How to check a particular value on basis of condition in karate - karate

Goal: Match the check value is correct for 123S and 123O response in API
First check the value on this location x.details[0].user.school.name[0].codeable.text if it is 123S then check if x.details[0].data.check value is abc
Then check if the value on this location x.details[1].user.school.name[0].codeable.text is 123O then check if x.details[1].data.check is xyz
The response in array inter changes it is not mandatory first element is 123S sometime API returns 123O as first array response.
Sample JSON.
{
"type": "1",
"array": 2,
"details": [
{
"path": "path",
"user": {
"school": {
"name": [
{
"value": "this is school",
"codeable": {
"details": [
{
"hello": "yty",
"condition": "check1"
}
],
"text": "123S"
}
}
]
},
"sample": "test1",
"id": "22222"
},
"data": {
"check": "abc"
}
},
{
"path": "path",
"user": {
"school": {
"name": [
{
"value": "this is school",
"codeable": {
"details": [
{
"hello": "def",
"condition": "check2"
}
],
"text": "123O"
}
}
]
},
"sample": "test",
"id": "11111"
},
"data": {
"check": "xyz"
}
}
]
}
How I did in Postman but how to replicate same in Karate?
var jsonData = pm.response.json();
pm.test("Body matches string", function () {
for(var i=0;i<jsonData.details.length;i++){
if(jsonData.details[i].user.school.name[0].codeable.text == '123S')
{
pm.expect(jsonData.details[i].data.check).to.equal('abc');
}
if(jsonData.details[i].user.school.name[0].codeable.text == '123O')
{
pm.expect(jsonData.details[i].data.check).to.equal('xyz');
}
}
});

2 lines. And this takes care of any number of combinations of lookup values :)
* def lookup = { '123S': 'abc', '123O': 'xyz' }
* match each response.details contains { data: { check: '#(lookup[_$.user.school.name[0].codeable.text])' } }

Related

JSON element extraction from response based on scenario outline examples or external file

This is my api response. Want to extract the value of the Id based on the displayNumber. This display number is a given in the list of values in examples/csv file.
{
"Acc": [
{
"Id": "2b765368696b3441673633325",
"code": "SGD",
"val": 406030.83,
"displayNumber": "8957",
"curval": 406030.83
},
{
"Id": "4e676269685a73787472355776764b50717a4",
"code": "GBP",
"val": 22.68,
"displayNumber": "1881",
"curval": 22.68
},
{
"Id": "526e666d65366e67626244626e6266467",
"code": "SGD",
"val": 38404.44,
"displayNumber": "1004",
"curval": 38404.44
},
],
"combinations": [
{
"displayNumber": "3444",
"Code": "SGD",
"Ids": [
{
"Id": "2b765368696b34416736333254462"
},
{
"Id": "4e676269685a7378747235577"
},
{
"Id": "526e666d65366e6762624d"
}
],
"destId": "3678434b643530456962435272d",
"curval": 3.85
},
{
"displayNumber": "8957",
"code": "SGD",
"Ids": [
{
"Id": "3678434b6435304569624357"
},
{
"Id": "4e676269685a73787472355776764b50717a4"
},
{
"Id": "526e666d65366e67626244626e62664679"
}
],
"destId": "2b765368696b344167363332544",
"curval": 406030.83
},
{
"displayNumber": "1881",
"code": "GBP",
"Ids": [
{
"Id": "3678434b643530456962435275"
},
{
"Id": "2b765368696b3441673"
},
{
"Id": "526e666d65366e67626244626e626"
}
],
"destId": "4e676269685a7378747d",
"curval": 22.68
},
]
}
Examples
|displayNumber|
|8957|
|3498|
|4943|
Below expression works if i give the value
* def tempid = response
* def fromAccount = get[0] tempid.Acc[?(#.displayNumber==8957].Id
I'm not sure how to make this comparison value (i.e. 1881) as a variable which can be read from examples (scenario outline) or a csv file. Went through the documentation, which recommends, karate filters or maps. However, not able to follow how to implement.
You almost got it :-). This is the way you want to solve this
Scenario Outline: Testing SO question for Navneeth
* def tempid = response
* def fromAccount = get[0] tempid.Acc[?(#.displayNumber == <displayNumber>)]
* print fromAccount
Examples:
|displayNumber|
|8957|
|1881|
|3444|
You need to pass the placeholder in examples as -
'<displayNumber>'

How to avoid the duplicated data entry after parsing json in kusto?

