Re: ingest performance degrades sharply along with the documents having more fileds
Hi Kimchy,
I rerun the benchmark using ES1.3 with default settings (just disable the
_source _all ) and it makes a great progress on the performance. However
Solr still outperforms ES 1.3:
Number of different meta data field
ES
ES with disable _all/codec bloom filter
*ES 1.3 *
Solr
Scenario 0: 1000
12secs - *833*docs/sec
CPU: 30.24%
Heap: 1.08G
time(secs) for each 1k docs:3 1 1 1 1 1 0 1 2 1
*index size: 36Mb*
iowait: 0.02%
13 secs -769 docs/sec
CPU: 23.68%
iowait: 0.01%
Heap: 1.31G
Index Size: 248K
Ingestion speed change: 2 1 1 1 1 1 1 1 2 1
13 secs-769 docs/sec
CPU: 44.22%
iowait: 0.01%
Heap: 1.38G
Index Size: 69M
Ingestion speed change: 2 1 1 1 1 1 2 0 2 2
13 secs - 769 docs/sec
CPU: 28.85%
Heap: 9.39G
time(secs) for each 1k docs: 2 1 1 1 1 1 1 1 2 2
Scenario 1: 10k
29secs - *345*docs/sec
CPU: 40.83%
Heap: 5.74G
time(secs) for each 1k docs:14 2 2 2 1 2 2 1 2 1
iowait: 0.02%
*Index Size: 36Mb*
31 secs - 322.6 docs/sec
CPU: 39.29%
iowait: 0.01%
Heap: 4.76G
Index Size: 396K
Ingestion speed change: 12 1 2 1 1 1 2 1 4 2
20 secs-500 docs/sec
CPU: 54.74%
iowait: 0.02%
Heap: 3.06G
Index Size: 133M
Ingestion speed change: 2 2 1 2 2 3 2 2 2 1
12 secs - 833 docs/sec
CPU: 28.62%
Heap: 9.88G
time(secs) for each 1k docs:1 1 1 1 2 1 1 1 1 2
Scenario 2: 100k
17 mins 44 secs - *9.4*docs/sec
CPU: 54.73%
Heap: 47.99G
time(secs) for each 1k docs:97 183 196 147 109 89 87 49 66 40
iowait: 0.02%
*Index Size: 75Mb*
14 mins 24 secs - 11.6 docs/sec
CPU: 52.30%
iowait: 0.02%
Heap:
Index Size: 1.5M
Ingestion speed change: 93 153 151 112 84 65 61 53 51 41
1 mins 24 secs- 119 docs/sec
CPU: 47.67%
iowait: 0.12%
Heap: 8.66G
Index Size: 163M
Ingestion speed change: 9 14 12 12 8 8 5 7 5 4
13 secs - 769 docs/sec
CPU: 29.43%
Heap: 9.84G
time(secs) for each 1k docs:2 1 1 1 1 1 1 1 2 2
Scenario 3: 1M
183 mins 8 secs - *0.9* docs/sec
CPU: 40.47%
Heap: 47.99G
time(secs) for each 1k docs:133 422 701 958 989 1322 1622 1615 1630 1594
11 mins 9 secs-15docs/sec
CPU: 41.45%
iowait: 0.07%
Heap: 36.12G
Index Size: 163M
Ingestion speed change: 12 24 38 55 70 86 106 117 83 78
15 secs - 666.7 docs/sec
CPU: 45.10%
Heap: 9.64G
time(secs) for each 1k docs:2 1 1 1 1 2 1 1 3 2
Best Regards
Maco
On Saturday, July 5, 2014 11:46:59 PM UTC+8, kimchy wrote:
Heya, I worked a bit on it, and 1.x (upcoming 1.3) has some significant
perf improvements now for this case (including improvements Lucene wise,
that are for now in ES, but will be in Lucene next version). Those include:
6648: https://github.com/elasticsearch/elasticsearch/pull/6648
6714: https://github.com/elasticsearch/elasticsearch/pull/6714
6707: https://github.com/elasticsearch/elasticsearch/pull/6707
It would be interesting if you can run the tests again with 1.x branch.
Note, also, please use default features in ES for now, no disable flushing
and such.
On Friday, June 13, 2014 7:57:23 AM UTC+2, Maco Ma wrote:
I try to measure the performance of ingesting the documents having lots
of fields.
The latest elasticsearch 1.2.1:
Total docs count: 10k (a small set definitely)
ES_HEAP_SIZE: 48G
settings:
{doc:{settings:{index:{uuid:LiWHzE5uQrinYW1wW4E3nA,number_of_replicas:0,translog:{disable_flush:true},number_of_shards:5,refresh_interval:-1,version:{created:1020199}
mappings:
{doc:{mappings:{type:{dynamic_templates:[{t1:{mapping:{store:false,norms:{enabled:false},type:string},match:*_ss}},{t2:{mapping:{store:false,type:date},match:*_dt}},{t3:{mapping:{store:false,type:integer},match:*_i}}],_source:{enabled:false},properties:{}
All fields in the documents mach the templates in the mappings.
Since I disabled the flush refresh, I submitted the flush command
(along with optimize command after it) in the client program every 10
seconds. (I tried the another interval 10mins and got the similar results)
Scenario 0 - 10k docs have 1000 different fields:
Ingestion took 12 secs. Only 1.08G heap mem is used(only states the used
heap memory).
Scenario 1 - 10k docs have 10k different fields(10 times fields compared
with scenario0):
This time ingestion took 29 secs. Only 5.74G heap mem is used.
Not sure why the performance degrades sharply.
If I try to ingest the docs having 100k different fields, it will take 17
mins 44 secs. We only have 10k docs totally and not sure why ES perform so
badly.
Anyone can give suggestion to improve the performance?
--
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Re: ingest performance degrades sharply along with the documents having more fileds
Yes, this is the equivalent of using RAMDirectory. Please, don't use this,
Mmap is optimized for random access and if the lucene index can fit in heap
(to use ram dir), it can certainly fit in OS RAM, without the implications
of loading it to heap.
On Monday, July 7, 2014 6:26:07 PM UTC+2, Mahesh Venkat wrote:
Thanks Shay for updating us with perf improvements.
Apart from using the default parameters, should we follow the guideline
listed in
http://elasticsearch-users.115913.n3.nabble.com/Is-ES-es-index-store-type-memory-equivalent-to-Lucene-s-RAMDirectory-td4057417.html
Lucene supports MMapDirectory at the data indexing phase (in a batch) and
switch to in-memory for queries to optimize on search latency.
Should we use JVM system parameter -Des.index.store.type=memory . Isn't
this equivalent to using RAMDirectory in Lucene for in-memory search query
?
Thanks
--Mahesh
On Saturday, July 5, 2014 8:46:59 AM UTC-7, kimchy wrote:
Heya, I worked a bit on it, and 1.x (upcoming 1.3) has some significant
perf improvements now for this case (including improvements Lucene wise,
that are for now in ES, but will be in Lucene next version). Those include:
6648: https://github.com/elasticsearch/elasticsearch/pull/6648
6714: https://github.com/elasticsearch/elasticsearch/pull/6714
6707: https://github.com/elasticsearch/elasticsearch/pull/6707
It would be interesting if you can run the tests again with 1.x branch.
Note, also, please use default features in ES for now, no disable flushing
and such.
On Friday, June 13, 2014 7:57:23 AM UTC+2, Maco Ma wrote:
I try to measure the performance of ingesting the documents having lots
of fields.
The latest elasticsearch 1.2.1:
Total docs count: 10k (a small set definitely)
ES_HEAP_SIZE: 48G
settings:
{doc:{settings:{index:{uuid:LiWHzE5uQrinYW1wW4E3nA,number_of_replicas:0,translog:{disable_flush:true},number_of_shards:5,refresh_interval:-1,version:{created:1020199}
mappings:
{doc:{mappings:{type:{dynamic_templates:[{t1:{mapping:{store:false,norms:{enabled:false},type:string},match:*_ss}},{t2:{mapping:{store:false,type:date},match:*_dt}},{t3:{mapping:{store:false,type:integer},match:*_i}}],_source:{enabled:false},properties:{}
All fields in the documents mach the templates in the mappings.
Since I disabled the flush refresh, I submitted the flush command
(along with optimize command after it) in the client program every 10
seconds. (I tried the another interval 10mins and got the similar results)
Scenario 0 - 10k docs have 1000 different fields:
Ingestion took 12 secs. Only 1.08G heap mem is used(only states the
used heap memory).
Scenario 1 - 10k docs have 10k different fields(10 times fields compared
with scenario0):
This time ingestion took 29 secs. Only 5.74G heap mem is used.
Not sure why the performance degrades sharply.
If I try to ingest the docs having 100k different fields, it will take
17 mins 44 secs. We only have 10k docs totally and not sure why ES perform
so badly.
Anyone can give suggestion to improve the performance?
