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I need to only display information from the person doc but each query should match fields in both person and subject. In the case the query matches only the subject doc I need to display all docs from the person that have a matching id. Is this possible to do without running two seperate queries?
Something like a JOIN query would do the job. One thing that you should keep in mind is to always think of Solr indexes as single denormalized tables. This is sometimes a challenge and there may be times where you must be forced to use different indexes for each kind of data. This will have pretty poor performance if there are a large number of id's, but should be fine if there are just a few matching ids.
If you anticipate that your "join" queries will generally match a lot say hundreds of subjects, then you're probably better off denormalizing as suggested.
Learn more. Asked 10 years, 6 months ago. Active 9 years, 9 months ago. Viewed 10k times. Any help?
Apache Solr and Joins
Sfairas Sfairas 4 4 gold badges 11 11 silver badges 20 20 bronze badges. Active Oldest Votes. I do not think it is possible to do what you are asking with a single query using your schema. Pascal Dimassimo Pascal Dimassimo 6, 1 1 gold badge 33 33 silver badges 33 33 bronze badges.
Thanks very much Pascal. I don't know about changing the schema really. We've got some quite big XML files to index about 4 each one with it's own schema having IDs that connect one another.One reason for using nested documents is to prevent false matches. But if we represented the SKUs as two different documents, then there would be no incorrect match.
All children of a parent document must be indexed together with the parent document.
One cannot update any document parent or child individually. The entire block needs to be re-indexed of any changes need to be made. Any document can have nested child documents. The locality of children and parents can be used to both speed up query operations and lower memory requirements compared to other join methods. Also see the Solr ref guide entry on the [child] doc transformer. Since our root implicit facet bucket formed by the query and filters consists of parent documents bookswe need to switch the facet domain to the children for the author facet.
By default, blockChildren will match all children of every parent doc from the input domain. The easiest way to limit children is with the filter clause. Note that regardless of which direction we are mapping parents to children or children to parentsor what documents we are operating on, we provide a parent filter to define the complete set of parents in the index.In addition to the main query parsers discussed earlier, there are several other query parsers that can be used instead of or in conjunction with the main parsers for specific purposes.
Many of these parsers are expressed the same way as Local Parameters in Queries. There are two query parsers that support block joins. These parsers allow indexing and searching for relational content that has been indexed as nested documents. The example usage of the query parsers below assumes these two documents and each of their child documents have been indexed:. The parameter allParents is a filter that matches only parent documents ; here you would define the field and value that you used to identify all parent documents.
The parameter someParents identifies a query that will match some of the parent documents. The output is the children.
We only get one document in response:. Note that the query for someParents should match only parent documents passed by allParents or you may get an exception:. The parameter someChildren is a query that matches some or all of the child documents. Note that the query for someChildren should match only child documents or you may get an exception:.
We get this document in response:. A common mistake is to try to filter parents with a which filter, as in this bad example:.
You can optionally use the score local parameter to return scores of the subordinate query. The values to use for this parameter define the type of aggregation, which are avg averagemax maximummin minimumtotal sum.
Implicit default is none which returns 0. The main value is the query to be boosted. Parameter b is the function query to use as the boost. The query to be boosted may be of any type. Creates a query "foo" which is boosted scores are multiplied by the function query log popularity :. Creates a query "foo" which is boosted by the date boosting function referenced in ReciprocalFloatFunction :. This parser collapses the result set to a single document per group before it forwards the result set to the rest of the search components.
So all downstream components faceting, highlighting, etc. Under the covers, this query parser makes use of the Span group of queries, e. Set to true to force phrase queries to match terms in the order specified.
Default: true. Performance is sensitive to the number of unique terms that are associated with a pattern. It may be prudent to restrict wildcards to at least two or preferably three letters as a prefix.
Allowing very short prefixes may result in to many low-quality documents being returned. Applying ReversedWildcardFilterFactory in index-time analysis is usually a good idea. You may need to increase MaxBooleanClauses in solrconfig. This property is described in more detail in the section Query Sizing and Warming. Lets say we add the terms theupand to to stopwords. The next query that does use the Complex Phrase Query Parser, as in this query:. If you must remove stopwords for your use case, use a custom filter factory or perhaps a customized synonyms filter that reduces given stopwords to some impossible token.
Special care has to be given when escaping: clauses between double quotes usually whole query is parsed twice, these parts have to be escaped as twice. The FieldQParser extends the QParserPlugin and creates a field query from the input value, applying text analysis and constructing a phrase query if appropriate.Most of the technical details are covered in this talk by Martijn van Groningen.
I have a single-segment 55 GBindex with 27 M docs -- about a million parent documents, each with five children. I used SolrMeter with a slightly modified RandomExecutorwhich tries to keep a specified rate of queries per time period. It also provides several useful statistics and charts. In addition, I attached iostat traces to show system load during tests. Query Result Cache and Filter Cache have been disabled. Document Cache is enabled and shows a hit ratio of about 0.
See more about these Solr bolts and nuts.
You can see that Join almost never ran for less than a second, and the CPU saturated with requests per minute. Adding more queries harmed latency. All index was cached in RAM via memory mapped files magic. I used Sen for the same queries with blockjoin. You see it! Search now takes only a few tens of milliseconds and survives with 6K requests per minute qps.
And you see plenty of free CPU! We can check where Join uses so much CPU power with jstack :. How could that be? I ran two tests to understand how cache index files impact performance. You should know that not all files in your index are equally valuable. In other words, tune your schema wisely. In my index the frq file is 7.We recently had a client who wanted some up-front sense of how Apache Solr provided join support and, if so, how it performed. Naturally, the client wanted to use a join query in the most painful way, so I set out to make a prototype.
Of course I ran into some issues, but one of the delights of working for Lucidworks is that I have ready access to many of the people who wrote the code, something to treasure! Being able to access these folks makes me look waaaay smarter than I am….
Anyway, on my MacBook Pro I ran some rather unscientific experiments, but enough to give me a sense of how join query performs in one particular case. For this experiment, I created an index consisting of 26M documents. They were divided up into groups, one text document and 5 metadata documents. The metadata documents also had an integer field in the rangeThe whole purpose of this setup was to form queries that returned the text docs for which a metadata doc existed granting access.
The complexity of granting access is…er…low, I just did a range query. I could configure the number of simultaneous threads firing off queries. Note that I was testing this form because it applied to the customer, but I suspect that the other forms have the same issue.
As I mentioned, one of the pleasures of working for Lucid is having access to people who deeply understand the code. So I chatted with the join author Yonik Seeley and discovered, of course, that the scenario I was testing was the worst performance wise. So these results are worst-case. A note about these rather counter-intuitive numbers. On a dual-core machine, we see that with 2 threads. The 5 and 10 thread client rows simply show that each individual request takes longer, end-to-end, but there are more queries being served by Solr simultaneously.
When I took the join part out, performance went up about 15x. I was monitoring the CPU, and it was pegged with 2 threads, which makes sense. These numbers, assuming that they are representative of your particular situation could well be killers. On the other hand, they may be fine if your particular situation is serving a small community of users for whom the time spent waiting for a query to return is well-spent.
It might also mean that the case that Solr join functionality was meant to solve takes an unnecessarily restrictive approach for this particular problem.A free add-on for web browser Firefox, Split Panel lets you split browser windows in half, so you can see two web pages at once. This way, you can copy and paste answers at speed. Installing it is simple. If you don't already have Firefox, head to mozilla. Then go to Split Panel and hit the 'add to Firefox' button on the left.
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High-performance Join in Solr with BlockJoinQuery
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Nested Objects in Solr
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