I have following sample json data.
{
"data": {
"type": "ABC",
"id": "17495500314",
"attributes": {
[!["event": "update",
"gps_vali][1]][1]d": true,
"gps": {
"distance_diff": 6.48,
"total_distance": 848.6
},
"hdop": 79,
"fuel_level": 46.8,
"total_fuel_used": 60443.9,
"location": {
"latitude": 411.372618,
"longitude": -1.254931,
"relative_position": {
"distance": "37",
}
},
"idle_periods": []
},
"relationships": {
"assets": {
"data": [
{
"type": "ABCDFTTG",
"id": "1589799143500003",
"attributes": {
"external_id": "ABCDFTTG",
"hardware_id": "ABCDFTTG"
}
}
]
},
"devices": {
"data": [
{
"type": "ABCDFTTG",
"id": "1585231172900341",
"attributes": {
"serial": "5572016191"
}
},
{
"type": "tablet",
"id": "1587893062600175",
"attributes": {
"serial": "ABCDFTTG"
}
}
]
},
"users": {
"data": [
{
"type": "user",
"id": "ABCDFTTG",
"attributes": {
"external_id": "ABCDFTTG"
}
}
]
}
}
},
"meta": {
"message_id": "11eb-8c75-0b3f87aedbb5",
"consumer_version": "1.2.0",
"origin_version": null,
"timestamp": "2021-06-14T17:42:29Z"
}
}
I want only one row instead of this two. Here is my kusto query which is used for parsing json data into table columns.
Test
|where messageId =="123"
//|mv-expand message=message.data.attributes
|mv-expand message
|mv-expand Value=message.data.relationships.assets.['data']
|mv-expand value_devices=message.data.relationships.devices.['data']
|mv-expand value_user=message.data.relationships.users.['data']
| project type=message.data.type,id=message.data.id,
event=tostring(message.data.attributes.event),
logged_at=tostring(message.data.attributes.logged_at),
distance=toint(message.data.attributes.location.relative_position.distance),
// Value=message.data.relationships.assets.['data'],//.['data']
type_asset=Value.type,asset_id=Value.id,
device_type=value_devices.type,device_id=value_devices.id,
device_attr_serial=value_devices.attributes.serial,
user_type=value_user.type,user_id=value_user.id,
user_external_id=value_user.attributes.external_id
This duplicate row appeared after adding user tag this tag is array so how to handle this array with single id.
I have parse my json data any got the following output.
Expected output should be like
check device_type and device_id columns