--
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Re: ingest performance degrades sharply along with the documents having more fileds
Hi, thanks for running the tests!. My tests were capped at 10k fields and improve for it, any more than that, I, and anybody here on Elasticsearch (+Lucene: Mike/Robert) simply don't recommend and can't really be behind when it comes to supporting it. In Elasticsearch, there is a conscious decision to have concrete mappings for fields introduced. This allows for nice upstream features, such as autocomplete on Kibana and Sense, as well as certain index/search level optimizations that can't be done without concrete mapping for each field introduced. This incurs a cost when it comes to many fields introduced. The idea here, is that a system that tries to put 1M different fields into Lucene simply not going to scale. The cost overhead, and even testability of such a system, is simply not something that we can support. Aside from the obvious overhead when it comes to just wrangling so many fields in Lucene (merge costs that keep being incremental, ...), there is also the plan of what to do with it. For example, if sorting is enabled, then there is a multiplied cost at loading it for sorting (compared to using nested documents, where the cost is constant, since its the same field). I think that there might be other factors in play to the performance test numbers I see below aside from the 100k and 1M different fields scenario. We can try and chase them, but the bottom line is the same, we can't support a system that asks to have 1M different fields, as we don't believe it uses either ES or Lucene correctly at this point. I suggest looking into nested documents (regardless of the system you decided to use) as a viable alternative to the many fields solution. This is the only way you will be able to scale such a system, especially across multiple nodes (nested document scales out well, many fields don't). On Tuesday, July 8, 2014 11:41:11 AM UTC+2, Maco Ma wrote: Hi Kimchy, I rerun the benchmark using ES1.3 with default settings (just disable the _source _all ) and it makes a great progress on the performance. However Solr still outperforms ES 1.3: Number of different meta data field ES ES with disable _all/codec bloom filter *ES 1.3 * Solr Scenario 0: 1000 12secs - *833*docs/sec CPU: 30.24% Heap: 1.08G time(secs) for each 1k docs:3 1 1 1 1 1 0 1 2 1 *index size: 36Mb* iowait: 0.02% 13 secs -769 docs/sec CPU: 23.68% iowait: 0.01% Heap: 1.31G Index Size: 248K Ingestion speed change: 2 1 1 1 1 1 1 1 2 1 13 secs-769 docs/sec CPU: 44.22% iowait: 0.01% Heap: 1.38G Index Size: 69M Ingestion speed change: 2 1 1 1 1 1 2 0 2 2 13 secs - 769 docs/sec CPU: 28.85% Heap: 9.39G time(secs) for each 1k docs: 2 1 1 1 1 1 1 1 2 2 Scenario 1: 10k 29secs - *345*docs/sec CPU: 40.83% Heap: 5.74G time(secs) for each 1k docs:14 2 2 2 1 2 2 1 2 1 iowait: 0.02% *Index Size: 36Mb* 31 secs - 322.6 docs/sec CPU: 39.29% iowait: 0.01% Heap: 4.76G Index Size: 396K Ingestion speed change: 12 1 2 1 1 1 2 1 4 2 20 secs-500 docs/sec CPU: 54.74% iowait: 0.02% Heap: 3.06G Index Size: 133M Ingestion speed change: 2 2 1 2 2 3 2 2 2 1 12 secs - 833 docs/sec CPU: 28.62% Heap: 9.88G time(secs) for each 1k docs:1 1 1 1 2 1 1 1 1 2 Scenario 2: 100k 17 mins 44 secs - *9.4*docs/sec CPU: 54.73% Heap: 47.99G time(secs) for each 1k docs:97 183 196 147 109 89 87 49 66 40 iowait: 0.02% *Index Size: 75Mb* 14 mins 24 secs - 11.6 docs/sec CPU: 52.30% iowait: 0.02% Heap: Index Size: 1.5M Ingestion speed change: 93 153 151 112 84 65 61 53 51 41 1 mins 24 secs- 119 docs/sec CPU: 47.67% iowait: 0.12% Heap: 8.66G Index Size: 163M Ingestion speed change: 9 14 12 12 8 8 5 7 5 4 13 secs - 769 docs/sec CPU: 29.43% Heap: 9.84G time(secs) for each 1k docs:2 1 1 1 1 1 1 1 2 2 Scenario 3: 1M 183 mins 8 secs - *0.9* docs/sec CPU: 40.47% Heap: 47.99G time(secs) for each 1k docs:133 422 701 958 989 1322 1622 1615 1630 1594 11 mins 9 secs-15docs/sec CPU: 41.45% iowait: 0.07% Heap: 36.12G Index Size: 163M Ingestion speed change: 12 24 38 55 70 86 106 117 83 78 15 secs - 666.7 docs/sec CPU: 45.10% Heap: 9.64G time(secs) for each 1k docs:2 1 1 1 1 2 1 1 3 2 Best Regards Maco On Saturday, July 5, 2014 11:46:59 PM UTC+8, kimchy wrote: Heya, I worked a bit on it, and 1.x (upcoming 1.3) has some significant perf improvements now for this case (including improvements Lucene wise, that are for now in ES, but will be in Lucene next version). Those include: 6648: https://github.com/elasticsearch/elasticsearch/pull/6648 6714: https://github.com/elasticsearch/elasticsearch/pull/6714 6707: https://github.com/elasticsearch/elasticsearch/pull/6707 It would be interesting if you can run the tests again with 1.x branch. Note, also, please use default features in ES for now, no disable flushing and such. On Friday, June 13, 2014 7:57:23 AM UTC+2, Maco Ma wrote: I try to measure the performance of ingesting the documents having lots of
Re: ingest performance degrades sharply along with the documents having more fileds
Thanks Shay for updating us with perf improvements.
Apart from using the default parameters, should we follow the guideline
listed in
http://elasticsearch-users.115913.n3.nabble.com/Is-ES-es-index-store-type-memory-equivalent-to-Lucene-s-RAMDirectory-td4057417.html
Lucene supports MMapDirectory at the data indexing phase (in a batch) and
switch to in-memory for queries to optimize on search latency.
Should we use JVM system parameter -Des.index.store.type=memory . Isn't
this equivalent to using RAMDirectory in Lucene for in-memory search query
?
Thanks
--Mahesh
On Saturday, July 5, 2014 8:46:59 AM UTC-7, kimchy wrote:
Heya, I worked a bit on it, and 1.x (upcoming 1.3) has some significant
perf improvements now for this case (including improvements Lucene wise,
that are for now in ES, but will be in Lucene next version). Those include:
6648: https://github.com/elasticsearch/elasticsearch/pull/6648
6714: https://github.com/elasticsearch/elasticsearch/pull/6714
6707: https://github.com/elasticsearch/elasticsearch/pull/6707
It would be interesting if you can run the tests again with 1.x branch.
Note, also, please use default features in ES for now, no disable flushing
and such.
On Friday, June 13, 2014 7:57:23 AM UTC+2, Maco Ma wrote:
I try to measure the performance of ingesting the documents having lots
of fields.
The latest elasticsearch 1.2.1:
Total docs count: 10k (a small set definitely)
ES_HEAP_SIZE: 48G
settings:
{doc:{settings:{index:{uuid:LiWHzE5uQrinYW1wW4E3nA,number_of_replicas:0,translog:{disable_flush:true},number_of_shards:5,refresh_interval:-1,version:{created:1020199}
mappings:
{doc:{mappings:{type:{dynamic_templates:[{t1:{mapping:{store:false,norms:{enabled:false},type:string},match:*_ss}},{t2:{mapping:{store:false,type:date},match:*_dt}},{t3:{mapping:{store:false,type:integer},match:*_i}}],_source:{enabled:false},properties:{}
All fields in the documents mach the templates in the mappings.
Since I disabled the flush refresh, I submitted the flush command
(along with optimize command after it) in the client program every 10
seconds. (I tried the another interval 10mins and got the similar results)
Scenario 0 - 10k docs have 1000 different fields:
Ingestion took 12 secs. Only 1.08G heap mem is used(only states the used
heap memory).
Scenario 1 - 10k docs have 10k different fields(10 times fields compared
with scenario0):
This time ingestion took 29 secs. Only 5.74G heap mem is used.
Not sure why the performance degrades sharply.
If I try to ingest the docs having 100k different fields, it will take 17
mins 44 secs. We only have 10k docs totally and not sure why ES perform so
badly.
Anyone can give suggestion to improve the performance?
--
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Re: ingest performance degrades sharply along with the documents having more fileds
Heya, I worked a bit on it, and 1.x (upcoming 1.3) has some significant
perf improvements now for this case (including improvements Lucene wise,
that are for now in ES, but will be in Lucene next version). Those include:
6648: https://github.com/elasticsearch/elasticsearch/pull/6648
6714: https://github.com/elasticsearch/elasticsearch/pull/6714
6707: https://github.com/elasticsearch/elasticsearch/pull/6707
It would be interesting if you can run the tests again with 1.x branch.
Note, also, please use default features in ES for now, no disable flushing
and such.
On Friday, June 13, 2014 7:57:23 AM UTC+2, Maco Ma wrote:
I try to measure the performance of ingesting the documents having lots of
fields.
The latest elasticsearch 1.2.1:
Total docs count: 10k (a small set definitely)
ES_HEAP_SIZE: 48G
settings:
{doc:{settings:{index:{uuid:LiWHzE5uQrinYW1wW4E3nA,number_of_replicas:0,translog:{disable_flush:true},number_of_shards:5,refresh_interval:-1,version:{created:1020199}
mappings:
{doc:{mappings:{type:{dynamic_templates:[{t1:{mapping:{store:false,norms:{enabled:false},type:string},match:*_ss}},{t2:{mapping:{store:false,type:date},match:*_dt}},{t3:{mapping:{store:false,type:integer},match:*_i}}],_source:{enabled:false},properties:{}
All fields in the documents mach the templates in the mappings.
Since I disabled the flush refresh, I submitted the flush command (along
with optimize command after it) in the client program every 10 seconds. (I
tried the another interval 10mins and got the similar results)
Scenario 0 - 10k docs have 1000 different fields:
Ingestion took 12 secs. Only 1.08G heap mem is used(only states the used
heap memory).
Scenario 1 - 10k docs have 10k different fields(10 times fields compared
with scenario0):
This time ingestion took 29 secs. Only 5.74G heap mem is used.
Not sure why the performance degrades sharply.
If I try to ingest the docs having 100k different fields, it will take 17
mins 44 secs. We only have 10k docs totally and not sure why ES perform so
badly.
Anyone can give suggestion to improve the performance?
