So to begin. I am trying to add around 7.2k documents. No problem there. The issue is after I am not able to get any suggestions returned to me. So this is how the information is added:
def addVariantToElasticSearch(self,docId, companyId, companyName, parent, companyIndustry, variants, count,conn):
body = { "company":{
"company_name": companyName,
"parent": parent,
"suggest": { "input": variants,
"output": companyName,
"weight": count,
"payload": {"industry_id": companyIndustry,
"no_of_jobseekers":count,
"company_id": companyId
}
}
}
}
res = conn.index(body=body, index="companies", doc_type="company", id=docId)
The mapping and settings is defined as:
def setting():
return { "settings" : {
"index": {
"number_of_replicas" : 0,
"number_of_shards": 1
},
"analysis" : {
"analyzer" : {
"my_edge_ngram_analyzer" : {
"tokenizer" : "my_edge_ngram_tokenizer",
"filter":["standard", "lowercase"]
}
},
"tokenizer" : {
"my_edge_ngram_tokenizer" : {
"type" : "edgeNGram",
"min_gram" : "1",
"max_gram" : "5",
"token_chars": [ "letter", "digit" ]
}
}
}
},
"mappings": {
"company" : {
"properties" : {
"name" : { "type" : "string" },
"industy": {"type": "integer"},
"count" : {"type": "long" },
"parent": {"type": "string"},
"suggest" : {
"type" : "completion",
"index_analyzer": "my_edge_ngram_analyzer",
"search_analyzer": "my_edge_ngram_analyzer",
"payloads": True
}
}
}
}
}
Index creation:
def createMapping(es):
settings = setting()
es.indices.create(index="companies", body=settings)
I call createMapping which uses setting(), then add each variant - surrounded by a try,except -> causes no issue. I can see all my documents added in the browser as well as looking at the status, settings and mappings.
But when I use a curl request as below, I get no results. (See curl and output beneath)
curl -X POST localhost:9200/companies/_suggest -d '
{
"company-suggest" : {
"text" : "1800",
"completion" : {
"field" : "suggest"
}
}
}'
{
"_shards" : {
"total" : 1,
"successful" : 1,
"failed" : 0
},
"suggest" : [ {
"text" : "ruby",
"offset" : 0,
"length" : 4,
"options" : [ ]
} ]
I am currently using ES 1.1.0. I have tried both Python API 0.4 and 1.1.0 with no luck (I tried 0.4 as a result of 1.1.0 not working although I know it isn't best to due to compatibility issues with version of ES). I have also been able to add the same settings with mappings via curl and added a company which I have been able to retrieve by this curl above.
I'm not sure exactly where the issue lies. I have looked at the Data folder in ES to ensure it has been created, as well as the browser. I have also ensured only a single ES instance is running.
Any help greatly appreciated,
Related
I have an explicit JSON input that I want to transform into metadata driven generic objects within an array. I have successfully done this on an individual basis however, I want to be able to do it using a configuration file instead.
What I have below is an example of the input data, the configuration I want to apply and the output data.
Since it is outputing into a generic schema, no matter what the input value data type is, I want it to always output as a string.
In addition, the origin data may not always exist in the origin payload so, when I did an individual one of these, I used try which worked really well however, when doing it via a second file of configuration, I am not sure if it will still be the same method, I guess it would, I expect a loop through the configuration file and it create whatever it can else skips to the next one until completed.
