We are using Solr to store items that have been received and ingested through another service.
I am currently looking into a task to avoid duplicate items being created with the same id.
I am not an expert in Solr and trying pick up the task from someone who has left the company. The last suggestion about how to prevent duplication mentioned that it should be possible using a combination of defining unique id on the id field and using UpdateRequestProcessor chain. I don't know enough about the UpdateRequestProcessor chain to know the approach in mind. I know the ultimate goal was that when an item was sent to Solr with the same id as an existing id then an update would be performed rather than a create.
I have looked at Solr documentation about the UpdateRequestProcessor chain. Without more background information, those resources have not helped that much so far. I think I would benefit from Solr experts to help me get started or pointing me in the right direction.
You don't need to get a URP involved. It is much simpler than that. If your doc's id (defined in schema.xml as <uniqueKey>id</uniqueKey>
) is already an unique id, then, you don't need to do anything else. Indexing the same doc with same id twice will update it the second time (delete and new insert under the hood).
If your uniqueKey is not the unique id, then just rework the schema (and the app using Solr if it needs to), so they match.
Related
My goal is to create a single searchable Azure Index that has all of the relevant information currently stored in many different sql tables.
I'm also using an Azure Cognitive Service to add additional info from related documents. Each document is tied to only a single item in my Index, but each item in the index will be tied to many documents.
According to my understanding, if two documents have the same value for the indexer's Key, then the index will overwrite the extracted information from the first document with the information extracted from the second. I'm hoping there's a way to append the information instead of overwriting it. For example: if two documents relate to the same index item, I want the values mapped to keyphrases for that item to include the keyphrases found in the first document and the keyphrases found in the second document.
Is this possible? Is there a different way I should be approaching this?
If it is possible, can I do it without having duplicate values?
Currently I have multiple indexes and I'm combining the search results from each one, but this seems inefficient and likely messes up the default scoring algorithm.
Every code example I find only has one document for each index item and doesn't address my problem. Admittedly, I haven't tried to set up my index as described above, because it would take a lot of refactoring, and I'm confident it would just overwrite itself.
I am currently creating my indexes and indexers programmatically using dotnet. I'm assuming my code isn't relevant to my question, but I can provide it if need be.
Thank you so much! I'd appreciate any feedback you can give.
Edit: I'm thinking about creating a custom skill to do the aggregation for me, but I don't know how the skill would access access everything it needs. It needs the extracted info from the current document, and it needs the previously aggregated info from previous documents. I guess the custom skill could perform a search on the index and get the item that way, but that sounds dangerously hacky. Any thoughts would be appreciated.
Pasting from docs:
Indexing actions: upload, merge, mergeOrUpload, delete
You can control the type of indexing action on a per-document basis, specifying whether the document should be uploaded in full, merged with existing document content, or deleted.
Whether you use the REST API or an SDK, the following document operations are supported for data import:
Upload, similar to an "upsert" where the document is inserted if it is new, and updated or replaced if it exists. If the document is missing values that the index requires, the document field's value is set to null.
merge updates a document that already exists, and fails a document that cannot be found. Merge replaces existing values. For this reason, be sure to check for collection fields that contain multiple values, such as fields of type Collection(Edm.String). For example, if a tags field starts with a value of ["budget"] and you execute a merge with ["economy", "pool"], the final value of the tags field is ["economy", "pool"]. It won't be ["budget", "economy", "pool"].
mergeOrUpload behaves like merge if the document exists, and upload if the document is new.
delete removes the entire document from the index. If you want to remove an individual field, use merge instead, setting the field in question to null.
I'm currently trying to figure out if Solr is the right tool for me. I have the following setup:
There is the primary document type "blog". Then there are two additional document types "user" and "category". Both of these are parents of the "blog" document type.
Now when searching the "blog" documents, I not only want to search in those fields (e.g. title and content), but also in the parent fields (user>name and category>name.
Of course, I could just flatten that down to a single document for Solr, which would ease the search a lot. The downside to this is though, that when e.g. a user updates their name, I have to run through all blog posts of them and update the documents for that in Solr, instead of just updating a single document.
This becomes even worse when the user has another parent, on which I need to search as well.
Do you have any recommendations about how to handle this use case? Maybe my Google foo is just not good enough, but what I found (block joins, etc.) don't seem to do the trick.
The absolutely most performant and easiest solution would be to flatten everything to a single document. It turns out that these relations aren't updated as often as people think, and that searches are performed more often than the documents update. And even if one of the values that are identical across a large set of documents change, reindexing from the most recent documents (for a blog) and then going backwards will appear rather performant for most users. The assumes that you have to actually search the values and don't just need the values - which you could look up from secondary storage when displaying an item (and just store the never changing id in the document).
Another option is to divide this into a multi-search problem. One collection for blog posts, one collection for users and one collection for categories. You then search through each of the collections for the relevant data and merge it in your search model. You can also use [Streaming Expressions] to hand off most of this processing to a Solr cluster for you.
The reason why I always recommend flattening if possible is that most features in Solr (and Lucene) are written for a flat document structure, and allows you to fully leverage the features available. Since Lucene by design is a flat document store, most other features require special care to support blockjoins and parent/child relationships, and you end up experimenting a lot to get the correct queries and feature set you want (if possible). If the documents are flat, it just works.
I am new to Apache SOLR and I want to implement archiving in SOLR since my data is growing day by day. I am not very sure whether SOLR allows data archiving or not?
If anybody has any suggestions on this then please give it to me.
This question is pretty general so it's a bit hard to give a cut and dried answer, but if one thinks about archiving for a moment, there are two parts to it.
Removing old data
Storing the old data in an alternate location.