--
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Re: ingest performance degrades sharply along with the documents having more fileds
Added the Solr benchmark as well:
Number of different meta data field
ES with disable _all/codec bloom filter
ES (Ingestion Query concurrently)
Solr
Solr(Ingestion Query concurrently)
Scenario 0: 1000
13 secs -769 docs/sec
CPU: 23.68%
iowait: 0.01%
Heap: 1.31G
Index Size: 248K
Ingestion speed change: 2 1 1 1 1 1 1 1 2 1
14 secs -714 docs/sec
CPU: 27.51%
iowait: 0.03%
Heap: 1.27G
Index Size: 304K
Ingestion speed change: 3 1 1 1 1 1 1 2 2 1
13 secs - 769 docs/sec
CPU: 28.85%
Heap: 9.39G
time(secs) for each 1k docs: 2 1 1 1 1 1 1 1 2 2
14 secs-714 docs/sec
CPU: 37.02%
Heap: 10G
Ingestion speed change: 2 2 1 1 1 1 2 2 1 1
Scenario 1: 10k
31 secs - 322.6 docs/sec
CPU: 39.29%
iowait: 0.01%
Heap: 4.76G
Index Size: 396K
Ingestion speed change: 12 1 2 1 1 1 2 1 4 2
35 secs - 285docs/sec
CPU: 42.46%
iowait: 0.01%
Heap: 5.14G
Index Size: 336K
Ingestion speed change: 13 2 1 1 2 1 1 4 1 2
12 secs - 833 docs/sec
CPU: 28.62%
Heap: 9.88G
time(secs) for each 1k docs:1 1 1 1 2 1 1 1 1 2
16 secs- 625 docs/sec
CPU: 34.07%
Heap: 10G
Ingestion speed change: 2 2 1 1 1 1 2 2 2 2
List several sample queries for Solr:
curl -s
'http://localhost:8983/solr/collection2/query?rows=0q=field282_ss:f*'
curl -s
'http://localhost:8983/solr/collection2/query?rows=0q=field989_dt:\[2012-3-06T01%3A15%3A51Z%20TO%20NOW\]'
curl -s
'http://localhost:8983/solr/collection2/query?rows=0q=field363_i:\[0%20TO%20177\]'
filters:
curl -s
'http://localhost:8983/solr/collection2/query?rows=0q=*fq=field118_i:\[0%20TO%2029\]'
curl -s
'http://localhost:8983/solr/collection2/query?rows=0q=*fq=field91_dt:\[2012-1-06T01%3A15%3A51Z%20TO%20NOW\]'
curl -s
'http://localhost:8983/solr/collection2/query?rows=0q=*fq=field879_ss:f*'
Maco
On Wednesday, June 25, 2014 5:23:16 PM UTC+8, Maco Ma wrote:
I run the benchmark where search and ingest runs concurrently. Paste the
results here:
Number of different meta data field
ES with disable _all/codec bloom filter
ES disabled params (Ingestion Query concurrently)
Scenario 0: 1000
13 secs -769 docs/sec
CPU: 23.68%
iowait: 0.01%
Heap: 1.31G
Index Size: 248K
Ingestion speed change: 2 1 1 1 1 1 1 1 2 1
14 secs -714 docs/sec
CPU: 27.51%
iowait: 0.03%
Heap: 1.27G
Index Size: 304K
Ingestion speed change: 3 1 1 1 1 1 1 2 2 1
Scenario 1: 10k
31 secs - 322.6 docs/sec
CPU: 39.29%
iowait: 0.01%
Heap: 4.76G
Index Size: 396K
Ingestion speed change: 12 1 2 1 1 1 2 1 4 2
35 secs - 285docs/sec
CPU: 42.46%
iowait: 0.01%
Heap: 5.14G
Index Size: 336K
Ingestion speed change: 13 2 1 1 2 1 1 4 1 2
I added one more thread to do the query to the existing ingestion script:
sub query {
my $qstr = q(curl -s 'http://localhost:9200/doc/type/_search'
-d'{query:{filtered:{query:{query_string:{fields : [);
my $fstr = q(curl -s 'http://localhost:9200/doc/type/_search'
-d'{query:{filtered:{query:{match_all:{}},filter:{);
my $fieldNum = 1000;
while ($no $total )
{
$tr= int(rand(5));
if( $tr == 0 )
{
$fieldName = field.int(rand($fieldNum))._i;
$fieldValue = *1*;
}
elsif ($tr == 1)
{
$fieldName = field.int(rand($fieldNum))._dt;
$fieldValue = *2*;
}
else
{
$fieldName = field.int(rand($fieldNum))._ss;
$fieldValue = f*;
}
$cstr = $qstr. $fieldName . q(],query:) . $fieldValue .
q(}');
print $cstr.\n;
print `$cstr`.\n;
$tr= int(rand(5));
if( $tr == 0 )
{
$cstr = $fstr. q(range:{
field).int(rand($fieldNum)).q(_i:{gte:). int(rand(1000)). q(}}');
}
elsif ($tr == 1)
{
$cstr = $fstr. q(range:{ field).
int(rand($fieldNum)).q(_dt:{from:
2010-01-).(1+int(rand(31))).q(T02:10:03}}');
}
else
{
$cstr = $fstr.
q(regexp:{field).int(rand($fieldNum)).q(_ss:f.*}');
}
print $cstr.\n;
print `$cstr`.\n;
}
}
Maco
On Wednesday, June 25, 2014 1:04:08 AM UTC+8, Cindy Hsin wrote:
Looks like the memory usage increased a lot with 10k fields with these
two parameter disabled.
Based on the experiment we have done, looks like ES have abnormal memory
usage and performance degradation when number of fields are large (ie.
10k). Where Solr memory usage and performance remains for the large number
fields.
If we are only looking at 10k fields scenario, is there a way for ES to
make the ingest performance better (perhaps via a bug fix)? Looking at the
performance number, I think this abnormal memory usage performance drop
is most likely a bug in ES layer. If this is not technically feasible then
we'll report back that we have checked with ES experts and confirmed that
there is no way for ES to provide a fix to address this issue. The solution
Mike suggestion sounds like a workaround (ie combine multiple fields into
one field to reduce the large number of fields). I can run it by our team
but not sure if this will fly.
I have also asked Maco to do one more
Re: ingest performance degrades sharply along with the documents having more fileds
I run the benchmark where search and ingest runs concurrently. Paste the
results here:
Number of different meta data field
ES with disable _all/codec bloom filter
ES disabled params (Ingestion Query concurrently)
Scenario 0: 1000
13 secs -769 docs/sec
CPU: 23.68%
iowait: 0.01%
Heap: 1.31G
Index Size: 248K
Ingestion speed change: 2 1 1 1 1 1 1 1 2 1
14 secs -714 docs/sec
CPU: 27.51%
iowait: 0.03%
Heap: 1.27G
Index Size: 304K
Ingestion speed change: 3 1 1 1 1 1 1 2 2 1
Scenario 1: 10k
31 secs - 322.6 docs/sec
CPU: 39.29%
iowait: 0.01%
Heap: 4.76G
Index Size: 396K
Ingestion speed change: 12 1 2 1 1 1 2 1 4 2
35 secs - 285docs/sec
CPU: 42.46%
iowait: 0.01%
Heap: 5.14G
Index Size: 336K
Ingestion speed change: 13 2 1 1 2 1 1 4 1 2
I added one more thread to do the query to the existing ingestion script:
sub query {
my $qstr = q(curl -s 'http://localhost:9200/doc/type/_search'
-d'{query:{filtered:{query:{query_string:{fields : [);
my $fstr = q(curl -s 'http://localhost:9200/doc/type/_search'
-d'{query:{filtered:{query:{match_all:{}},filter:{);
my $fieldNum = 1000;
while ($no $total )
{
$tr= int(rand(5));
if( $tr == 0 )
{
$fieldName = field.int(rand($fieldNum))._i;
$fieldValue = *1*;
}
elsif ($tr == 1)
{
$fieldName = field.int(rand($fieldNum))._dt;
$fieldValue = *2*;
}
else
{
$fieldName = field.int(rand($fieldNum))._ss;
$fieldValue = f*;
}
$cstr = $qstr. $fieldName . q(],query:) . $fieldValue .
q(}');
print $cstr.\n;
print `$cstr`.\n;
$tr= int(rand(5));
if( $tr == 0 )
{
$cstr = $fstr. q(range:{
field).int(rand($fieldNum)).q(_i:{gte:). int(rand(1000)). q(}}');
}
elsif ($tr == 1)
{
$cstr = $fstr. q(range:{ field).
int(rand($fieldNum)).q(_dt:{from:
2010-01-).(1+int(rand(31))).q(T02:10:03}}');
}
else
{
$cstr = $fstr.
q(regexp:{field).int(rand($fieldNum)).q(_ss:f.*}');
}
print $cstr.\n;
print `$cstr`.\n;
}
}
Maco
On Wednesday, June 25, 2014 1:04:08 AM UTC+8, Cindy Hsin wrote:
Looks like the memory usage increased a lot with 10k fields with these two
parameter disabled.
Based on the experiment we have done, looks like ES have abnormal memory
usage and performance degradation when number of fields are large (ie.
10k). Where Solr memory usage and performance remains for the large number
fields.
If we are only looking at 10k fields scenario, is there a way for ES to
make the ingest performance better (perhaps via a bug fix)? Looking at the
performance number, I think this abnormal memory usage performance drop
is most likely a bug in ES layer. If this is not technically feasible then
we'll report back that we have checked with ES experts and confirmed that
there is no way for ES to provide a fix to address this issue. The solution
Mike suggestion sounds like a workaround (ie combine multiple fields into
one field to reduce the large number of fields). I can run it by our team
but not sure if this will fly.
I have also asked Maco to do one more benchmark (where search and ingest
runs concurrently) for both ES and Solr to check whether there is any
performance degradation for Solr when search and ingest happens
concurrently. I think this is one point that Mike mentioned, right? Even
with Solr, you think we will hit some performance issue with large fields
when ingest and query runs concurrently.
Thanks!
Cindy
On Thursday, June 12, 2014 10:57:23 PM UTC-7, Maco Ma wrote:
I try to measure the performance of ingesting the documents having lots
of fields.
The latest elasticsearch 1.2.1:
Total docs count: 10k (a small set definitely)
ES_HEAP_SIZE: 48G
settings:
{doc:{settings:{index:{uuid:LiWHzE5uQrinYW1wW4E3nA,number_of_replicas:0,translog:{disable_flush:true},number_of_shards:5,refresh_interval:-1,version:{created:1020199}
mappings:
{doc:{mappings:{type:{dynamic_templates:[{t1:{mapping:{store:false,norms:{enabled:false},type:string},match:*_ss}},{t2:{mapping:{store:false,type:date},match:*_dt}},{t3:{mapping:{store:false,type:integer},match:*_i}}],_source:{enabled:false},properties:{}
All fields in the documents mach the templates in the mappings.
Since I disabled the flush refresh, I submitted the flush command
(along with optimize command after it) in the client program every 10
seconds. (I tried the another interval 10mins and got the similar results)
Scenario 0 - 10k docs have 1000 different fields:
Ingestion took 12 secs. Only 1.08G heap mem is used(only states the used
heap memory).