ES6: Joining of subqueries to two different rows through the AND operator

I have following index:
+-----+-----+-------+
| oid | tag | value |
+-----+-----+-------+
| 1 | t1 | aaa |
| 1 | t2 | bbb |
| 2 | t1 | aaa |
| 2 | t2 | ddd |
| 2 | t3 | eee |
+-----+-----+-------+
where: oid - object ID, tag - property name, value - property value.
Mappings:
"mappings": {
"document": {
"_all": { "enabled": false },
"properties": {
"oid": { "type": "integer" },
"tag": { "type": "text" }
"value": { "type": "text" },
}
}
}
This simple structure allows store any number of object properties and it is a quite simple to search by one property or by more using OR logical operator.
E.g. get object oid's where:
(tag='t1' AND value='aaa') OR (tag='t2' AND value='ddd')
ES query:
{
"_source": { "includes":["oid"] },
"query": {
"bool": {
"should": [
{
"bool": {
"must": [
{ "term": { "tag": "t1" } },
{ "term": { "value": "aaa" } }
]
}
},
{
"bool": {
"must": [
{ "term": { "tag": "t2" } },
{ "term": { "value": "ddd" } }
]
}
}
],
"minimum_should_match": "1"
}
}
}
But it is hard to search by two or more properties using AND logical operator. So the question is how to join two sub-queries to two different records through the AND operator. E.g. get object oid's where:
(tag='t1' AND value='aaa') AND (tag='t2' AND value='ddd')
In this case result must be: { "oid": "2" }
Searching data contains in two different records and applying MUST instead of SHOULD from the previous example returns nothing in this case.
I have two equivalents in SQL of what I need:
SELECT i1.[oid]
FROM [index] i1 INNER JOIN [index] i2 ON i1.oid = i2.oid
WHERE
(i1.tag='t1' AND i1.value='aaa')
AND
(i2.tag='t2' AND i2.value='ddd')
---------
SELECT [oid] FROM [index] WHERE tag='t1' AND value='aaa'
INTERSECT
SELECT [oid] FROM [index] WHERE tag='t2' AND value='ddd'
Do the two requests and merge them on the client is not the option.
Elastic Search version is 6.1.1
In order to achieve what you want, you need to use the nested type, i.e. your mapping should look like this:
PUT my-index
{
"mappings": {
"doc": {
"properties": {
"oid": {
"type": "keyword"
},
"data": {
"type": "nested",
"properties": {
"tag": {
"type": "keyword"
},
"value": {
"type": "text"
}
}
}
}
}
}
}
The documents would be indexed like this:
PUT /my-index/doc/_bulk
{ "index": {"_id": 1}}
{ "oid": 1, "data": [ {"tag": "t1", "value": "aaa"}, {"tag": "t2", "value": "bbb"}] }
{ "index": {"_id": 2}}
{ "oid": 2, "data": [ {"tag": "t1", "value": "aaa"}, {"tag": "t2", "value": "ddd"}, {"tag": "t3", "value": "eee"}] }
Then you can make your query work like this:
POST my-index/_search
{
"query": {
"bool": {
"filter": [
{
"nested": {
"path": "data",
"query": {
"bool": {
"filter": [
{
"term": {
"data.tag": "t1"
}
},
{
"term": {
"data.value": "aaa"
}
}
]
}
}
}
},
{
"nested": {
"path": "data",
"query": {
"bool": {
"filter": [
{
"term": {
"data.tag": "t2"
}
},
{
"term": {
"data.value": "ddd"
}
}
]
}
}
}
}
]
}
}
}
There might be one way, which is a little ugly: adding terms aggregations to your query body.
{
"query": {
"bool": {
"should": [
{
"bool": {
"must": [
{ "term": { "tag": "t1" } },
{ "term": { "value": "aaa" } }
]
}
},
{
"bool": {
"must": [
{ "term": { "tag": "t2" } },
{ "term": { "value": "ddd" } }
]
}
}
],
"minimum_should_match": "1"
}
},
"size": 0,
"aggs": {
"find_joined_oid": {
"terms": {
"field": "oid.keyword"
}
}
}
}
If everything goes right, this will output something like
{
"took": 123,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 123,
"max_score": 0,
"hits": []
},
"aggregations": {
"find_joined_oid": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "1",
"doc_count": 1
},
{
"key": "2",
"doc_count": 2
}
}
}
}
Here, in the "aggregations" part,
"key": "1"
means your "oid":"1", and
"doc_counts": 1
means there is 1 hit in query with "oid":"1".
As you know how many tags you are querying to match, say N, in the aggregations result body, only those "key"s with "doc_count" equal to N are the result you're pursuing. In this example, you are querying tag:t1 (with value aaa) and tag:t2 (with value ddd), thus N=2. You can iterate in the result bucket list to find out those "key"s who have "doc_count" equal to 2.
However, there should be a better way. If you would alter your mapping to a document like style, ie. store all fields of one oid in one doc, life will be much easier.
{
"properties": {
"oid": { "type": "integer" },
"tag-1": { "type": "text" }
"value-1": { "type": "text" },
"tag-2": { "type": "text" }
"value-2": { "type": "text" }
}
}
When you want to add new tag-value pairs, just get the original doc with oid concerned, put new tag-pair into the doc, and put the whole new doc back into Elasticsearch with the same _id which you get from the original one. Most of the time dynamic mapping will work properly in your case, which means you don't need to assert mapping for new fields explicitly.
No-SQL databases like Elasticsearch and others are not designed to handle such SQL style query you are asking.

make a new array from a nested object using Lodash

Here is my data
[
{
"properties": {
"key": {
"data": "companya data",
"company": "Company A"
}
},
"uniqueId" : 1
},
{
"properties": {
"key": {
"data": "companyb data",
"company": "Company B"
}
},
"uniqueId" : 2
},
{
"properties": {
"key": {
"data": "companyc data",
"company": "Company C"
}
},
"uniqueId" : 3
}
]
The format I need for my typeahead directive is below. I was trying to figure out the other post I made but still couldn't make it work. The best was to just make the nested collection as a simple collection of object.
[
{
"uniqueId" : 1,
"data": "companya data"
},
{
"uniqueId" : 2,
"data": "companyb data"
},
{
"uniqueId" : 3,
"data": "companyc data"
}
]
I got it!
console.log(
_(jsonData).map(function(obj) {
return {
d : obj.properties.key.data,
id : obj.uniqueId
}
})
.value()
);
You do not have to use the chaining feature of lodash as long as you are only performing one operation. You can simply use:
_.map(jsonData, function(obj) {
return {
d : obj.properties.key.data,
id : obj.uniqueId
}
});

