 1), product of:
        0.24097076 = queryWeight(title:book^1000.0), product of:
          1000.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.29726744 = (MATCH) fieldWeight(title:book in 1), product of:
          1.0 = tf(termFreq(title:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=title, doc=1)
      3.581638E-5 = (MATCH) weight(description:book in 1), product of:
        2.4097077E-4 = queryWeight(description:book), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:book in 1), product of:
          1.0 = tf(termFreq(description:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=1)
</str>
    <str name="1">
0.0734685 = (MATCH) sum of:
  0.0734685 = (MATCH) sum of:
    7.517859E-4 = (MATCH) max plus 0.99 times others of:
      7.1632763E-4 = (MATCH) weight(author:joe^10.0 in 0), product of:
        0.0024097078 = queryWeight(author:joe^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.29726744 = (MATCH) fieldWeight(author:joe in 0), product of:
          1.0 = tf(termFreq(author:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=author, doc=0)
      3.581638E-5 = (MATCH) weight(description:joe in 0), product of:
        2.4097077E-4 = queryWeight(description:joe), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:joe in 0), product of:
          1.0 = tf(termFreq(description:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=0)
    0.0010484984 = (MATCH) max plus 0.99 times others of:
      0.0010130403 = (MATCH) weight(author:blow^10.0 in 0), product of:
        0.0024097078 = queryWeight(author:blow^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.42039964 = (MATCH) fieldWeight(author:blow in 0), product of:
          1.4142135 = tf(termFreq(author:blow)=2)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=author, doc=0)
      3.581638E-5 = (MATCH) weight(description:blow in 0), product of:
        2.4097077E-4 = queryWeight(description:blow), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:blow in 0), product of:
          1.0 = tf(termFreq(description:blow)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=0)
    0.071668215 = (MATCH) max plus 0.99 times others of:
      0.07163276 = (MATCH) weight(title:book^1000.0 in 0), product of:
        0.24097076 = queryWeight(title:book^1000.0), product of:
          1000.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.29726744 = (MATCH) fieldWeight(title:book in 0), product of:
          1.0 = tf(termFreq(title:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=title, doc=0)
      3.581638E-5 = (MATCH) weight(description:book in 0), product of:
        2.4097077E-4 = queryWeight(description:book), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:book in 0), product of:
          1.0 = tf(termFreq(description:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=0)
    </str>
  </lst>
</lst>
</response>

First document

Second document

What can we say about that ?

As you can see the score of the result documents changed. Let’s have a look at the same document and the same book word. In the case, we sent 0.99 as the value of the tie parameter and the score value of that word increased comparing to the score when using tie of 0.01. Of course, the change is not only because the tie parameter but also because of normalization, but let’s forget about it for things to be simple So, in the second case, we see the score of 0.071668215, which is the score of the title field summed with the score of the other fields multiplied by 0.99 (tie parameter value).

To sum up

As you can see, the tie parameter allows us, to control how the score is calculated for the DisjunctionMaxQuery. In extreme cases, when we only want the highest scored fields to contribute to the total score we can set the tie parameter to 0.0. Tie lets us control, how we want the low scoring fields to be treated, when score of the documents is calculated and thus where they are on the results list we get from Solr when using Dismax query parser.

In case you are wodering

In case you are wondering what we used to show you the diagrams – please go to http://explain.solr.pl/help and see, maybe http://explain.solr.pl/ may be helpful in Your case.