Great point Wayne,

Also apoc.warmup.run(true) also load properties (but I personally don't use
that property-loading a lot, more structural warm-up).
Better to run your queries (with parameters!!) as warm-up.

Michael

On Sat, Jul 1, 2017 at 12:39 PM, Michael Hunger <
[email protected]> wrote:

> It all *depends* on your queries.
>
> Neo4j Enterprise has the compiled Cypher runtime, which depending on the
> query can be 3-5 times faster.
> It also has a new label index implementation which also speeds things up.
>
> Without query examples / profile / data model etc. I can't give any
> predictions.
>
> Michael
>
>
> On Sat, Jul 1, 2017 at 12:21 PM, unrealadmin23 via Neo4j <
> [email protected]> wrote:
>
>>
>> Before queries, I try and warm things up before performing any timings:
>>
>> neo4j> call apoc.warmup.run() ;
>>
>> and
>>
>> neo4j> match (n:Entity) with n.name as name return count(*);
>>
>> Michael,    how much faster in real terms then is neo4j 3.2.1 over 3.2.0
>> Enterprise and for which operations?
>>
>>
>>
>> On Saturday, 1 July 2017 01:41:03 UTC+1, Michael Hunger wrote:
>>
>>> *What does your query look like?*
>>>
>>> *How do you do this: "the query is threaded to use all cpu." ?*
>>>
>>> If it has to scan the whole dataset, depending on your memory config it
>>> has to first load the data into memory, where you measure the performance
>>> of your IO.
>>> If the database is larger than memory it has to discard data and reload
>>> it again which affects this again massively.
>>>
>>> Did you configure the page-cache in your neo4j.conf according to
>>> database size? And set the heap to e.g. 16 or 32G ? Larger heaps shouldn't
>>> make a difference.
>>> *Page-Cache is what counts most*.
>>>
>>> Which Neo4j version are you using? I recommend 3.2.1 Enterprise which
>>> comes for instance with compiled cypher runtime.
>>>
>>> Michael
>>>
>>>
>>>
>>> On Fri, Jun 30, 2017 at 10:10 PM, Patrice Loos <[email protected]> wrote:
>>>
>>>> I am testing a java query on different size dataset, 100 Million to 1
>>>> Billion edges.
>>>> The query does not return much data 10 to 20 vertices with
>>>> corresponding edges but it need to scan the whole dataset.
>>>> I can see a big performances degradation when the database size is
>>>> bigger than 32 Gigs.
>>>> I am running the test on a 32 core 244G RAM virtual server, the query
>>>> is threaded to use all cpu.
>>>> I changed the java heap size to 96G and played with the garbage
>>>> collector options (retain -XX:+UseG1GC as the most improving option)
>>>> to get a better outcome but I still get big dip in performances, I
>>>> assumed the threshold is around 32G:
>>>>
>>>> 100M edges, database is 7.5G : 12 min
>>>> 250M edges, database is 19G : 35 min
>>>> 500M edges, database is 38G : 12 hours with -XX:+UseG1GC
>>>> 1B edges, database is 76G : 51 hours without -XX:+UseG1GC
>>>>
>>>> Furthermore for the 0.5 Billion and 1 Billion test I can see that the
>>>> bulk of the operations are system operations 60% versus
>>>> user operation 40% (from top linux command). When I run the smaller
>>>> test 100% of the operations are user operations.
>>>>
>>>> Are the java GC improvement in the Enterprise edition of Neo4j
>>>> significant enough to bring the performance of the large scale dataset
>>>> query in the same range as the smaller one?
>>>> Is there something else I can do to improve the performance of larger
>>>> dataset queries?
>>>>
>>>> tks
>>>> Patrice
>>>>
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>>>
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