from:"Michael Sick"

Re: Elasticsearch and MongoDB without River

2015-05-24 Thread Michael Sick

I'd use Apache Storm, especially if it was used elsewhere in your
organization. --Mike

On Sat, May 23, 2015 at 4:28 AM, sriharshakiran 
kiran.srihar...@imomentous.com wrote:

 Hi All

 Now that rivers are deprecated, I need to index data into ES from MongoDB.
 Can anyone suggest an approach?

 We currently have a system in place with mongo river. I just need to know
 what should be the plan of action to get data into ES without Rivers.

 Thanks
 Sri Harsha



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Re: Elasticsearch 1.1.0 - Optimize broken?

2014-04-04 Thread Michael Sick

Have you tried max_num_segments=1 on your optimize?

On Fri, Apr 4, 2014 at 11:27 AM, Elliott Bradshaw ebradsh...@gmail.comwrote:

Any thoughts on this? I've run optimize several more times, and the
number of segments falls each time, but I'm still over 1000 segments per
shard. Has anyone else run into something similar?

On Thursday, April 3, 2014 11:21:29 AM UTC-4, Elliott Bradshaw wrote:

OK. Optimize finally returned, so I suppose something was happening in
the background, but I'm still seeing over 6500 segments. Even after
setting max_num_segments=5. Does this seem right? Queries are a little
faster (350-400ms) but still not great. Bigdesk is still showing a fair
amount of file IO.

On Thursday, April 3, 2014 8:47:32 AM UTC-4, Elliott Bradshaw wrote:

Hi All,

I've recently upgraded to Elasticsearch 1.1.0. I've got a 4 node
cluster, each with 64G of ram, with 24G allocated to Elasticsearch on
each. I've batch loaded approximately 86 million documents into a single
index (4 shards) and have started benchmarking cross_field/multi_match
queries on them. The index has one replica and takes up a total of 111G.
I've run several batches of warming queries, but queries are not as fast as
I had hoped, approximately 400-500ms each. Given that *top *(on
Centos) shows 5-8 GB of free memory on each server, I would assume that the
entire index has been paged into memory (I had worried about disk
performance previously, as we are working in a virtualized environment).

A stats query on the index in questions shows that the index is composed
of 7000 segments. This seemed high to me, but maybe it's appropriate.
Regardless, I dispatched an optimize command, but I am not seeing any
progress and the command has not returned. Current merges remains at zero,
and the segment count is not changing. Checking out hot threads in
ElasticHQ, I initially saw an optimize call in the stack that was blocked
on a waitForMerge call. This however has disappeared, and I'm seeing no
evidence that the optimize is occuring.

Does any of this seem out of the norm or unusual? Has anyone else had
similar issues. This is the second time I have tried to optimize an index
since upgrading. I've gotten the same result both time.

Thanks in advance for any help/tips!

- Elliott

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Re: Advice for implementing a secure graph index with ElasticSearch

2014-03-05 Thread Michael Sick

https://github.com/thinkaurelius/titan/wiki

Titan is a distributed graph
databasehttp://en.wikipedia.org/wiki/Graph_database optimized
for storing and querying
graphshttp://en.wikipedia.org/wiki/Graph_(mathematics) represented
over a cluster of machines. The cluster can elastically scale to support a
growing dataset and user base. Titan has a pluggable storage architecture
which allows it to build on proven database technology such as Apache
Cassandra http://cassandra.apache.org/, Apache
HBasehttp://hbase.apache.org/,
or Oracle BerkeleyDBhttp://www.oracle.com/technetwork/database/berkeleydb/.
Furthermore, the pluggable indexing architecture supports
ElasticSearchhttp://elasticsearch.com/
and Lucene http://lucene.apache.org/.

I did some basic research for ES + graph and found the Titan project
interesting. Titan separates storage from indexing and only currently
supports ES for the latter. I'm sure that you could implement a storage
engine based on ES too (which makes more sense now that ES 1.x supports
backup/restore). Didn't look into security at all but this might be a good
starting point. Hope it's helpful. --Mike

On Wed, Mar 5, 2014 at 12:10 PM, Jeff Kunkle kunkl...@gmail.com wrote:

I've been trying to figure out how I can index a graph data structure
using ElasticSearch and could really use some advice from someone more
knowledgeable than me. First, let me explain the challenge. The graph model
has individual access controls at the vertex (node), edge (relationship),
and property level. I'd like my users to be able to search the graph for
vertices or edges containing matching properties, with two caveats:

1. They should not get vertex or edge results they don't have
permission to see.
2. Properties a user does not have access to see should not be
evaluated in the query.