Scenario 1 - 10k docs have 10k different fields(10 times fields compared
with scenario0):
This time ingestion took 29 secs. Only 5.74G heap mem is used.
Not sure why the performance degrades sharply.
If I try to ingest the docs having 100k different fields, it will take 17
mins 44 secs. We only have 10k docs totally and not sure why ES perform
Re: ingest performance degrades sharply along with the documents having more fileds
Some responses below: On Tue, Jun 24, 2014 at 7:04 PM, Cindy Hsin cindy.h...@gmail.com wrote: Looks like the memory usage increased a lot with 10k fields with these two parameter disabled. Based on the experiment we have done, looks like ES have abnormal memory usage and performance degradation when number of fields are large (ie. 10k). Where Solr memory usage and performance remains for the large number fields. If we are only looking at 10k fields scenario, is there a way for ES to make the ingest performance better (perhaps via a bug fix)? I've opened an ES issue to address the slowdown as more and more unique fields are added via dynamic templates: https://github.com/elasticsearch/elasticsearch/issues/6619 The solution Mike suggestion sounds like a workaround (ie combine multiple fields into one field to reduce the large number of fields). I can run it by our team but not sure if this will fly. Well, I think both Solr and ES (once we fix the above issue) will still have high cost if you index so many fields, since they both are based on Lucene. One simple but effective approach, whether you use Solr or ES, is to use nested documents, where the parent document holds any common fields across all of your documents, and then each child document has two fields, key and value. key holds the original field name you wanted to index, and value holds the original field value, so you have as many child documents as you had field+values to index for your original document. This approach has worked well in other applications that needed so many fields... It essentially changes the wide range of field names and field values instead, which Lucene handles very well. It results in more, smaller documents, but this scales out well as you add nodes. Mike McCandless http://blog.mikemccandless.com -- 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 elasticsearch+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/CAD7smRecxnOmVOrrNfgfk5vmKZaP3ReEcM9P%2BVu2qRgLxSL%2BKQ%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
Re: ingest performance degrades sharply along with the documents having more fileds
Hi Jörg,
I rerun the benchmark with disabling the _all and codec bloom filter: just
the index data size got reduced dramatically but ingestion speed is still
similar as previous:
Number of different meta data field
ES
ES with disable _all/codec bloom filter
Scenario 0: 1000
12secs - *833*docs/sec
CPU: 30.24%
Heap: 1.08G
time(secs) for each 1k docs:3 1 1 1 1 1 0 1 2 1
*index size: 36Mb*
iowait: 0.02%
13 secs -769 docs/sec
CPU: 23.68%
iowait: 0.01%
Heap: 1.31G
Index Size: 248K
Ingestion speed change: 2 1 1 1 1 1 1 1 2 1
Scenario 1: 10k
29secs - *345*docs/sec
CPU: 40.83%
Heap: 5.74G
time(secs) for each 1k docs:14 2 2 2 1 2 2 1 2 1
iowait: 0.02%
*Index Size: 36Mb*
31 secs - 322.6 docs/sec
CPU: 39.29%
iowait: 0.01%
Heap: 47.95G
Index Size: 396K
Ingestion speed change: 12 1 2 1 1 1 2 1 4 2
Scenario 2: 100k
17 mins 44 secs - *9.4*docs/sec
CPU: 54.73%
Heap: 47.99G
time(secs) for each 1k docs:97 183 196 147 109 89 87 49 66 40
iowait: 0.02%
*Index Size: 75Mb*
14 mins 24 secs - 11.6 docs/sec
CPU: 52.30%
iowait: 0.02%
Heap: 47.96G
Index Size: 1.5M
Ingestion speed change: 93 153 151 112 84 65 61 53 51 41
We ingested one single doc once, instead of bulk ingestion, and that was
our real world requirements.
scripts to disable _all/bloom filer:
curl -XPOST localhost:9200/doc -d '{
mappings : {
type : {
_source : { enabled : false },
_all : { enabled : false },
dynamic_templates : [
{t1:{
match : *_ss,
mapping:{
type: string,
store:false,
norms : {enabled : false}
}
}},
{t2:{
match : *_dt,
mapping:{
type: date,
store: false
}
}},
{t3:{
match : *_i,
mapping:{
type: integer,
store: false
}
}}
]
}
}
}'
curl -XPUT localhost:9200/doc/_settings -d '{
index.codec.bloom.load :false
}'
Best Regards
Maco
On Monday, June 23, 2014 12:17:27 AM UTC+8, Jörg Prante wrote:
Two things to add, to make Elasticsearch/Solr comparison more fair.
In the ES mapping, you did not disable the _all field.
If you have _all field enabled, all tokens will be indexed twice, one for
the field, one for _all.
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/mapping-all-field.html
Also you may want to disable ES codec bloom filter
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/index-modules-codec.html#bloom-postings
because loading the bloom filter consumes significant memory.
Not sure why you call curl from perl, since this adds overhead. There are
nice Solr/ES perl clients to push docs using bulk indexing.
Jörg
On Wednesday, June 18, 2014 4:50:13 AM UTC+2, Maco Ma wrote:
Hi Mike,
new_ES_config.sh(define the templates and disable the refresh/flush):
curl -XPOST localhost:9200/doc -d '{
mappings : {
type : {
_source : { enabled : false },
dynamic_templates : [
{t1:{
match : *_ss,
mapping:{
type: string,
store:false,
norms : {enabled : false}
}
}},
{t2:{
match : *_dt,
mapping:{
type: date,
store: false
}
}},
{t3:{
match : *_i,
mapping:{
type: integer,
store: false
}
}}
]
}
}
}'
curl -XPUT localhost:9200/doc/_settings -d '{
index.refresh_interval : -1
}'
curl -XPUT localhost:9200/doc/_settings -d '{
index.translog.disable_flush : true
}'
new_ES_ingest_threads.pl( spawn 10 threads to use curl command to ingest
the doc and one thread to flush/optimize periodically):
my $num_args = $#ARGV + 1;
if ($num_args 1 || $num_args 2) {
print \n usuage:$0 [src_dir] [thread_count]\n;
exit;
}
my $INST_HOME=/scratch/aime/elasticsearch-1.2.1;
my $pid = qx(jps | sed -e '/Elasticsearch/p' -n | sed 's/ .*//');
chomp($pid);
if( $pid eq )
{
print Instance is not up\n;
exit;
}
my $dir = $ARGV[0];
my $td_count = 10;
$td_count = $ARGV[1] if($num_args == 2);
open(FH, $lf);
print FH source dir: $dir\nthread_count: $td_count\n;
print FH localtime().\n;
use threads;
use threads::shared;
my
Re: ingest performance degrades sharply along with the documents having more fileds
Looks like the memory usage increased a lot with 10k fields with these two
parameter disabled.
Based on the experiment we have done, looks like ES have abnormal memory
usage and performance degradation when number of fields are large (ie.
10k). Where Solr memory usage and performance remains for the large number
fields.
If we are only looking at 10k fields scenario, is there a way for ES to
make the ingest performance better (perhaps via a bug fix)? Looking at the
performance number, I think this abnormal memory usage performance drop
is most likely a bug in ES layer. If this is not technically feasible then
we'll report back that we have checked with ES experts and confirmed that
there is no way for ES to provide a fix to address this issue. The solution
Mike suggestion sounds like a workaround (ie combine multiple fields into
one field to reduce the large number of fields). I can run it by our team
but not sure if this will fly.
I have also asked Maco to do one more benchmark (where search and ingest
runs concurrently) for both ES and Solr to check whether there is any
performance degradation for Solr when search and ingest happens
concurrently. I think this is one point that Mike mentioned, right? Even
with Solr, you think we will hit some performance issue with large fields
when ingest and query runs concurrently.
Thanks!
Cindy
On Thursday, June 12, 2014 10:57:23 PM UTC-7, Maco Ma wrote:
I try to measure the performance of ingesting the documents having lots of
fields.
The latest elasticsearch 1.2.1:
Total docs count: 10k (a small set definitely)
ES_HEAP_SIZE: 48G
settings:
{doc:{settings:{index:{uuid:LiWHzE5uQrinYW1wW4E3nA,number_of_replicas:0,translog:{disable_flush:true},number_of_shards:5,refresh_interval:-1,version:{created:1020199}
mappings:
{doc:{mappings:{type:{dynamic_templates:[{t1:{mapping:{store:false,norms:{enabled:false},type:string},match:*_ss}},{t2:{mapping:{store:false,type:date},match:*_dt}},{t3:{mapping:{store:false,type:integer},match:*_i}}],_source:{enabled:false},properties:{}
All fields in the documents mach the templates in the mappings.
Since I disabled the flush refresh, I submitted the flush command (along
with optimize command after it) in the client program every 10 seconds. (I
tried the another interval 10mins and got the similar results)
Scenario 0 - 10k docs have 1000 different fields:
Ingestion took 12 secs. Only 1.08G heap mem is used(only states the used
heap memory).
Scenario 1 - 10k docs have 10k different fields(10 times fields compared
with scenario0):
This time ingestion took 29 secs. Only 5.74G heap mem is used.
Not sure why the performance degrades sharply.
If I try to ingest the docs having 100k different fields, it will take 17
mins 44 secs. We only have 10k docs totally and not sure why ES perform so
badly.
Anyone can give suggestion to improve the performance?
--
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elasticsearch group.
To unsubscribe from this group and stop receiving emails from it, send an email
to elasticsearch+unsubscr...@googlegroups.com.
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For more options, visit https://groups.google.com/d/optout.
Re: ingest performance degrades sharply along with the documents having more fileds
Thanks!
I have asked Maco to re-test ES with these two parameter disabled.
One more question regard Lucene's capability with large amount of metadata
fields. What is the largest meta data fileds Lucene supports per Index?
What are different strategy to solve the large metadata fields issue? Do
you recommend to use type to partition different set of meta data fields
within a index?
I will clarify with our team regard their usage for large meta data fields
as well.
Thanks!
Cindy
On Thursday, June 12, 2014 10:57:23 PM UTC-7, Maco Ma wrote:
I try to measure the performance of ingesting the documents having lots of
fields.