The first part is fairly easy in solr so long as you can identify a query that will select the "old" documents. For example if you have a field that records when you sent the data to solr called 'index_date' want to delete everything before Jan 1, 2014 you might do this:
curl http://localhost:8983/solr/update --data '<delete><query>indexed_date:[* TO 2014-01-01T00:00:00]</query></delete>' -H 'Content-type:text/xml; charset=utf-8'
The second part requires more thought. The first question is, why would you want to move the data in solr to some other location. The answer to that more or less has to be because you think you might need it again. But ask yourself what the use case for that is, and you you might service that use case. Are you planning on putting the data back into solr at some later point if you want it? Is solr the only place where this data was stored and you need it for record keeping/audit only?
You will have to determine the second half of "archiving" based on your needs, but here's some things to think about: The data behind fields in solr that are stored="false" are already lost. You can not completely reconstruct the data that went into creating them. Fields for which stored="true" can be retrieved in xml/json/csv with a regular query, and then output to the long term storage of your choice. Many systems use solr as an index into the primary sources rather than using solr as a primary source itself. In this case there may be no need to archive the data, simply remove the data that is too old to be relevant in the search results, but of course make sure that your business team understands and agrees with this strategy before you do it! :)
EDIT: I happened to look back at this and when I re-read it I realized I left something out and there's a new development.
What I Left Out
The above delete by query strategy has the drawback that deleted documents remain in the index (just marked deleted), potentially wasting as much as 50% of your space (or more if you've run "optimize"! in the past). Here's a good article by Eric Erickson about deleting and space consequences:
https://lucidworks.com/2017/10/13/segment-merging-deleted-documents-optimize-may-bad/
New Development
If time is the criteria for deletion and you followed the best practice I mentioned above about not having solr be the single source of truth (i.e. solr is just an index into a primary source, not the data store) then you may very well want to use the new Time Routed Aliases feature which keeps a set of temporally bounded collections and deletes the oldest collections. The great thing about deleting a collection rather than delete by query is that there's no merging to do. The segments for the index disappear as a whole, so there are no deleted documents hanging out wasting space.
http://lucene.apache.org/solr/guide/7_4/time-routed-aliases.html
Self Promotion Disclaimer: Along with David Smiley, I helped write this feature
We have a requirement that documents that we currently index in SOLR may periodically need to be PARTIALLY UPDATED. The updates can either be
a. add new fields
b. update the content of existing fields.
Some of the fields in our schema are stored, others are not.
SOLR 4 does allow this but all the fields must be stored. See Update a new field to existing document and http://solr.pl/en/2012/07/09/solr-4-0-partial-documents-update/
Questions:
1. Is there a way that SOLR can achieve this. We've tried SOLR JOINs in the past but it wasn't the right fit for all our use cases.
On the other hand, can elastic search , linkedin's senseidb or other text search engines achieve this ?
For now, we manage by re-indexing the affected documents when they need to be indexed
Thanks
Solr has the limitation of stored fields, that's correct. The underlying lucene always requires to delete the old document and index the new one. In fact lucene segments are write-once, it never goes back to modify the existing ones, thus it only markes documents as deleted and deletes them for real when a merge happens.
Search servers on top of lucene try to work around this problem by exposing a single endpoint that's able to delete the old document and reindex the new one automatically, but there must be a way to retrieve the old document somehow. Solr can do that only if you store all the fields.
Elasticsearch works around it storing the source documents by default, in a special field called _source. That's exactly the document that you sent to the search engine in the first place, while indexing. This is by the way one of the features that make elasticsearch similar to NoSQL databases. The elasticsearch Update API allows you to update a document in two ways:
Sending a new partial document that will be merged with the existing one (still deleting the old one and indexing the result of the merge
Executing a script on the existing document and indexing the result after deleting the old one
Both options rely on the presence of the _source field. Storing the source can be disabled, if you disable it you of course lose this great feature.
I am going to change some field types in the schema, so seems it must re-index all the docs in current Solr index data with this kind of change.
The question is about how to "re-index" all the docs?
One solution that I can think of is to "query" all docs through the search interface and dump a large file in XML or JSON, then convert it to the input XML format for Solr, and load it back to Solr again to make the schema change happen.
Is there some better way can do this more efficiently? Thanks for your suggestion.
First of all, dumping the results of a query may not give you the original data if you have fields that are indexed and not stored. In general, it is best to keep a copy of the input to SOLR in a form that you can easily use to rebuild indexes from scratch if you need to. In that case, just run a delete query by posting <delete><query>*:*</query></delete> then <commit/> and then <optimize/>. After that your index is empty and you can add new documents that use the new schema.
But you may be able to get away with just running <optimize/> after you restart SOLR with the new schema file. It would be good to have a backup where you can test that it works for your configuration.
There is a tool called Luke that can be used to browse and export Lucene indexes. I have never tried it myself, but it might be able to help you export your data so that you can reimport it.
The idea of dumping all the results of a query could give you incomplete or invalid data since you might not surface all of the data within your index.
While the idea of keeping a copy of your index in a form in which you can re-insert it would work well in a situation where the data doesn't change, it becomes more complicated when you've added a new field to the schema. In such a situation, you'll need to collect all the data from the source, format the data to match the new schema and then insert it.
If the number of documents in the Solr is big and you need to keep Solr server available for querying, the indexing job could be started to re-add/re-index documents in the background.
It is helpful to introduce a new field to keep the lastindexed timestamp per each document, so in the case of any indexing/re-indexing issues, it will be possible to identify waiting for reindexing documents.
To improve the latency of querying, it is possible to play with configurations parameters to keep the caches after every commit.
There is a PHP script that does exactly this: fetch and reinsert all your Solr documents, reindexing them.
For optimizing, call from command line:
curl http://<solr_host>:<port>/solr/<core_name>/update -F stream.body=' <optimize />'