My first thought was to index properties as either nested or child
documents of a vertex/edge and use a custom filter to remove properties a
user didn't have access to. The first problem I run into is when I try a
boolean query across properties. For example, assume I want to query a
person vertex by first name and date of birth. Since these properties are
indexed as separate documents there is never a match.

What I essentially need is the ability to query across nested or child
documents and return the parent only when there are matches across the
child documents. For example, assume a parent vertex with one property
document called full_name set to Barak Obama and another property
document named political_party set to Democrat. Is there any way for me
to query for the parent document of these two properties by asking for one
property with full_name=Barak Obama and another property with
political_party=Democrat?

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Re: Advice for implementing a secure graph index with ElasticSearch

2014-03-05 Thread Michael Sick

Hi Jeff,

Lumify looks very interesting - I'll have to take a serious look later.
While I didn't get very far down the Titan path, my starting point would be
to use Titan for all it's graphing features and add some type of query pre
and post processing to insert ACL information into the ES queries and
indexing statements. Not sure if Titan offers any hooks - but it seems it
could be added.

I'd start with the Aurelius folks and see how something like this could be
added with low impact to the existing interfaces. As far as the ES part, I
have added ACL's to documents before by having the id's as an array. This
worked in the simple case I needed because the owners of the documents
changed infrequently (so there was not much reindexing load) and I didn't
have to think it through more than that.

--Mike

On Wed, Mar 5, 2014 at 1:08 PM, Jeff Kunkle kunkl...@gmail.com wrote:

Hi Mike,

Thanks for the reply. We actually started with Titan and its a very good
project, but we couldn't easily add the needed security constraints on top
of it. Hence why I'm exploring this topic. It would be rather
straightforward to implement the index on ElasticSearch if all the data was
open to everyone. I'd be able to consolidate all of a vertex's or edge's
properties in a single document. Unfortunately, that's not the case. The
project I'm working on is at http://lumify.io if that's helpful in any
way.

Thanks Again,
Jeff

On Wednesday, March 5, 2014 12:41:59 PM UTC-5, Michael Sick wrote:

https://github.com/thinkaurelius/titan/wiki

On Wed, Mar 5, 2014 at 12:10 PM, Jeff Kunkle kunk...@gmail.com wrote:

1. They should not get vertex or edge results they don't have
permission to see.
2. Properties a user does not have access to see should not be
evaluated in the query.

To view this discussion on the web visit https://groups.google.com/d/
msgid/elasticsearch/a8beee5b-82d0-45fa-8666-31e956c03439%
40googlegroups.comhttps://groups.google.com/d/msgid/elasticsearch/a8beee5b-82d0-45fa-8666-31e956c03439%40googlegroups.com?utm_medium=emailutm_source=footer
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Re: Insert later feature

2014-02-23 Thread Michael Sick

Also, if there are no other clients wanting a faster refresh, you can
set index.refresh_interval to a higher value than the 1s default either in
general for your index or just during the times when you're doing your bulk
updates.
http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/index-modules.html

On Sun, Feb 23, 2014 at 8:28 AM, joergpra...@gmail.com
joergpra...@gmail.com wrote:

Best method to achieve this would be to implement this in front of ES so
the bulk indexing client runs only at the time it should run.

For the gathering plugin which I am working on, I plan to separate the two
phases of gathering documents and indexing documents. So, by giving a
scheduling option, it will be possible to index (or even reindex) gathered
documents at a later time, for example, documents are continuously
collected from various sources, like JDBC, web, or file system, and then
indexed at some later time (for example at night). Such collected documents
will be stored in an archive format at each gatherer node, like the archive
formats supported in the knapsack plugin.

Jörg

On Sun, Feb 23, 2014 at 6:52 AM, vineeth mohan
vm.vineethmo...@gmail.comwrote:

Hi ,

I am doing a lot of bulk insert into Elasticsearch and at the same time
doing lots of read in another index.

Because of the bulk insert my searches on other index are slow.

It is not very urgent that these bulk indexes actually gets indexed and
are immediately searchable.

Is there anyway , I can ask Elasticsearch to receive the bulk inserts but
do the actual indexing ( Which should be the CPU consuming part ) later.

I figured out that Elasticsearch would wait for 1 second before making
the documents searchable.
Here , what is it waiting for ? Is it to index the document or reopening
the indexWriter ?
Will it help me if i can configure this 1 second to 1 hour ?
If so , which parameter should i tweak.

Kindly let me know if there are any other similar features out there
which can be of any help.