The latest elasticsearch 1.2.1:
Total docs count: 10k (a small set definitely)
ES_HEAP_SIZE: 48G
settings:
{doc:{settings:{index:{uuid:LiWHzE5uQrinYW1wW4E3nA,number_of_replicas:0,translog:{disable_flush:true},number_of_shards:5,refresh_interval:-1,version:{created:1020199}
mappings:
{doc:{mappings:{type:{dynamic_templates:[{t1:{mapping:{store:false,norms:{enabled:false},type:string},match:*_ss}},{t2:{mapping:{store:false,type:date},match:*_dt}},{t3:{mapping:{store:false,type:integer},match:*_i}}],_source:{enabled:false},properties:{}
All fields in the documents mach the templates in the mappings.
Since I disabled the flush refresh, I submitted the flush command (along
with optimize command after it) in the client program every 10 seconds. (I
tried the another interval 10mins and got the similar results)
Scenario 0 - 10k docs have 1000 different fields:
Ingestion took 12 secs. Only 1.08G heap mem is used(only states the used
heap memory).
Scenario 1 - 10k docs have 10k different fields(10 times fields compared
with scenario0):
This time ingestion took 29 secs. Only 5.74G heap mem is used.
Not sure why the performance degrades sharply.
If I try to ingest the docs having 100k different fields, it will take 17
mins 44 secs. We only have 10k docs totally and not sure why ES perform so
badly.
Anyone can give suggestion to improve the performance?
--
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 elasticsearch+unsubscr...@googlegroups.com.
To view this discussion on the web visit
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For more options, visit https://groups.google.com/d/optout.
Re: ingest performance degrades sharply along with the documents having more fileds
Hi Cindy,
There isn't a hard limit on the number of field Lucene supports, it's more
than per-field there is highish heap used, added CPU/IO cost for merging,
etc. It's just not a well tested usage of Lucene, not something the
developers focus on optimizing, etc.
Partitioning by _type won't change things (it's still a single Lucene
index).
How you design your schema really depends on how you want to search on
them. E.g. if these are single-token text fields that you need to filter
on then you can index them all under a single field (say allFilterFields),
pre-pending your original field name onto each token, and then at search
time doing the same (searching for field:text as your text token within
allFilterFields).
Mike McCandless
http://blog.mikemccandless.com
On Tue, Jun 24, 2014 at 12:12 AM, Cindy Hsin cindy.h...@gmail.com wrote:
Thanks!
I have asked Maco to re-test ES with these two parameter disabled.
One more question regard Lucene's capability with large amount of metadata
fields. What is the largest meta data fileds Lucene supports per Index?
What are different strategy to solve the large metadata fields issue? Do
you recommend to use type to partition different set of meta data fields
within a index?
I will clarify with our team regard their usage for large meta data fields
as well.
Thanks!
Cindy
On Thursday, June 12, 2014 10:57:23 PM UTC-7, Maco Ma wrote:
I try to measure the performance of ingesting the documents having lots
of fields.
The latest elasticsearch 1.2.1:
Total docs count: 10k (a small set definitely)
ES_HEAP_SIZE: 48G
settings:
{doc:{settings:{index:{uuid:LiWHzE5uQrinYW1wW4E3nA
,number_of_replicas:0,translog:{disable_flush:
true},number_of_shards:5,refresh_interval:-1,
version:{created:1020199}
mappings:
{doc:{mappings:{type:{dynamic_templates:[{t1:{
mapping:{store:false,norms:{enabled:false},
type:string},match:*_ss}},{t2:{mapping:{store:
false,type:date},match:*_dt}},{t3:{mapping:{
store:false,type:integer},match:*_i}}],_source:{
enabled:false},properties:{}
All fields in the documents mach the templates in the mappings.
Since I disabled the flush refresh, I submitted the flush command
(along with optimize command after it) in the client program every 10
seconds. (I tried the another interval 10mins and got the similar results)
Scenario 0 - 10k docs have 1000 different fields:
Ingestion took 12 secs. Only 1.08G heap mem is used(only states the used
heap memory).
Scenario 1 - 10k docs have 10k different fields(10 times fields compared
with scenario0):
This time ingestion took 29 secs. Only 5.74G heap mem is used.
Not sure why the performance degrades sharply.
If I try to ingest the docs having 100k different fields, it will take 17
mins 44 secs. We only have 10k docs totally and not sure why ES perform so
badly.
Anyone can give suggestion to improve the performance?
--
You received this message because you are subscribed to the Google Groups
elasticsearch group.
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email to elasticsearch+unsubscr...@googlegroups.com.
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.
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For more options, visit https://groups.google.com/d/optout.
Re: ingest performance degrades sharply along with the documents having more fileds
Two things to add, to make Elasticsearch/Solr comparison more fair.
In the ES mapping, you did not disable the _all field.
If you have _all field enabled, all tokens will be indexed twice, one for
the field, one for _all.
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/mapping-all-field.html
Also you may want to disable ES codec bloom filter
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/index-modules-codec.html#bloom-postings
because loading the bloom filter consumes significant memory.
Not sure why you call curl from perl, since this adds overhead. There are
nice Solr/ES perl clients to push docs using bulk indexing.
Jörg
On Wednesday, June 18, 2014 4:50:13 AM UTC+2, Maco Ma wrote:
Hi Mike,
new_ES_config.sh(define the templates and disable the refresh/flush):
curl -XPOST localhost:9200/doc -d '{
mappings : {
type : {
_source : { enabled : false },
dynamic_templates : [
{t1:{
match : *_ss,
mapping:{
type: string,
store:false,
norms : {enabled : false}
}
}},
{t2:{
match : *_dt,
mapping:{
type: date,
store: false
}
}},
{t3:{
match : *_i,
mapping:{
type: integer,
store: false
}
}}
]
}
}
}'
curl -XPUT localhost:9200/doc/_settings -d '{
index.refresh_interval : -1
}'
curl -XPUT localhost:9200/doc/_settings -d '{
index.translog.disable_flush : true
}'
new_ES_ingest_threads.pl( spawn 10 threads to use curl command to ingest
the doc and one thread to flush/optimize periodically):
my $num_args = $#ARGV + 1;
if ($num_args 1 || $num_args 2) {
print \n usuage:$0 [src_dir] [thread_count]\n;
exit;
}
my $INST_HOME=/scratch/aime/elasticsearch-1.2.1;
my $pid = qx(jps | sed -e '/Elasticsearch/p' -n | sed 's/ .*//');
chomp($pid);
if( $pid eq )
{
print Instance is not up\n;
exit;
}
my $dir = $ARGV[0];
my $td_count = 10;
$td_count = $ARGV[1] if($num_args == 2);
open(FH, $lf);
print FH source dir: $dir\nthread_count: $td_count\n;
print FH localtime().\n;
use threads;
use threads::shared;
my $flush_intv = 10;
my $no:shared=0;
my $total = 1;
my $intv = 1000;
my $tstr:shared = ;
my $ltime:shared = time;
sub commit {
$SIG{'KILL'} = sub {`curl -XPOST '
http://localhost:9200/doc/_flush'`;print
http://localhost:9200/doc/_flush';print forced commit done on
.localtime().\n;threads-exit();};
while ($no $total )
{
`curl -XPOST 'http://localhost:9200/doc/_flush'`
http://localhost:9200/doc/_flush';
`curl -XPOST 'http://localhost:9200/doc/_optimize'`
http://localhost:9200/doc/_optimize';
print commit on .localtime().\n;
sleep($flush_intv);
}
`curl -XPOST 'http://localhost:9200/doc/_flush'`
http://localhost:9200/doc/_flush';
print commit done on .localtime().\n;
}
sub do {
my $c = -1;
while(1)
{
{
lock($no);
$c=$no;
$no++;
}
last if($c = $total);
`curl -XPOST -s localhost:9200/doc/type/$c --data-binary
\@$dir/$c.json`;
if( ($c +1) % $intv == 0 )
{
lock($ltime);
$curtime = time;
$tstr .= ($curtime - $ltime). ;
$ltime = $curtime;
}
}
}
# start the monitor processes
my $sarId = qx(sar -A 5 10 -o sar5sec_$dir.out /dev/null \necho
\$!);
my $jgcId = qx(jstat -gc $pid 2s jmem_$dir.out \necho \$!);
my $ct = threads-create(\commit);
my $start = time;
my @ts=();
for $i (1..$td_count)
{
my $t = threads-create(\do);
push(@ts, $t);
}
for my $t (@ts)
{
$t-join();
}
$ct-kill('KILL');
my $fin = time;
qx(kill -9 $sarId\nkill -9 $jgcId);
print FH localtime().\n;
$ct-join();
print FH qx(curl 'http://localhost:9200/doc/type/_count?q=*');
close(FH);
new_Solr_ingest_threads.pl is similar to the file
new_ES_ingest_threads.pl and uses the different parameters for curl
commands. Only post the differences here:
sub commit {
while ($no $total )
{
`curl 'http://localhost:8983/solr/collection2/update?commit=true'`
http://localhost:8983/solr/collection2/update?commit=true';
`curl 'http://localhost:8983/solr/collection2/update?optimize=true'`
http://localhost:8983/solr/collection2/update?optimize=true';
print commit on .localtime().\n;
sleep(10);
}
`curl 'http://localhost:8983/solr/collection2/update?commit=true'`
http://localhost:8983/solr/collection2/update?commit=true';
print commit done on .localtime().\n;
}
Re: ingest performance degrades sharply along with the documents having more fileds
On Fri, Jun 20, 2014 at 8:00 PM, Cindy Hsin cindy.h...@gmail.com wrote: Hi, Mike: Since both ES and Solr uses Lucene, do you know why we only see big ingest performance degradation with ES but not Solr? I'm not sure why: clearly something is slow with ES as you add more and more fields. I think it has to do with how it manages its mappings. Are you suggesting that if our customer require large amount of Metadata field, even Solr won't be able to provide decent performance when ingest and search are happening concurrently? Exactly. Even if you/we fixed ES's slowness as you add tons of fields, or if you went with Solr, you're still going to see poor indexing/merging/searching performance because Lucene itself doesn't scale very well to so many fields: this use case (tons of fields) has never been a priority for Lucene developers because it's typically easy for the application to change its approach to not use so many fields. Mike McCandless http://blog.mikemccandless.com -- 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 elasticsearch+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/CAD7smRce61ZAPYv2zdFfFqjQ_onvCWN3K6Qopu6-iG1aa9MHNQ%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
Re: ingest performance degrades sharply along with the documents having more fileds
