Unfortunately, adding edges per update script will soon become expensive. Note, updating is a multistep process of reading the doc, looking up the field (often by fetching _source), and reindexing the whole(!) document (not only the new edge) plus the versioning conflict management in case you run concurrent updates. Also, this is the same procedure for removing an edge. This is a huge difference to graph algorithms, where it is very cheap to add/remove edges. Script updates will work to a certain extent quite satisfactory, but you are right, if you want to add millions of edges to an ES doc one by one, this will not be efficient.
So I would like to suggest to avoid the overhead of updating fields by script in preference to add / remove relations by their "relation id", i.e. to treat relations as first citizen docs. Adding millions of docs to an ES index is cheaper than a million scripted updates on a single field. Jörg On Tue, Oct 7, 2014 at 1:23 AM, Todd Nine <[email protected]> wrote: > Hi Jorg, > Thanks for the response. I don't actually need to model the > relationship per se, more that a document is used in a relationship via a > filter, then search on it's properties. See the example below for more > clarity. > > > Restaurant: => {name: "duo"} > > Now, lets say I have 3 users, > > George, Dave and Rod > > George Dave and Rod all "like" the restaurant Duo. These are directed > edges from the user, of type "likes" to the "duo" document. We store these > edges in Cassandra. Envision the document looking something like this. > > > { > name: "duo", > openTime: 9, > closeTime: 18 > _in_edges: [ "george/likes", "dave/likes", "rod/likes" ] > } > > Then when searching, the user Dave would search something like this. > > select * where closeTime < 16 > > > Which we translate in to a query, which is then also filtered by _in_edges > = "dave/likes". > > Our goal is to only create 1 document per node in our graph (in this > example restaurant), then possibly use the scripting API to add and remove > elements to the _in_edges fields and update the document. My only concern > around this is document size. It's not clear to me how to go about this > when we start getting millions of edges to that same target node, or > _in_edges field could grow to be millions of fields long. At that point, > is it more efficient to de-normalize and just turn "dave/likes", > "rod/likes", and "george/likes" into document types and store multiple > copies? > > Thanks, > Todd > > > > > > > > > On Sat, Oct 4, 2014 at 2:52 AM, [email protected] < > [email protected]> wrote: > >> Not sure if this helps but I use a variant of graphs in ES, it is called >> Linked Data (JSON-LD) >> >> By using JSON-LD, you can index something like >> >> doc index: graph >> doc type: relations >> doc id: ... >> >> { >> "user" : { >> "id" : "...", >> "label" : "Bob", >> "likes" : "restaurant:Duo" >> } >> } >> >> for the statement "Bob likes restaurant Duo" >> >> and then you can run ES queries on the field "likes" or better >> "user.likes" for finding the users that like a restaurant etc. Referencing >> the "id" it is possible to lookup another document in another index about >> "Bob". >> >> Just to give an idea how you can model relations in structured ES JSON >> objects. >> >> Jörg >> >> >> On Fri, Oct 3, 2014 at 7:59 PM, Todd Nine <[email protected]> wrote: >> >>> So clearly I need to RTFM. I missed this in the documentation the first >>> time. >>> >>> >>> http://www.elasticsearch.org/guide/en/elasticsearch/guide/current/mapping.html#_how_types_are_implemented >>> >>> Will filters at this scale be fast enough? >>> >>> >>> >>> On Friday, October 3, 2014 11:48:40 AM UTC-6, Todd Nine wrote: >>>> >>>> Hey guys, >>>> We're currently storing entities and edges in Cassandra. The >>>> entities are JSON, and edges are directed edges with a >>>> source---type-->target. We're using ElasticSearch for indexing and I could >>>> really use a hand with design. >>>> >>>> What