Thanks
Vineeth

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Re: Problem with keeping in sync Elasticsearch across two data centers

2014-02-22 Thread Michael Sick

Hi Amit,

Ivan is correct. You might also check out I believe that you're looking for
TribeNodes
http://www.elasticsearch.org/guide/en/elasticsearch/reference/master/modules-tribe.html
and
see if it fits your needs for cross-dc replication.

--Mike

On Sat, Feb 22, 2014 at 1:32 PM, Amit Soni amitson...@gmail.com wrote:

Hello Michael - Understand that ES is not built to maintain consistent
cluster state across data centers. what I am wondering is whether there is
a way for ElasticSearch to continue to replicate data onto a different data
center (with some delay of course) so that when the primary center fails,
the fail over data center still has most of the data (may be except for the
last few seconds/minutes/hours).

Overall I am looking for a right way to implement cross data center
deployment of elastic-search!

-Amit.

On Fri, Feb 21, 2014 at 9:37 AM, Michael Sick
michael.s...@serenesoftware.com wrote:

Dario,

I believe that you're looking for TribeNodes
http://www.elasticsearch.org/guide/en/elasticsearch/reference/master/modules-tribe.html

ES is not built to consistently cluster across DC's / larger network
lags.

On Fri, Feb 21, 2014 at 11:24 AM, Dario Rossi dario...@gmail.com wrote:

Hi,
I've the following problem: our application publishes content to an
Elasticsearch cluster. We use local data less node for querying
elasticsearch then, so we don't use HTTP REST and the local nodes are the
loadbalancer. Now they came with the requirement of having the cluster
replicated to another data center too (and in the future maybe another
too... ) for resilience.

At the very beginning we thought of having one large cluster that goes
across data centers (crazy). This solution has the following problems:

- The cluster has the split-brain problem (!)
- The client data less node will try to do requests across different
data centers (is there a solution to this???). I can't find a way to avoid
this. We don't want this to happen because of a) latency and b) firewalling
issues.

So we started to think that this solution is not really viable. So we
thought of having one cluster per data center, which seems more sensible.
But then here we have the problem that we must publish data to all clusters
and, if one fails, we have no means of rolling back (unless we try to set
up a complicated version based rollback system). I find this very
complicated and hard to maintain, although can be somewhat doable.

My biggest problem is that we have to keep the data centers in the same
state at any time, so that if one goes down, we can readily switch to the
other.

Any ideas, or can you recommend some support to help use deal with this?

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Re: Problem with keeping in sync Elasticsearch across two data centers

2014-02-21 Thread Michael Sick

Dario,

I believe that you're looking for TribeNodes
http://www.elasticsearch.org/guide/en/elasticsearch/reference/master/modules-tribe.html

ES is not built to consistently cluster across DC's / larger network lags.

On Fri, Feb 21, 2014 at 11:24 AM, Dario Rossi dario...@gmail.com wrote:

At the very beginning we thought of having one large cluster that goes
across data centers (crazy). This solution has the following problems:

- The cluster has the split-brain problem (!)
- The client data less node will try to do requests across different data
centers (is there a solution to this???). I can't find a way to avoid this.
We don't want this to happen because of a) latency and b) firewalling
issues.

My biggest problem is that we have to keep the data centers in the same
state at any time, so that if one goes down, we can readily switch to the
other.

Any ideas, or can you recommend some support to help use deal with this?

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performing range summary on the result of a metric within a bucket

2014-01-31 Thread Michael Sick

I have what will be a long running time series table that I would like to
aggregate by time buckets and analyze:

   1. Query  Filter (in this case by dates  a term query on user_guid)
   (Working)
   2. Bucket by time for analysis (working using the aggs date_aggregate
   and heartRate_stats aggs
   3. Perform range counts on the metrics produced by the metrics within
   the buckets. *Not working. *The heartRate_zoneCounts1 aggregator gets
   zero counts - seems you can't point from the top down. The
   heartRate_zoneCounts aggregator does counts of the individual documents
   within the bucket (nice - but not what I'm looking for).

