For my thesis I am going to test the scalability/performance of the 
Elasticsearch cluster depending on the number of nodes.

I have a physical server (with 8 cores, 30 GB memory) and on that physical 
server I create* 4 virtual machines*, each assigned with 2 cores and 4GB of 
memory.
On each virtual machine I install Elasticsearch, therefore each machine 
represents a separate node in my Elasticsearch cluster.
The data contains* about 7 million documents*, I have* 5 shards and 
replication_count=1 (default configuration)*.
I am executing a set of tests on the cluster with different 
cluster-configurations. At first I assign only 1 node to the cluster and 
execute the tests, then I assign 2 nodes to the cluster and execute the 
same tests again, then 3 nodes etc.
The tests do not contain any fancy features (no facets, highlighting, etc), 
but only filters and queries.

I expected that for each additional node in the cluster I would get a 
linear improvement of the query response time. Because each node contains a 
bit of the data (shard) and so the query could be executed on each shard at 
the same time. 
But it turns out that the response time of the query is not improving nor 
is it getting worse, *it almost stays the same*....

So my question: 
Are these results expected, that *the query response time does NOT improve 
when adding an additional node?*

I execute the test queries one after another and not in parallel. Is 
Elasticsearch made just to handle a high load of requests, but not for 
improving the query time of a single request when I add additional nodes to 
the cluster?
I mean, would I see only an improvement for each additional node, when I 
would run the tests in _parallel_, because then the query load is 
distributed?


Regards,
Herbert

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