INPUT ORIGIN DATA
{
"activities_acceptance" : {
"contractors_sub_contractors" : {
"contractors_subcontractors_engaged" : "yes"
},
"cooking_deep_frying" : {
"deep_frying_engaged" : "yes",
"deep_fryer_vat_limit" : 10
}
},
"situation_acceptance" : {
"building_construction" : {
"wall_materials" : "CONCRETE"
}
}
}
CONFIGURATION PARAMETERS
{
"processiong_configuration" : [
{
"origin_path" : "activities_acceptance.contractors_sub_contractors",
"set_category" : "business-activity",
"set_type" : "contractors-subcontractors",
"set_value" : [
{
"use_value" : "activities_acceptance.contractors_sub_contractors.contractors_subcontractors_engaged",
"set_value" : "value"
}
]
},
{
"origin_path" : "activities_acceptance.cooking_deep_frying",
"set_category" : "business-activity",
"set_type" : "cooking-deep-frying",
"set_value" : [
{
"use_value" : "activities_acceptance.cooking_deep_frying.deep_frying_engaged",
"set_value" : "value"
},
{
"use_value" : "activities_acceptance.cooking_deep_frying.deep_fryer_vat_limit",
"set_value" : "details"
}
]
},
{
"origin_path" : "situation_acceptance.building_construction",
"set_category" : "situation-materials",
"set_type" : "wall-materials",
"set_value" : [
{
"use_value" : "situation_acceptance.building_construction.wall_materials",
"set_value" : "CONCRETE"
}
]
}
]
}
EXPECTED OUTPUT
{
"characteristics" : [
{
"category" : "business-activity",
"type" : "contractors-subcontractors",
"value" : "yes"
},
{
"category" : "business-activity",
"type" : "deep-frying",
"value" : "yes",
"details" : "10"
},
{
"category" : "situation-materials",
"type" : "wall-materials",
"value" : "CONCRETE"
}
]
}
What I currently have for a single transform without configuration is the following:
# Create Business Characteristics
business_characteristics = {
"characteristics" : []
}
# Create Characteristics - Business - Liability
# if liability section exists logic to go in here
try:
acc_liability = {
"category" : "business-activities",
"type" : "contractors-sub-contractors-engaged",
"description" : "",
"value" : "",
"details" : ""
}
acc_liability['value'] = d['line_of_businesses'][0]['assets']['commercial_operations'][0]['liability_asset']['acceptance']['contractors_and_subcontractors']['contractors_and_subcontractors_engaged']
acc_liability['details'] = d['line_of_businesses'][0]['assets']['commercial_operations'][0]['liability_asset']['acceptance']['contractors_and_subcontractors']['types_of_work_contractors_performed']
business_characteristics['characteristics'].append(acc_liability)
except:
acc_liability = {}
CURRENT OUTPUT in Jupyter
{
"characteristics": [
{
"category": "business-activities",
"type": "contractors-sub-scontractors-engaged",
"description": "",
"value": "YES",
"details": ""
}
]
}
I have a field distribution in record schema that looks likes this:
...
"distribution": {
"properties": {
"availability": {
"type": "keyword"
}
}
}
...
I want to rank the records with distribution.availability == "ondemand" lower than other records.
I looked in Elasticsearch docs but can't find a way to reduce the scores of this type of records in index-time to appear lower in search results.
How can I achieve this, any pointers to related source would be enough as well.
More Info:
I was completely omitting these ondemand records with help of python client in query-time like this:
from elasticsearch_dsl.query import Q
_query = Q("query_string", query=query_string) & ~Q('match', **{'availability.keyword': 'ondemand'})
Now, I want to include these records but I want to place them lower than other records.
If it is not possible to implement something like this in index-time, please suggest how can I achieve this in query-time with python client.
After applying the suggestion from llermaly, the python client query looks like this:
boosting_query = Q(
"boosting",
positive=Q("match_all"),
negative=Q(
"bool", filter=[Q({"term": {"distribution.availability.keyword": "ondemand"}})]
),
negative_boost=0.5,
)
if query_string:
_query = Q("query_string", query=query_string) & boosting_query
else:
_query = Q() & boosting_query
EDIT2 : elasticsearch-dsl-py version of boosting query
from elasticsearch import Elasticsearch
from elasticsearch_dsl import Search
from elasticsearch_dsl import Q
client = Elasticsearch()
q = Q('boosting', positive=Q("match_all"), negative=Q('bool', filter=[Q({"term": {"test.available.keyword": "ondemand"}})]), negative_boost=0.5)
s = Search(using=client, index="test_parths007").query(q)
response = s.execute()
print(response)
for hit in response:
print(hit.meta.score, hit.test.available)
EDIT : Just read you need to do it on index time.