So how would I apply a range aggregate to the outcome of the
heartRate_stats metric and get only one value per date_aggregate bucket? I
can post process the results to apply a range but would rather have ES do
it for me. Any / all help apprecuated. Thanks in advance!
--Mike

*The Results*
mikeasick https://gist.github.com/mikeasick /
*gist:8734325*https://gist.github.com/mikeasick/8734325

*The Data (per second stream of the information below)*
mikeasick https://gist.github.com/mikeasick / *gist:8734404
https://gist.github.com/mikeasick/8734404*

*The Search*
https://gist.github.com/mikeasick/8734117


curl -XGET http://localhost:9200/vitals/vital/_search?pretty=true; -d'
{

  size: 0,
  query: {
filtered: {
  query: {
match_all: {}
  },
  filter: {

and: {
  filters: [
{
  term: {user_guid: 0ad08904-c1cf-46cf-9a04-e0865c1cced2}
},
{
  numeric_range: {

recorded_time: {
  gte: 2013-01-05T04:44:33.396-05:00,
  lte: 2014-01-05T04:44:33.396-05:00
}
  }
}

  ]
}
  }
}

  },
  aggs: {
date_aggregate: {
  date_histogram: {
field: recorded_time,
interval: 5m

  },
  aggs: {
heartRate_zoneAverageCounts: {

   range : {
field : heartRate_stats.avg,
ranges : [
{ to : 50 },
{ from : 50, to : 100 },

{ from : 100 }
]
}

},
heartRate_zoneCounts: {
   range : {

field : heartRate,
ranges : [
{ to : 50 },
{ from : 50, to : 100 },
{ from : 100 }

]
}
},
heartRate_stats: {
  extended_stats: {
field: heartRate

  }
}
  }
}

  }
}'

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Re: performing range summary on the result of a metric within a bucket

2014-01-31 Thread Michael Sick

Forgot to post the mapping (here mikeasickhttps://gist.github.com/mikeasick /
gist:8738689 https://gist.github.com/mikeasick/8738689) and that it's
using 1.0.0.Beta2 and Java 1.7.0_25 on Windows 7.

Thanks!

On Fri, Jan 31, 2014 at 10:42 AM, Michael Sick 
michael.s...@serenesoftware.com wrote:

 I have what will be a long running time series table that I would like to
 aggregate by time buckets and analyze:

1. Query  Filter (in this case by dates  a term query on user_guid)
(Working)
2. Bucket by time for analysis (working using the aggs
date_aggregate and heartRate_stats aggs
3. Perform range counts on the metrics produced by the metrics within
the buckets. *Not working. *The heartRate_zoneCounts1 aggregator
gets zero counts - seems you can't point from the top down. The
heartRate_zoneCounts aggregator does counts of the individual documents
within the bucket (nice - but not what I'm looking for).

 So how would I apply a range aggregate to the outcome of the
 heartRate_stats metric and get only one value per date_aggregate bucket? I
 can post process the results to apply a range but would rather have ES do
 it for me. Any / all help apprecuated. Thanks in advance!
 --Mike

 *The Results*
 mikeasick https://gist.github.com/mikeasick / 
 *gist:8734325*https://gist.github.com/mikeasick/8734325

 *The Data (per second stream of the information below)*
 mikeasick https://gist.github.com/mikeasick / *gist:8734404
 https://gist.github.com/mikeasick/8734404*

 *The Search*
 https://gist.github.com/mikeasick/8734117


 curl -XGET http://localhost:9200/vitals/vital/_search?pretty=true; -d'
 {

   size: 0,
   query: {
 filtered: {
   query: {
 match_all: {}
   },
   filter: {

 and: {
   filters: [
 {
   term: {user_guid: 0ad08904-c1cf-46cf-9a04-e0865c1cced2}
 },
 {
   numeric_range: {

 recorded_time: {
   gte: 2013-01-05T04:44:33.396-05:00,
   lte: 2014-01-05T04:44:33.396-05:00

 }
   }
 }

   ]
 }
   }
 }

   },
   aggs: {
 date_aggregate: {
   date_histogram: {
 field: recorded_time,
 interval: 5m

   },
   aggs: {
 heartRate_zoneAverageCounts: {

range : {
 field : heartRate_stats.avg,
 ranges : [
 { to : 50 },
 { from : 50, to : 100 },

 { from : 100 }
 ]
 }

 },
 heartRate_zoneCounts: {
range : {

 field : heartRate,
 ranges : [
 { to : 50 },
 { from : 50, to : 100 },
 { from : 100 }

 ]
 }
 },
 heartRate_stats: {
   extended_stats: {
 field: heartRate

   }
 }
   }
 }

   }
 }'




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Re: Elasticsearch and MongoDB without River

Re: Elasticsearch 1.1.0 - Optimize broken?

Re: Advice for implementing a secure graph index with ElasticSearch

Re: Advice for implementing a secure graph index with ElasticSearch

Re: Insert later feature

Re: Problem with keeping in sync Elasticsearch across two data centers

Re: Problem with keeping in sync Elasticsearch across two data centers

performing range summary on the result of a metric within a bucket

Re: performing range summary on the result of a metric within a bucket

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