Hi Vinayak, I am not sure how to improve this query further through gremlin. Query can be faster through data model. VCI will be helpful, if you are applying any other filter along with hasLabel and

Hi All, I'm trying to run index repair job through mapreduce in a Kerberos+SSL enabled cluster. I have added all required hbase and hadoop configurations but getting the below exception in mapreduce

Hi Alessandro, The executors tab of the spark UI shows the product of spark.executor.instances times spark.executor.cores. I guess spark.executor.instances defaults to one and EMR might limit the

By the way, if you have any properties file or running example of OLAP that you would like to share, I'd be happy to see something working and compare it to what I am trying to do! Best

Hi, here are the properties that I am setting so far (plus the same ones that are set in the TinkerPop example, such as the classpath for the executors and the driver): On Spark UI I can see a number

Hi Alessandro, Yes, please include the properties file. To be clear, you see in the spark web UI: spark.master=yarn spark.executor.instances=12 and only two executors for 700+ tasks show up, while

Hi Amiya, Thank you for the query. It also increased the performance. But, it's still 35 seconds. Is there any other way to optimize it further, there are only 10 records returned by the query.

Hi Vinayak, You can try below query, it can use index and combine as many traversals you want. g.inject(1). union ( V().has('title', 'V1'). outE().hasLabel('E1').inV().has('title', 'V2'),

Hi, cmilowka, The property title has a composite index created on it. Further modified the query as follows: g.V().has('title',within('V1','V2')).union(has('title',

I guess, building composite index for 'title' property will do the job of accessing title(V1) and title(V2) fast, without full scan of DB as currently does. cheers, CM

Hi Alessandro, I'm also working through a similar use-case with AWS EMR, but I'm running into some Hadoop class errors. What version of EMR are you using? Additionally, if you could pass along the

Hi Marc, the Tinkerpop example works correctly. We are actually using Scylla, and with 256 tokens per node I am getting 768 tasks in the Spark job (which I correctly see listed in the UI). The

Hi All, I need to select multiple nodes and edges and display the content in v1 - e - v2 format. The query generated is as follows: g.V().union(has('title',

Hi Fredrick! First of all, thanks for your contribution! How did you try to squash the commits? In the PR, I still see a total of eight commits so it appears your squash didn't work for some

Hi Claire In our earlier attempts to work with JG OLAP, we hit the bottleneck / bounded by scylla virtual token ring count (it appears thats the unit of parallelism while bulk export) Can you please

Also the https://github.com/JanusGraph/janusgraph/blob/master/CONTRIBUTING.md is probably out of date. It recommends getting help from... ...but that group appears to no longer exist. I presume that

I have a pull request which contains commits which have not been properly signed. I have tried to squash the commits and have made things worse. Here is a link to the pull

Hi Alessandro, I assume Amazon EMR uses hadoop-yarn, so you need to specify spark.master = yarn, see: https://tinkerpop.apache.org/docs/current/recipes/#olap-spark-yarn Once you can run the TinkerPop

Hello everyone, is there anyone with experience of running OLAP on an AWS EMR cluster? I am currently trying to do so, but strange things are happening. The first one is that the application is not

Hi Marc, problem is that Janusgraph uses tokenised field for "neq" comparisons and non tokenised for "eq". For example for a property "x" tokenised field in ES will have the name "x" and