Elasticsearch deprecated index time boosting on 5.0
https://www.elastic.co/guide/en/elasticsearch/reference/7.11/mapping-boost.html
You can use a Boosting query to achieve that on query time.
Ingest Documents
POST test_parths007/_doc
{
"name": "doc1",
"test": {
"available": "ondemand"
}
}
POST test_parths007/_doc
{
"name": "doc1",
"test": {
"available": "higherscore"
}
}
POST test_parths007/_doc
{
"name": "doc2",
"test": {
"available": "higherscore"
}
}
Query (index time)
POST test_parths007/_search
{
"query": {
"boosting": {
"positive": {
"match_all": {}
},
"negative": {
"term": {
"test.available.keyword": "ondemand"
}
},
"negative_boost": 0.5
}
}
}
Response
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 1.0,
"hits" : [
{
"_index" : "test_parths007",
"_type" : "_doc",
"_id" : "VMdY7XcB50NMsuQPelRx",
"_score" : 1.0,
"_source" : {
"name" : "doc2",
"test" : {
"available" : "higherscore"
}
}
},
{
"_index" : "test_parths007",
"_type" : "_doc",
"_id" : "Vcda7XcB50NMsuQPiVRB",
"_score" : 1.0,
"_source" : {
"name" : "doc1",
"test" : {
"available" : "higherscore"
}
}
},
{
"_index" : "test_parths007",
"_type" : "_doc",
"_id" : "U8dY7XcB50NMsuQPdlTo",
"_score" : 0.5,
"_source" : {
"name" : "doc1",
"test" : {
"available" : "ondemand"
}
}
}
]
}
}
For more advanced manipulation you can check the Function Score Query
We've been using Avro IDL to define message sets used on our Kafka back end and are quite happy with it. We've also been interested in tying to validate JSON to a REST api on a Python Flask app with the Avro Schema as well and have been running into some difficulty.
There are a variety of packages out there but I have yet to find something that clearly works the way I need it to. I'm hoping for some guidance.
I can take my avdl file and generate a set of avsc files with:
avro-tools idl2schemata message.avdl output_dir
or
avro-tools idl message.avdl > output_dir/schema.avsc
And I'm able to read these in python but I've found nothing "easy" that can just tell me if my JSON input matches the schema.
Has anybody done something similar? Am I going down the wrong path? Any advice would be appreciated thanks.
I know if I was playing in the SpringBoot land this would likely be VERY simple.
Thanks
IDL
#namespace("org.jeeftor.avro")
protocol TacoRequest {
enum MeatType{
CHICKEN,
BEEF,
TURKEY,
FISH
}
enum CheeseType {
GROSS_VEGAN,
ACTUAL_COW_CHEESE,
GOAT_CHEESE
}
enum Toppings {
LECHUGA,
TOMATO,
SAUCE
}
record Taco {
MeatType meat;
CheeseType cheese;
array<Toppings> toppings;
}
record Order {
union { string, int } order_id;
array<Taco> tacos;
}
}
Schema
I generate the schema with: avro-tools idl order.avdl protocol.avpr
{
"protocol" : "TacoRequest",
"namespace" : "org.jeeftor.avro",
"types" : [ {
"type" : "enum",
"name" : "MeatType",
"symbols" : [ "CHICKEN", "BEEF", "TURKEY", "FISH" ]
}, {
"type" : "enum",
"name" : "CheeseType",
"symbols" : [ "GROSS_VEGAN", "ACTUAL_COW_CHEESE", "GOAT_CHEESE" ]
}, {
"type" : "enum",
"name" : "Toppings",
"symbols" : [ "LECHUGA", "TOMATO", "SAUCE" ]
}, {
"type" : "record",
"name" : "Taco",
"fields" : [ {
"name" : "meat",
"type" : "MeatType"
}, {
"name" : "cheese",
"type" : "CheeseType"
}, {
"name" : "toppings",
"type" : {
"type" : "array",
"items" : "Toppings"
}
} ]
}, {
"type" : "record",
"name" : "Order",
"fields" : [ {
"name" : "order_id",
"type" : [ "string", "int" ]
}, {
"name" : "tacos",
"type" : {
"type" : "array",
"items" : "Taco"
}
} ]
} ],
"messages" : { }
}
My question is how to easily use this "schema" to validate input.