we're doing currently, is we take an entity, and turn it's JSON >>>> into a document. We then create multiple copies of our document and change >>>> it's type to match the index. For instance, Image the following use case. >>>> >>>> >>>> bob(user) -- likes -- > Duo (restaurant) ===> Document Type = >>>> bob(user) + likes + restaurant ; bob(user) + likes >>>> >>>> >>>> bob(user) -- likes -> Root Down (restaurant) ===> Document Type = >>>> bob(user) + likes+ restaurant ; bob(user) + likes >>>> >>>> bob(user) -- likes --> Coconut Porter (beer). ===> Document Types = >>>> bob(user) + likes + beer; bob(user) + likes >>>> >>>> >>>> When we index using this scheme we create 3 documents based on the >>>> restaurants Duo and Root Down, and the beer Coconut Porter. We then store >>>> this document 2x, one for it's specific type, and one in the "all" bucket. >>>> >>>> Essentially, the document becomes a node in the graph. For each >>>> incoming directed edge, we're storing 2x documents and changing the type. >>>> This gives us fast seeks when we search by type, but a LOT of data bloat. >>>> Would it instead be more efficient to keep an array of incoming edges in >>>> the document, then add it to our search terms? For instance, should we >>>> instead have a document like this? >>>> >>>> >>>> docId: Duo(restaurant) >>>> >>>> edges: [ "bob(user) + likes + restaurant", "bob(user) + likes" ] >>>> >>>> When searching where edges = "bob(user) + likes + restaurant"? >>>> >>>> >>>> I don't know internally what specifying type actually does, if it just >>>> treats it as as field, or if it changes the routing of the response? In >>>> a social situation millions of people can be connected to any one entity, >>>> so we have to have a scheme that won't fall over when we get to that case. >>>> >>>> Any help would be greatly appreciated! >>>> >>>> Thanks, >>>> Todd >>>> >>> -- >>> You received this message because you are subscribed to the Google >>> Groups "elasticsearch" group. >>> To unsubscribe from this group and stop receiving emails from it, send >>> an email to [email protected]. >>> To view this discussion on the web visit >>> https://groups.google.com/d/msgid/elasticsearch/f97c6475-f4fc-4078-b052-b497ac82dc91%40googlegroups.com >>> <https://groups.google.com/d/msgid/elasticsearch/f97c6475-f4fc-4078-b052-b497ac82dc91%40googlegroups.com?utm_medium=email&utm_source=footer> >>> . >>> >>> For more options, visit https://groups.google.com/d/optout. >>> >> >> -- >> You received this message because you are subscribed to a topic in the >> Google Groups "elasticsearch" group. >> To unsubscribe from this topic, visit >> https://groups.google.com/d/topic/elasticsearch/wtKQYcpb1-A/unsubscribe. >> To unsubscribe from this group and all its topics, send an email to >> [email protected]. >> To view this discussion on the web visit >> https://groups.google.com/d/msgid/elasticsearch/CAKdsXoF0jKYVLKNV7RDjTCqsKnzjQmjZb%2BxBpkkGPa3YAHfM6A%40mail.gmail.com >> <https://groups.google.com/d/msgid/elasticsearch/CAKdsXoF0jKYVLKNV7RDjTCqsKnzjQmjZb%2BxBpkkGPa3YAHfM6A%40mail.gmail.com?utm_medium=email&utm_source=footer> >> . >> >> For more options, visit https://groups.google.com/d/optout. >> > > -- > You received this message because you are subscribed to the Google Groups > "elasticsearch" group. > To unsubscribe from this group and stop receiving emails from it, send an > email to [email protected]. > To view this discussion on the web visit > https://groups.google.com/d/msgid/elasticsearch/CA%2Byzqf9pw2YMtFDqjcH3QejL%3DF04dZVUaw1j5Jt8Nrd%3DxX3ZPw%40mail.gmail.com > <https://groups.google.com/d/msgid/elasticsearch/CA%2Byzqf9pw2YMtFDqjcH3QejL%3DF04dZVUaw1j5Jt8Nrd%3DxX3ZPw%40mail.gmail.com?utm_medium=email&utm_source=footer> > . > > For more options, visit https://groups.google.com/d/optout. > -- You received this message because you are subscribed to the Google Groups "elasticsearch" group. 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