I tried your script with setting iwc.setRAMBufferSizeMB(4)/ and 48G heap size. The speed can be around 430 docs/sec before the first flush and the final speed is 350 docs/sec. Not sure what configuration Solr uses and its ingestion speed can be 800 docs/sec. Maco On Wednesday, June 18, 2014 6:09:07 AM UTC+8, Michael McCandless wrote: I tested roughly your Scenario 2 (100K unique fields, 100 fields per document) with a straight Lucene test (attached, but not sure if the list strips attachments). Net/net I see ~100 docs/sec with one thread ... which is very slow. Lucene stores quite a lot for each unique indexed field name and it's really a bad idea to plan on having so many unique fields in the index: you'll spend lots of RAM and CPU. Can you describe the wider use case here? Maybe there's a more performant way to achieve it... On Fri, Jun 13, 2014 at 2:40 PM, Cindy Hsin cindy...@gmail.com javascript: wrote: Hi, Mark: We are doing single document ingestion. We did a performance comparison between Solr and Elastic Search (ES). The performance for ES degrades dramatically when we increase the metadata fields where Solr performance remains the same. The performance is done in very small data set (ie. 10k documents, the index size is only 75mb). The machine is a high spec machine with 48GB memory. You can see ES performance drop 50% even when the machine have plenty memory. ES consumes all the machine memory when metadata field increased to 100k. This behavior seems abnormal since the data is really tiny. We also tried with larger data set (ie. 100k and 1Mil documents), ES throw OOW for scenario 2 for 1 Mil doc scenario. We want to know whether this is a bug in ES and/or is there any workaround (config step) we can use to eliminate the performance degradation. Currently ES performance does not meet the customer requirement so we want to see if there is anyway we can bring ES performance to the same level as Solr. Below is the configuration setting and benchmark results for 10k document set. scenario 0 means there are 1000 different metadata fields in the system. scenario 1 means there are 10k different metatdata fields in the system. scenario 2 means there are 100k different metadata fields in the system. scenario 3 means there are 1M different metadata fields in the system. - disable hard-commit soft commit + use a *client* to do commit (ES Solr) every 10 second - ES: flush, refresh are disabled - Solr: autoSoftCommit are disabled - monitor load on the system (cpu, memory, etc) or the ingestion speed change over time - monitor the ingestion speed (is there any degradation over time?) - new ES config:new_ES_config.sh https://stbeehive.oracle.com/content/dav/st/Cloud%20Search/Documents/new_ES_config.sh; new ingestion: new_ES_ingest_threads.pl https://stbeehive.oracle.com/content/dav/st/Cloud%20Search/Documents/new_ES_ingest_threads.pl - new Solr ingestion: new_Solr_ingest_threads.pl https://stbeehive.oracle.com/content/dav/st/Cloud%20Search/Documents/new_Solr_ingest_threads.pl - flush interval: 10s Number of different meta data fieldESSolrScenario 0: 100012secs - 833docs/sec CPU: 30.24% Heap: 1.08G time(secs) for each 1k docs:3 1 1 1 1 1 0 1 2 1 index size: 36M iowait: 0.02%13 secs - 769 docs/sec CPU: 28.85% Heap: 9.39G time(secs) for each 1k docs: 2 1 1 1 1 1 1 1 2 2Scenario 1: 10k29secs - 345docs/sec CPU: 40.83% Heap: 5.74G time(secs) for each 1k docs:14 2 2 2 1 2 2 1 2 1 iowait: 0.02% Index Size: 36M12 secs - 833 docs/sec CPU: 28.62% Heap: 9.88G time(secs) for each 1k docs:1 1 1 1 2 1 1 1 1 2 Scenario 2: 100k17 mins 44 secs - 9.4docs/sec CPU: 54.73% Heap: 47.99G time(secs) for each 1k docs:97 183 196 147 109 89 87 49 66 40 iowait: 0.02% Index Size: 75M13 secs - 769 docs/sec CPU: 29.43% Heap: 9.84G time(secs) for each 1k docs:2 1 1 1 1 1 1 1 2 2Scenario 3: 1M183 mins 8 secs - 0.9 docs/sec CPU: 40.47% Heap: 47.99G time(secs) for each 1k docs:133 422 701 958 989 1322 1622 1615 1630 1594 15 secs - 666.7 docs/sec CPU: 45.10% Heap: 9.64G time(secs) for each 1k docs:2 1 1 1 1 2 1 1 3 2 Thanks! Cindy -- 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 elasticsearc...@googlegroups.com javascript:. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/4efc9c2d-ead4-4702-896d-dc32b5867859%40googlegroups.com https://groups.google.com/d/msgid/elasticsearch/4efc9c2d-ead4-4702-896d-dc32b5867859%40googlegroups.com?utm_medium=emailutm_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
Re: ingest performance degrades sharply along with the documents having more fileds
On Wed, Jun 18, 2014 at 2:38 AM, Maco Ma mayaohu...@gmail.com wrote: I tried your script with setting iwc.setRAMBufferSizeMB(4)/ and 48G heap size. The speed can be around 430 docs/sec before the first flush and the final speed is 350 docs/sec. Not sure what configuration Solr uses and its ingestion speed can be 800 docs/sec. Well, probably the difference is threads? That simple Lucene test uses only 1 thread, but your ES/Solr test uses 10 threads. I think the cost in ES is how the MapperService maintains mappings for all fields; I don't think there's a quick fix to reduce this cost. But net/net you really need to take a step back and re-evaluate your approach here: even if you use Solr, indexing at 800 docs/sec using 10 threads is awful indexing performance and this is because Lucene itself has a high cost per field, at indexing time and searching time. E.g. have you tried opening a searcher once you've built a large index with so many unique fields? The heap usage will be very high. Tested search performance on that searcher? Merging cost will be very high, etc. Lucene is just not optimized for the zillions of unique fields case, because you can so easily move those N fields into a single field; e.g. if this is just for simple term filtering, make a single field and then as terms insert fieldName:fieldValue as your tokens. If you insist on creating so many unique fields in your use case you will be unhappy down the road with Lucene ... Mike McCandless http://blog.mikemccandless.com -- 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 elasticsearch+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/CAD7smRdMdQDP1e8MhxnJb%2BBWU02pmjTVfoV6r-BTNescv4%3DSvQ%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
Re: ingest performance degrades sharply along with the documents having more fileds
Hi, Could you post the scripts you linked to (new_ES_config.sh, new_ES_ingest_threads.pl, new_Solr_ingest_threads.pl) inlined? I can't download them from where you linked. Optimizing every 10 seconds or 10 minutes is really not a good idea in general, but I guess if you're doing the same with ES and Solr then the comparison is at least fair. It's odd you see such a slowdown with ES... Mike On Fri, Jun 13, 2014 at 2:40 PM, Cindy Hsin cindy.h...@gmail.com wrote: Hi, Mark: We are doing single document ingestion. We did a performance comparison between Solr and Elastic Search (ES). The performance for ES degrades dramatically when we increase the metadata fields where Solr performance remains the same. The performance is done in very small data set (ie. 10k documents, the index size is only 75mb). The machine is a high spec machine with 48GB memory. You can see ES performance drop 50% even when the machine have plenty memory. ES consumes all the machine memory when metadata field increased to 100k. This behavior seems abnormal since the data is really tiny. We also tried with larger data set (ie. 100k and 1Mil documents), ES throw OOW for scenario 2 for 1 Mil doc scenario. We want to know whether this is a bug in ES and/or is there any workaround (config step) we can use to eliminate the performance degradation. Currently ES performance does not meet the customer requirement so we want to see if there is anyway we can bring ES performance to the same level as Solr. Below is the configuration setting and benchmark results for 10k document set. scenario 0 means there are 1000 different metadata fields in the system. scenario 1 means there are 10k different metatdata fields in the system. scenario 2 means there are 100k different metadata fields in the system. scenario 3 means there are 1M different metadata fields in the system. - disable hard-commit soft commit + use a *client* to do commit (ES Solr) every 10 second - ES: flush, refresh are disabled - Solr: autoSoftCommit are disabled - monitor load on the system (cpu, memory, etc) or the ingestion speed change over time - monitor the ingestion speed (is there any degradation over time?) - new ES config:new_ES_config.sh; new ingestion: new_ES_ingest_threads.pl - new Solr ingestion: new_Solr_ingest_threads.pl - flush interval: 10s Number of different meta data field ESSolrScenario 0: 100012secs - 833docs/sec CPU: 30.24% Heap: 1.08G time(secs) for each 1k docs:3 1 1 1 1 1 0 1 2 1 index size: 36M iowait: 0.02%13 secs - 769 docs/sec CPU: 28.85% Heap: 9.39G time(secs) for each 1k docs: 2 1 1 1 1 1 1 1 2 2Scenario 1: 10k29secs - 345docs/sec CPU: 40.83% Heap: 5.74G time(secs) for each 1k docs:14 2 2 2 1 2 2 1 2 1 iowait: 0.02% Index Size: 36M12 secs - 833 docs/sec CPU: 28.62% Heap: 9.88G time(secs) for each 1k docs:1 1 1 1 2 1 1 1 1 2Scenario 2: 100k17 mins 44 secs - 9.4docs/sec CPU: 54.73% Heap: 47.99G time(secs) for each 1k docs:97 183 196 147 109 89 87 49 66 40 iowait: 0.02% Index Size: 75M13 secs - 769 docs/sec CPU: 29.43% Heap: 9.84G time(secs) for each 1k docs:2 1 1 1 1 1 1 1 2 2 Scenario 3: 1M183 mins 8 secs - 0.9 docs/sec CPU: 40.47% Heap: 47.99G time(secs) for each 1k docs:133 422 701 958 989 1322 1622 1615 1630 159415 secs - 666.7 docs/sec CPU: 45.10% Heap: 9.64G time(secs) for each 1k docs:2 1 1 1 1 2 1 1 3 2 Thanks! Cindy -- 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 elasticsearch+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/4efc9c2d-ead4-4702-896d-dc32b5867859%40googlegroups.com . 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 elasticsearch+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/CAD7smRfsxEPvTjfv%2BPWgpyWD5fLE1DTaPUfAe9%3DdLVzXRe4p4w%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
Re: ingest performance degrades sharply along with the documents having more fileds