I have been using basic queries in python for elasticsearch like this:
from elasticsearch import Elasticsearch
from elasticsearch_dsl import Search
es = Elasticsearch()
def someView(request):
s = Search().query("regexp", title_en="someword.*")
response = s.execute()
I would like to combine a query to check if someword exists in either of the fields "title_en" or "text_en"
Any idea how to accomplish this?
In this link I saw an example of a bool query using JSON, but I don´t understand how something similar could be done with python code.
{
"query": {
"bool" : {
"must" : {
"term" : { "user" : "kimchy" }
},
"filter": {
"term" : { "tag" : "tech" }
},
"must_not" : {
"range" : {
"age" : { "gte" : 10, "lte" : 20 }
}
},
"should" : [
{ "term" : { "tag" : "wow" } },
{ "term" : { "tag" : "elasticsearch" } }
],
"minimum_should_match" : 1,
"boost" : 1.0
}
}
}
Figured out the way to use or in query :
q = Q("regexp", text_en='someword.*') | Q("regexp", title_en='someword.*')
c = Search().query(q)
response = c.execute()
I am trying to use a custom query DSL to get results using the pyes library. I have query DSL that works when I use the command line
curl -XGET localhost:9200/test_index/_search -d '{
"query": {
"function_score": {
"query": {
"match_all": {}
},
"field_value_factor": {
"field": "starred",
"modifier": "none",
"factor": 2
}
}
},
"aggs" : {
"types" : {
"filters" : {
"filters" : {
"category1" : { "type" : { "value" : "category1"}},
"category2" : { "type" : { "value" : "category2"}},
"category3" : { "type" : { "value" : "category3"}},
"category4": { "type" : { "value" : "category4"}},
"category5" : { "type" : { "value" : "category5"}}
}
},
"aggs": {
"topFoundHits": {
"top_hits": {
"size": 5
}
}
}
}
}
}'
The idea here is to search across many categorized documents for all documents matching a particular string query. Then using aggregations I want to find the top five resulting documents by category. Starred items are boosted so that they show up above other search results.
This works great when I enter the command as listed above directly in terminal but it doesn't work when I try to put it in pyes. I'm not sure what the best way is to do it. The documentation for the pyes library is really confusing for me to translate this totally into pyes objects.
I'm trying to do the following:
query_dsl = self.get_text_index_query_dsl()
resulting_docs = conn.search(query=query_dsl)
(where self.get_test_index_query_dsl returns the query dsl dict above)
Searching as is gives me a:
ElasticSearchException: QueryParsingException[[test_index] No query registered for [query]]; }]
If I remove the parent "query" mapping and try:
query_dsl = {
"function_score": {
"query": {
"match_all": {}
},
"field_value_factor": {
"field": "starred",
"modifier": "none",
"factor": 2
}
},
"aggs" : {
"types" : {
"filters" : {
"filters" : {
"category1" : { "type" : { "value" : "category1"}},
"category2" : { "type" : { "value" : "category2"}},
"category3" : { "type" : { "value" : "category3"}},
"category4": { "type" : { "value" : "category4"}},
"category5" : { "type" : { "value" : "category5"}}
}
},
"aggs": {
"topFoundHits": {
"top_hits": {
"size": 5
}
}
}
}
}
}
This also errors out with: ElasticSearchException: ElasticsearchParseException[Expected field name but got START_OBJECT "aggs"]; }]
These errors in addition to the fact that pyes doesn't seem to have a 'topFoundHits' functionality yet (I think) are holding me up.
Any ideas why this is happening and how to fix it?
Thank you so much!
I got this working using this library where you can just use your regular query dsl JSON syntax : http://elasticsearch-dsl.readthedocs.org/en/latest/.