My System is calling elasticsearch. After updating a document I would like to fetch the same document again. While doing so elasticsearch sometimes fetches cached results (results before the update) even after retrying the elasticsearch get after 5-6 seconds.
I have used refresh:'wait_for' while updating the document. Can anyone help me what can be a workaround for this? I would like to fetch the latest revision of the updated document. My query to fetch is:
body: {
query: {
terms: {
_id: [
idsToFetch
]
}
}
}
First, you can check whats the refresh interval set for your index defaults to 1 second, in this case: refresh:wait_for should return back in maximum 1 second but as explained in official ES documents :
If the refresh interval is set to -1, disabling the automatic
refreshes, then requests with refresh=wait_for will wait indefinitely
until some action causes a refresh. Conversely, setting
index.refresh_interval to something shorter than the default like
200ms will make refresh=wait_for come back faster, but it’ll still
generate inefficient segment
You can get the whats the refresh_interval set for index using https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-get-settings.html, please note it would come in the result only if it's not set to its default value.
Let me know if you face any issue or have more question.
Related
To create a new point a time we do
POST /my-index-000001/_pit?keep_alive=1m
output:
{
"id": "46ToAwMDaWR5BXV1aWQyKwZub2RlXzMAAAAAAAAAACoBYwADaWR4BXV1aWQxAgZub2RlXzEAAAAAAAAAAAEBYQADaWR5BXV1aWQyKgZub2RlXzIgAAAAA=="
}
It will return a PIT ID.
Now to get the total number of open search, we do
GET /_nodes/stats/indices/search
and output has
"open_contexts" : 18,
As Elastic says that we should close/delete the PIT after the work is done. Close the Point in time
Now the question:
how can we get ids of all open search or PITs?
if we can not get the Ids, is there any way to close the search after a time or it is auto-closed after keep-alive time ended.
I am not able to find any documentation regarding this.
There's no way to do retrieve the IDs of the opened search contexts, you're supposed to keep track of them in your application code.
As the link you provided states:
Point-in-time is automatically closed when its keep_alive has been elapsed.
So in your case, if you don't issue a new query after 1 minute, then the search context will be automatically closed.
My friend stored 65000 documents on the Elastic Search cloud and I would like to retrieve all of them (using python). However, when I am running my current script, there is an error noticing that :
RequestError(400, 'search_phase_execution_exception', 'Result window is too large, from + size must be less than or equal to: [10000] but was [30000]. See the scroll api for a more efficient way to request large data sets. This limit can be set by changing the [index.max_result_window] index level setting.')
My script
es = Elasticsearch(cloud_id=cloud_id, http_auth=(username, password))
docs = es.search(body={"query": {"match_all": {}}, '_source': ["_id"], 'size': 65000})
What would be the easiest way to retrieve all those document and not limit it to 10000 docs? thanks
The limit has been set so that the result set does not overwhelm your nodes. Results will occupy memory in the elastic node. So bigger the result set, bigger the memory footprint and impact on the nodes.
Depending on what you want to do with the retrieved documents,
try to use the scroll api (as suggested in your error message) if its a batch job. Be mindful of the lifetime of scroll context in that case.
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-body.html#request-body-search-scroll
or, use the Search After
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-request-body.html#request-body-search-search-after
You should use the scroll API and get the results in different calls. The scroll API will return to you the results 10000 by 10000 as maximum (that will be available to consult during the amount of time you indicate in the call) and you will be able then to paginate the results and obtain them thanks to a scroll_id.
The error message itself is mentioning that how can you solve the issue, look carefully this part of the error message.
This limit can be set by changing the [index.max_result_window] index
level setting.
Please refer update indices level setting on how to change that.
So for your setting it would look like:
PUT /<your-index-name>/_settings
{
"index" : {
"index.max_result_window" : 65000 -> note its equal to your all the docs in your index
}
}
I am using Elasticsearch version 5.6.10. I have a query that deletes records for a given agency, so they can later be updated by a nightly script.
The query is in elasticsearch-dsl and look like this:
def remove_employees_from_search(jurisdiction_slug, year):
s = EmployeeDocument.search()
s = s.filter('term', year=year)
s = s.query('nested', path='jurisdiction', query=Q("term", **{'jurisdiction.slug': jurisdiction_slug}))
response = s.delete()
return response
The problem is I am getting a ConflictError exception when trying to delete the records via that function. I have read this occurs because the documents were different between the time the delete process started and executed. But I don't know how this can be, because nothing else is modifying the records during the delete process.
I am going to add s = s.params(conflicts='proceed') in order to silence the exception. But this is a band-aid as I do not understand why the delete is not processing as expected. Any ideas on how to troubleshoot this? A snapshot of the error is below:
ConflictError:TransportError(409,
u'{
"took":10,
"timed_out":false,
"total":55,
"deleted":0,
"batches":1,
"version_conflicts":55,
"noops":0,
"retries":{
"bulk":0,
"search":0
},
"throttled_millis":0,
"requests_per_second":-1.0,
"throttled_until_millis":0,
"failures":[
{
"index":"employees",
"type":"employee_document",
"id":"24681043",
"cause":{
"type":"version_conflict_engine_exception",
"reason":"[employee_document][24681043]: version conflict, current version [5] is different than the one provided [4]",
"index_uuid":"G1QPF-wcRUOCLhubdSpqYQ",
"shard":"0",
"index":"employees"
},
"status":409
},
{
"index":"employees",
"type":"employee_document",
"id":"24681063",
"cause":{
"type":"version_conflict_engine_exception",
"reason":"[employee_document][24681063]: version conflict, current version [5] is different than the one provided [4]",
"index_uuid":"G1QPF-wcRUOCLhubdSpqYQ",
"shard":"0",
"index":"employees"
},
"status":409
}
You could try making it do a refresh first
client.indices.refresh(index='your-index')
source https://www.elastic.co/guide/en/elasticsearch/client/javascript-api/current/api-reference.html#_indices_refresh
First, this is a question that was asked 2 years ago, so take my response with a grain of salt due to the time gap.