I tested roughly your Scenario 2 (100K unique fields, 100 fields per document) with a straight Lucene test (attached, but not sure if the list strips attachments). Net/net I see ~100 docs/sec with one thread ... which is very slow. Lucene stores quite a lot for each unique indexed field name and it's really a bad idea to plan on having so many unique fields in the index: you'll spend lots of RAM and CPU. Can you describe the wider use case here? Maybe there's a more performant way to achieve it... On Fri, Jun 13, 2014 at 2:40 PM, Cindy Hsin cindy.h...@gmail.com wrote: Hi, Mark: We are doing single document ingestion. We did a performance comparison between Solr and Elastic Search (ES). The performance for ES degrades dramatically when we increase the metadata fields where Solr performance remains the same. The performance is done in very small data set (ie. 10k documents, the index size is only 75mb). The machine is a high spec machine with 48GB memory. You can see ES performance drop 50% even when the machine have plenty memory. ES consumes all the machine memory when metadata field increased to 100k. This behavior seems abnormal since the data is really tiny. We also tried with larger data set (ie. 100k and 1Mil documents), ES throw OOW for scenario 2 for 1 Mil doc scenario. We want to know whether this is a bug in ES and/or is there any workaround (config step) we can use to eliminate the performance degradation. Currently ES performance does not meet the customer requirement so we want to see if there is anyway we can bring ES performance to the same level as Solr. Below is the configuration setting and benchmark results for 10k document set. scenario 0 means there are 1000 different metadata fields in the system. scenario 1 means there are 10k different metatdata fields in the system. scenario 2 means there are 100k different metadata fields in the system. scenario 3 means there are 1M different metadata fields in the system. - disable hard-commit soft commit + use a *client* to do commit (ES Solr) every 10 second - ES: flush, refresh are disabled - Solr: autoSoftCommit are disabled - monitor load on the system (cpu, memory, etc) or the ingestion speed change over time - monitor the ingestion speed (is there any degradation over time?) - new ES config:new_ES_config.sh https://stbeehive.oracle.com/content/dav/st/Cloud%20Search/Documents/new_ES_config.sh; new ingestion: new_ES_ingest_threads.pl https://stbeehive.oracle.com/content/dav/st/Cloud%20Search/Documents/new_ES_ingest_threads.pl - new Solr ingestion: new_Solr_ingest_threads.pl https://stbeehive.oracle.com/content/dav/st/Cloud%20Search/Documents/new_Solr_ingest_threads.pl - flush interval: 10s Number of different meta data fieldESSolrScenario 0: 100012secs - 833docs/sec CPU: 30.24% Heap: 1.08G time(secs) for each 1k docs:3 1 1 1 1 1 0 1 2 1 index size: 36M iowait: 0.02%13 secs - 769 docs/sec CPU: 28.85% Heap: 9.39G time(secs) for each 1k docs: 2 1 1 1 1 1 1 1 2 2Scenario 1: 10k29secs - 345docs/sec CPU: 40.83% Heap: 5.74G time(secs) for each 1k docs:14 2 2 2 1 2 2 1 2 1 iowait: 0.02% Index Size: 36M12 secs - 833 docs/sec CPU: 28.62% Heap: 9.88G time(secs) for each 1k docs:1 1 1 1 2 1 1 1 1 2 Scenario 2: 100k17 mins 44 secs - 9.4docs/sec CPU: 54.73% Heap: 47.99G time(secs) for each 1k docs:97 183 196 147 109 89 87 49 66 40 iowait: 0.02% Index Size: 75M13 secs - 769 docs/sec CPU: 29.43% Heap: 9.84G time(secs) for each 1k docs:2 1 1 1 1 1 1 1 2 2Scenario 3: 1M183 mins 8 secs - 0.9 docs/sec CPU: 40.47% Heap: 47.99G time(secs) for each 1k docs:133 422 701 958 989 1322 1622 1615 1630 1594 15 secs - 666.7 docs/sec CPU: 45.10% Heap: 9.64G time(secs) for each 1k docs:2 1 1 1 1 2 1 1 3 2 Thanks! Cindy -- 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 elasticsearch+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/4efc9c2d-ead4-4702-896d-dc32b5867859%40googlegroups.com https://groups.google.com/d/msgid/elasticsearch/4efc9c2d-ead4-4702-896d-dc32b5867859%40googlegroups.com?utm_medium=emailutm_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 elasticsearch+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/CAD7smRcDKZWA8tjsqfcthGUKcEX7q2dohWy_1vcFyKo7JgB53w%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout. ManyLuceneFields.java Description: Binary data
Re: ingest performance degrades sharply along with the documents having more fileds
The way we make Solr ingest faster (single document ingest) is by turn off
the engine soft commit and hard commit and use a client to commit the
changes every 10 seconds.
Solr ingest speed remains at 800 docs per second where ES ingest speed
drops in half when we increase the fields (ie. from 1000 to 10k).
I have asked Maco to send you the requested script so you can do more
analysis.
If you can help to solve the first level ES performance degradation (ie.
1000 to 10k) as a starting point, that will be the best.
We do have real customer scenario that require large amount of metadata
fields, that is why this is a blocking issue for the stack evaluation
between Solr and Elastic Search.
Thanks!
Cindy
On Thursday, June 12, 2014 10:57:23 PM UTC-7, Maco Ma wrote:
I try to measure the performance of ingesting the documents having lots of
fields.
The latest elasticsearch 1.2.1:
Total docs count: 10k (a small set definitely)
ES_HEAP_SIZE: 48G
settings:
{doc:{settings:{index:{uuid:LiWHzE5uQrinYW1wW4E3nA,number_of_replicas:0,translog:{disable_flush:true},number_of_shards:5,refresh_interval:-1,version:{created:1020199}
mappings:
{doc:{mappings:{type:{dynamic_templates:[{t1:{mapping:{store:false,norms:{enabled:false},type:string},match:*_ss}},{t2:{mapping:{store:false,type:date},match:*_dt}},{t3:{mapping:{store:false,type:integer},match:*_i}}],_source:{enabled:false},properties:{}
All fields in the documents mach the templates in the mappings.
Since I disabled the flush refresh, I submitted the flush command (along
with optimize command after it) in the client program every 10 seconds. (I
tried the another interval 10mins and got the similar results)
Scenario 0 - 10k docs have 1000 different fields:
Ingestion took 12 secs. Only 1.08G heap mem is used(only states the used
heap memory).
Scenario 1 - 10k docs have 10k different fields(10 times fields compared
with scenario0):
This time ingestion took 29 secs. Only 5.74G heap mem is used.
Not sure why the performance degrades sharply.
If I try to ingest the docs having 100k different fields, it will take 17
mins 44 secs. We only have 10k docs totally and not sure why ES perform so
badly.
Anyone can give suggestion to improve the performance?
--
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elasticsearch group.
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to elasticsearch+unsubscr...@googlegroups.com.
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For more options, visit https://groups.google.com/d/optout.
Re: ingest performance degrades sharply along with the documents having more fileds
Hi Mike,
new_ES_config.sh(define the templates and disable the refresh/flush):
curl -XPOST localhost:9200/doc -d '{
mappings : {
type : {
_source : { enabled : false },
dynamic_templates : [
{t1:{
match : *_ss,
mapping:{
type: string,
store:false,
norms : {enabled : false}
}
}},
{t2:{
match : *_dt,
mapping:{
type: date,
store: false
}
}},
{t3:{
match : *_i,
mapping:{
type: integer,
store: false
}
}}
]
}
}
}'
curl -XPUT localhost:9200/doc/_settings -d '{
index.refresh_interval : -1
}'
curl -XPUT localhost:9200/doc/_settings -d '{
index.translog.disable_flush : true
}'
new_ES_ingest_threads.pl( spawn 10 threads to use curl command to ingest
the doc and one thread to flush/optimize periodically):
my $num_args = $#ARGV + 1;
if ($num_args 1 || $num_args 2) {
print \n usuage:$0 [src_dir] [thread_count]\n;
exit;
}
my $INST_HOME=/scratch/aime/elasticsearch-1.2.1;
my $pid = qx(jps | sed -e '/Elasticsearch/p' -n | sed 's/ .*//');
chomp($pid);
if( $pid eq )
{
print Instance is not up\n;
exit;
}
my $dir = $ARGV[0];
my $td_count = 10;
$td_count = $ARGV[1] if($num_args == 2);
open(FH, $lf);
print FH source dir: $dir\nthread_count: $td_count\n;
print FH localtime().\n;
use threads;
use threads::shared;
my $flush_intv = 10;
my $no:shared=0;
my $total = 1;
my $intv = 1000;
my $tstr:shared = ;
my $ltime:shared = time;
sub commit {
$SIG{'KILL'} = sub {`curl -XPOST
'http://localhost:9200/doc/_flush'`;print forced commit done on
.localtime().\n;threads-exit();};
while ($no $total )
{
`curl -XPOST 'http://localhost:9200/doc/_flush'`;
`curl -XPOST 'http://localhost:9200/doc/_optimize'`;
print commit on .localtime().\n;
sleep($flush_intv);
}
`curl -XPOST 'http://localhost:9200/doc/_flush'`;
print commit done on .localtime().\n;
}
sub do {
my $c = -1;
while(1)
{
{
lock($no);
$c=$no;
$no++;
}
last if($c = $total);
`curl -XPOST -s localhost:9200/doc/type/$c --data-binary
\@$dir/$c.json`;
if( ($c +1) % $intv == 0 )
{
lock($ltime);
$curtime = time;
$tstr .= ($curtime - $ltime). ;
$ltime = $curtime;
}
}
}
# start the monitor processes
my $sarId = qx(sar -A 5 10 -o sar5sec_$dir.out /dev/null \necho \$!);
my $jgcId = qx(jstat -gc $pid 2s jmem_$dir.out \necho \$!);
my $ct = threads-create(\commit);
my $start = time;
my @ts=();
for $i (1..$td_count)
{
my $t = threads-create(\do);
push(@ts, $t);
}
for my $t (@ts)
{
$t-join();
}
$ct-kill('KILL');
my $fin = time;
qx(kill -9 $sarId\nkill -9 $jgcId);
print FH localtime().\n;
$ct-join();
print FH qx(curl 'http://localhost:9200/doc/type/_count?q=*');
close(FH);
new_Solr_ingest_threads.pl is similar to the file new_ES_ingest_threads.pl
and uses the different parameters for curl commands. Only post the
differences here:
sub commit {
while ($no $total )
{
`curl 'http://localhost:8983/solr/collection2/update?commit=true'`;
`curl 'http://localhost:8983/solr/collection2/update?optimize=true'`;
print commit on .localtime().\n;
sleep(10);
}
`curl 'http://localhost:8983/solr/collection2/update?commit=true'`;
print commit done on .localtime().\n;
}
sub do {
my $c = -1;
while(1)
{
{
lock($no);
$c=$no;
$no++;
}
last if($c = $total);
`curl -s 'http://localhost:8983/solr/collection2/update/json'
--data-binary \@$dir/$c.json -H 'Content-type:application/json'`;
if( ($c +1) % $intv == 0 )
{
lock($ltime);
$curtime = time;
$tstr .= ($curtime - $ltime). ;
$ltime = $curtime;
}
}
}
BR
Maco
On Wednesday, June 18, 2014 4:44:35 AM UTC+8, Michael McCandless wrote:
Hi,
Could you post the scripts you linked to (new_ES_config.sh,
new_ES_ingest_threads.pl, new_Solr_ingest_threads.pl) inlined? I can't
download them from where you linked.
