On Tuesday, September 23, 2014 2:18:35 PM UTC+12, Tim Uckun wrote:
I have documents with an array field. The array contains unique elements
only. In this case the data is strings and but it could be numbers.
I want to search for documents using this array field. Ideally I would
like
I'm just confused if Cassandra can really make a difference here, since
looks to me ES can suffice here.
If you are not going to be using Cassandra for indexing then there is no
reason to have it. If you want durability in case something goes wrong with
ES you can just store your data
I would like to have a river in reverse. Every time a document is inserted
or modified I would like to push that into another destination like a
database. Ideally this would be async or maybe even in batches.
Has anybody done anything like this before?
--
You received this message because
To follow up on this...
As a general rule is it better to have one horse size index or a hundred
duck sized indices. I am thinking about those types of searches where you
might frequently search a subset of the data. For example keeping a
separate index for every customer because normally the
I have roughly 50 million records in ES. The data was generated
artificially and is mostly duplicates. I am executing the following query
{
size: 0,
aggregations: {
by_month: {
date_histogram: {
field:time_stamp,
I hate to bump myself but does anybody have any input on this at all?
On Thursday, April 10, 2014 11:59:43 AM UTC+12, Tim Uckun wrote:
I want to do something like this.
select date_trunc('month', time_stamp), sum(distinct_count) from (
select date_trunc('week', time_stamp
On Wednesday, April 9, 2014 7:49:13 PM UTC+12, Alexander Reelsen wrote:
Hey,
can you try to use date formats instead of the named identificators like
date_time and see if it works? Also, can you check the exception of the
elasticsearch logs?
I tried the -MM-dd .. it gives an
I want to do something like this.
select date_trunc('month', time_stamp), sum(distinct_count) from (
select date_trunc('week', time_stamp) as time_stamp, count(distinct
field_name) as distinct_count
from blah
group by date_trun('week', time_stamp)
)
group by
I have a search like this
{
size: 0,
query:{
match_all: {}
},
aggregations: {
min_date: {min: {field: 'time_stamp', format: 'date_time'}},
max_date: {max: {field: 'time_stamp', format: -MM-dd HH:mm}}
}
}
This fails with a parse
Is ES a suitable replacement for Cassandra for an analytics platform? I
need high speed data ingestion, time series analysis, rollups and
aggregations etc. Cassandra is used for this kind of task often but it
seems to me ES might be a suitable if not better replacement.
Cheers.
--
You
10 matches
Mail list logo