I am using the javascript API, but I would bet that the flags are similar. When you index or delete there is a refresh flag which allows you to force the index to have the result appear to search.
I am not an Elasticsearch guru, but the engine must perform some systematic maintenance on the indices and shards so that it moves the indices to a stable state. It's probably done over time, so you would not necessarily get an immediate state update. Furthermore, from personal experience, I have seen when delete does not seemingly remove the item from the index. It might mark it as "deleted", give the document a new version number, but it seems to "stick around" (probably until general maintenance sweeps run).
Here I am showing the js API for delete, but it is the same for index and some of the other calls.
client.delete({
id: string,
index: string,
type: string,
wait_for_active_shards: string,
refresh: 'true' | 'false' | 'wait_for',
routing: string,
timeout: string,
if_seq_no: number,
if_primary_term: number,
version: number,
version_type: 'internal' | 'external' | 'external_gte' | 'force'
})
https://www.elastic.co/guide/en/elasticsearch/client/javascript-api/current/api-reference.html#_delete
refresh
'true' | 'false' | 'wait_for' - If true then refresh the affected shards to make this operation visible to search, if wait_for then wait for a refresh to make this operation visible to search, if false (the default) then do nothing with refreshes.
For additional reference, here is the page on Elasticsearch refresh info and what might be a fairly relevant blurb for you.
https://www.elastic.co/guide/en/elasticsearch/reference/current/indices-refresh.html
Use the refresh API to explicitly refresh one or more indices. If the request targets a data stream, it refreshes the stream’s backing indices. A refresh makes all operations performed on an index since the last refresh available for search.
By default, Elasticsearch periodically refreshes indices every second, but only on indices that have received one search request or more in the last 30 seconds. You can change this default interval using the index.refresh_interval setting.
When I index a document in ES, I am trying to access the same document within in the refresh interval has passed and the search is not returning the result. Is there a Realtime GET support which allows to get a document once indexed regardless of the "refresh rate" of the index. I tried reducing the refresh_interval to 500ms instead of 1s, but my search query happens even before 500 ms and it is not a good idea to reduce it even further.
After indexing a document, you can GET it immediately without waiting for the refresh interval.
The GET API is real-time
So if you index a new document like this
POST index/type/1
{ "name": "John Doe" }
You can get it immediately without waiting using
GET index/type/1
If you search, however, you'll need to wait for the refresh interval to pass in order to retrieve the new document or call the refresh API.
For completeness' sake, it's worth stating that when indexing you also have the option of refreshing the shards immediately, by passing the refresh=true parameter like below. Note, however, that this can have bad performance implications, so it should be used sparingly.
POST index/type/1?refresh=true
{ "name": "John Doe" }
Also worth noting that in ES 5, you'll have the option of telling ES to wait for a refresh before returning from the create call:
POST index/type/1?refresh=wait_for
{ "name": "John Doe" }
In this case, once the POST request returns, you're guaranteed that the new document is available in the next search call.
I am running a very simple performance experiment where I post 2000 documents to my application.
Who in tern persists them to a relational DB and sends them to Solr for indexing (Synchronously, in the same request).
I am testing 3 use cases:
No indexing at all - ~45 sec to post 2000 documents
Indexing included - commit after each add. ~8 minutes (!) to post and index 2000 documents
Indexing included - commitWithin 1ms ~55 seconds (!) to post and index 2000 documents
The 3rd result does not make any sense, I would expect the behavior to be similar to the one in point 2. At first I thought that the documents were not really committed but I could actually see them being added by executing some queries during the experiment (via the solr web UI).
I am worried that I am missing something very big. Is it possible that committing after each add will degrade performance by a factor of 400?!
The code I use for point 2:
SolrInputDocument = // get doc
SolrServer solrConnection = // get connection
solrConnection.add(doc);
solrConnection.commit();
Where as the code for point 3:
SolrInputDocument = // get doc
SolrServer solrConnection = // get connection
solrConnection.add(doc, 1); // According to API documentation I understand there is no need to call an explicit commit after this
According to this wiki:
https://wiki.apache.org/solr/NearRealtimeSearch
the commitWithin is a soft-commit by default. Soft-commits are very efficient in terms of making the added documents immediately searchable. But! They are not on the disk yet. That means the documents are being committed into RAM. In this setup you would use updateLog to be solr instance crash tolerant.
What you do in point 2 is hard-commit, i.e. flush the added documents to disk. Doing this after each document add is very expensive. So instead, post a bunch of documents and issue a hard commit or even have you autoCommit set to some reasonable value, like 10 min or 1 hour (depends on your user expectations).