Optimizing every 10 seconds or 10 minutes is really not a good idea in
general, but I guess if you're doing the same with ES and Solr then the
comparison is at least fair.
It's odd you see such a slowdown with ES...
Mike
On Fri, Jun 13, 2014 at 2:40 PM, Cindy Hsin cindy...@gmail.com
javascript: wrote:
Hi, Mark:
We are doing single document ingestion. We did a performance comparison
between Solr and Elastic Search (ES).
The performance for ES degrades dramatically when we increase the
metadata fields where Solr performance remains
Re: ingest performance degrades sharply along with the documents having more fileds
It's not surprising that the time increases when you have an order of
magnitude more fields.
Are you using the bulk API?
Regards,
Mark Walkom
Infrastructure Engineer
Campaign Monitor
email: ma...@campaignmonitor.com
web: www.campaignmonitor.com
On 13 June 2014 15:57, Maco Ma mayaohu...@gmail.com wrote:
I try to measure the performance of ingesting the documents having lots of
fields.
The latest elasticsearch 1.2.1:
Total docs count: 10k (a small set definitely)
ES_HEAP_SIZE: 48G
settings:
{doc:{settings:{index:{uuid:LiWHzE5uQrinYW1wW4E3nA,number_of_replicas:0,translog:{disable_flush:true},number_of_shards:5,refresh_interval:-1,version:{created:1020199}
mappings:
{doc:{mappings:{type:{dynamic_templates:[{t1:{mapping:{store:false,norms:{enabled:false},type:string},match:*_ss}},{t2:{mapping:{store:false,type:date},match:*_dt}},{t3:{mapping:{store:false,type:integer},match:*_i}}],_source:{enabled:false},properties:{}
All fields in the documents mach the templates in the mappings.
Since I disabled the flush refresh, I submitted the flush command (along
with optimize command after it) in the client program every 10 seconds. (I
tried the another interval 10mins and got the similar results)
Scenario 0 - 10k docs have 1000 different fields:
Ingestion took 12 secs. Only 1.08G heap mem is used(only states the used
heap memory).
Scenario 1 - 10k docs have 10k different fields(10 times fields compared
with scenario0):
This time ingestion took 29 secs. Only 5.74G heap mem is used.
Not sure why the performance degrades sharply.
If I try to ingest the docs having 100k different fields, it will take 17
mins 44 secs. We only have 10k docs totally and not sure why ES perform so
badly.
Anyone can give suggestion to improve the performance?
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Re: ingest performance degrades sharply along with the documents having more fileds
I used the curl command to do the ingestion(one command, one doc) and
flush. I also tried the Solr(disabled the soft/hard commit do the commit
with client program) with the same data commands and its performance did
not degrade. Lucene are used for both of them and not sure why there is a
big difference with the performances.
On Friday, June 13, 2014 2:02:58 PM UTC+8, Mark Walkom wrote:
It's not surprising that the time increases when you have an order of
magnitude more fields.
Are you using the bulk API?
Regards,
Mark Walkom
Infrastructure Engineer
Campaign Monitor
email: ma...@campaignmonitor.com javascript:
web: www.campaignmonitor.com
On 13 June 2014 15:57, Maco Ma mayao...@gmail.com javascript: wrote:
I try to measure the performance of ingesting the documents having lots
of fields.
The latest elasticsearch 1.2.1:
Total docs count: 10k (a small set definitely)
ES_HEAP_SIZE: 48G
settings:
{doc:{settings:{index:{uuid:LiWHzE5uQrinYW1wW4E3nA,number_of_replicas:0,translog:{disable_flush:true},number_of_shards:5,refresh_interval:-1,version:{created:1020199}
mappings:
{doc:{mappings:{type:{dynamic_templates:[{t1:{mapping:{store:false,norms:{enabled:false},type:string},match:*_ss}},{t2:{mapping:{store:false,type:date},match:*_dt}},{t3:{mapping:{store:false,type:integer},match:*_i}}],_source:{enabled:false},properties:{}
All fields in the documents mach the templates in the mappings.
Since I disabled the flush refresh, I submitted the flush command
(along with optimize command after it) in the client program every 10
seconds. (I tried the another interval 10mins and got the similar results)
Scenario 0 - 10k docs have 1000 different fields:
Ingestion took 12 secs. Only 1.08G heap mem is used(only states the used
heap memory).
Scenario 1 - 10k docs have 10k different fields(10 times fields compared
with scenario0):
This time ingestion took 29 secs. Only 5.74G heap mem is used.
Not sure why the performance degrades sharply.
If I try to ingest the docs having 100k different fields, it will take 17
mins 44 secs. We only have 10k docs totally and not sure why ES perform so
badly.
Anyone can give suggestion to improve the performance?
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Re: ingest performance degrades sharply along with the documents having more fileds
Hi, Mark: We are doing single document ingestion. We did a performance comparison between Solr and Elastic Search (ES). The performance for ES degrades dramatically when we increase the metadata fields where Solr performance remains the same. The performance is done in very small data set (ie. 10k documents, the index size is only 75mb). The machine is a high spec machine with 48GB memory. You can see ES performance drop 50% even when the machine have plenty memory. ES consumes all the machine memory when metadata field increased to 100k. This behavior seems abnormal since the data is really tiny. We also tried with larger data set (ie. 100k and 1Mil documents), ES throw OOW for scenario 2 for 1 Mil doc scenario. We want to know whether this is a bug in ES and/or is there any workaround (config step) we can use to eliminate the performance degradation. Currently ES performance does not meet the customer requirement so we want to see if there is anyway we can bring ES performance to the same level as Solr. Below is the configuration setting and benchmark results for 10k document set. scenario 0 means there are 1000 different metadata fields in the system. scenario 1 means there are 10k different metatdata fields in the system. scenario 2 means there are 100k different metadata fields in the system. scenario 3 means there are 1M different metadata fields in the system. - disable hard-commit soft commit + use a *client* to do commit (ES Solr) every 10 second - ES: flush, refresh are disabled - Solr: autoSoftCommit are disabled - monitor load on the system (cpu, memory, etc) or the ingestion speed change over time - monitor the ingestion speed (is there any degradation over time?) - new ES config:new_ES_config.sh https://stbeehive.oracle.com/content/dav/st/Cloud%20Search/Documents/new_ES_config.sh; new ingestion: new_ES_ingest_threads.pl https://stbeehive.oracle.com/content/dav/st/Cloud%20Search/Documents/new_ES_ingest_threads.pl - new Solr ingestion: new_Solr_ingest_threads.pl https://stbeehive.oracle.com/content/dav/st/Cloud%20Search/Documents/new_Solr_ingest_threads.pl - flush interval: 10s Number of different meta data fieldESSolrScenario 0: 100012secs - 833docs/sec CPU: 30.24% Heap: 1.08G time(secs) for each 1k docs:3 1 1 1 1 1 0 1 2 1 index size: 36M iowait: 0.02%13 secs - 769 docs/sec CPU: 28.85% Heap: 9.39G time(secs) for each 1k docs: 2 1 1 1 1 1 1 1 2 2Scenario 1: 10k29secs - 345docs/sec CPU: 40.83% Heap: 5.74G time(secs) for each 1k docs:14 2 2 2 1 2 2 1 2 1 iowait: 0.02% Index Size: 36M12 secs - 833 docs/sec CPU: 28.62% Heap: 9.88G time(secs) for each 1k docs:1 1 1 1 2 1 1 1 1 2Scenario 2: 100k17 mins 44 secs - 9.4docs/sec CPU: 54.73% Heap: 47.99G time(secs) for each 1k docs:97 183 196 147 109 89 87 49 66 40 iowait: 0.02% Index Size: 75M13 secs - 769 docs/sec CPU: 29.43% Heap: 9.84G time(secs) for each 1k docs:2 1 1 1 1 1 1 1 2 2Scenario 3: 1M183 mins 8 secs - 0.9 docs/sec CPU: 40.47% Heap: 47.99G time(secs) for each 1k docs:133 422 701 958 989 1322 1622 1615 1630 159415 secs - 666.7 docs/sec CPU: 45.10% Heap: 9.64G time(secs) for each 1k docs:2 1 1 1 1 2 1 1 3 2 Thanks! Cindy -- 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 elasticsearch+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/elasticsearch/4efc9c2d-ead4-4702-896d-dc32b5867859%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