hierarchical faceting with Elasticsearch

I'm using elasticsearch and need to implement facet search for hierarchical object as follow:
category 1 (10)
subcategory 1 (4)
subcategory 2 (6)
category 2 (X)
...
So I need to get facets for two related objects. Documentation says that it's possible to get such kind of facets for numeric value, but I need it for strings http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/search-facets-terms-stats-facet.html
Here is another interesting topic, unfortunately it's old: http://elasticsearch-users.115913.n3.nabble.com/Pivot-facets-td2981519.html
Does it possible with elastic search?
If so, how can I do that?
The previous solution works really well until you have no more than a multi-level tag on a single-document. In this case a simple aggregation doesn't work, because the flat structure of the lucene fields mix the results on the internal aggregation.
See the example below:
DELETE /test_category
POST /test_category
# Insert a doc with 2 hierarchical tags
POST /test_category/test/1
{
"categories": [
{
"cat_1": "1",
"cat_2": "1.1"
},
{
"cat_1": "2",
"cat_2": "2.2"
}
]
}
# Simple two-levels aggregations query
GET /test_category/test/_search?search_type=count
{
"aggs": {
"main_category": {
"terms": {
"field": "categories.cat_1"
},
"aggs": {
"sub_category": {
"terms": {
"field": "categories.cat_2"
}
}
}
}
}
}
That's the WRONG response that I have got on ES 1.4, where the fields on the internal aggregation are mixed at a document level:
{
...
"aggregations": {
"main_category": {
"buckets": [
{
"key": "1",
"doc_count": 1,
"sub_category": {
"buckets": [
{
"key": "1.1",
"doc_count": 1
},
{
"key": "2.2", <= WRONG
"doc_count": 1
}
]
}
},
{
"key": "2",
"doc_count": 1,
"sub_category": {
"buckets": [
{
"key": "1.1", <= WRONG
"doc_count": 1
},
{
"key": "2.2",
"doc_count": 1
}
]
}
}
]
}
}
}
A Solution can be to use nested objects. These are the steps to do:
1) Define a new type in the schema with nested objects
POST /test_category/test2/_mapping
{
"test2": {
"properties": {
"categories": {
"type": "nested",
"properties": {
"cat_1": {
"type": "string"
},
"cat_2": {
"type": "string"
}
}
}
}
}
}
# Insert a single document
POST /test_category/test2/1
{"categories":[{"cat_1":"1","cat_2":"1.1"},{"cat_1":"2","cat_2":"2.2"}]}
2) Run a nested aggregation query:
GET /test_category/test2/_search?search_type=count
{
"aggs": {
"categories": {
"nested": {
"path": "categories"
},
"aggs": {
"main_category": {
"terms": {
"field": "categories.cat_1"
},
"aggs": {
"sub_category": {
"terms": {
"field": "categories.cat_2"
}
}
}
}
}
}
}
}
That's the response, now correct, that I have got:
{
...
"aggregations": {
"categories": {
"doc_count": 2,
"main_category": {
"buckets": [
{
"key": "1",
"doc_count": 1,
"sub_category": {
"buckets": [
{
"key": "1.1",
"doc_count": 1
}
]
}
},
{
"key": "2",
"doc_count": 1,
"sub_category": {
"buckets": [
{
"key": "2.2",
"doc_count": 1
}
]
}
}
]
}
}
}
}
The same solution can be extended to a more than two-levels hierarchy facet.
Currently, elasticsearch does not support hierarchical facetting out-of-the-box. But the upcoming 1.0 release features a new aggregations module, that can be used to get these kind of facets (which are more like pivot-facets rather than hierarchical facets). Version 1.0 is currently in beta, you can download the second beta and test out aggregatins by yourself. Your example might look like
curl -XPOST 'localhost:9200/_search?pretty' -d '
{
"aggregations": {
"main category": {
"terms": {
"field": "cat_1",
"order": {"_term": "asc"}
},
"aggregations": {
"sub category": {
"terms": {
"field": "cat_2",
"order": {"_term": "asc"}
}
}
}
}
}
}'
The idea is, to have a different field for each level of facetting and bucket your facets based on the terms of the first level (cat_1). These aggregations then would have sub-buckets, based on the terms of the second level (cat_2). The result may look like
{
"aggregations" : {
"main category" : {
"buckets" : [ {
"key" : "category 1",
"doc_count" : 10,
"sub category" : {
"buckets" : [ {
"key" : "subcategory 1",
"doc_count" : 4
}, {
"key" : "subcategory 2",
"doc_count" : 6
} ]
}
}, {
"key" : "category 2",
"doc_count" : 7,
"sub category" : {
"buckets" : [ {
"key" : "subcategory 1",
"doc_count" : 3
}, {
"key" : "subcategory 2",
"doc_count" : 4
} ]
}
} ]
}
}
}