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response_parser_test.go
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package quickwit
import (
"encoding/json"
"flag"
"fmt"
"testing"
"time"
"github.com/grafana/grafana-plugin-sdk-go/backend"
"github.com/grafana/grafana-plugin-sdk-go/data"
"github.com/grafana/grafana-plugin-sdk-go/experimental"
"github.com/stretchr/testify/assert"
"github.com/stretchr/testify/require"
es "github.com/quickwit-oss/quickwit-datasource/pkg/quickwit/client"
)
var update = flag.Bool("update", true, "update golden files")
func TestProcessLogsResponse(t *testing.T) {
t.Run("Simple log query response", func(t *testing.T) {
query := []byte(`
[
{
"refId": "A",
"metrics": [{ "type": "logs"}],
"bucketAggs": [
{
"type": "date_histogram",
"settings": { "interval": "auto" },
"id": "2"
}
],
"key": "Q-1561369883389-0.7611823271062786-0",
"query": "hello AND message"
}
]
`)
response := []byte(`
{
"responses": [
{
"aggregations": {},
"hits": {
"hits": [
{
"_id": "fdsfs",
"_type": "_doc",
"_index": "mock-index",
"_source": {
"testtime": "2019-06-24T09:51:19.765Z",
"host": "djisaodjsoad",
"number": 1,
"line": "hello, i am a message",
"level": "debug",
"fields": { "lvl": "debug" }
},
"highlight": {
"message": [
"@HIGHLIGHT@hello@/HIGHLIGHT@, i am a @HIGHLIGHT@message@/HIGHLIGHT@"
]
}
},
{
"_id": "kdospaidopa",
"_type": "_doc",
"_index": "mock-index",
"_source": {
"testtime": "2019-06-24T09:52:19.765Z",
"host": "dsalkdakdop",
"number": 2,
"line": "hello, i am also message",
"level": "error",
"fields": { "lvl": "info" }
},
"highlight": {
"message": [
"@HIGHLIGHT@hello@/HIGHLIGHT@, i am a @HIGHLIGHT@message@/HIGHLIGHT@"
]
}
}
]
}
}
]
}
`)
t.Run("creates correct data frame fields", func(t *testing.T) {
result, err := queryDataTest(query, response)
require.NoError(t, err)
require.Len(t, result.response.Responses, 1)
frames := result.response.Responses["A"].Frames
require.Len(t, frames, 1)
logsFrame := frames[0]
meta := logsFrame.Meta
require.Equal(t, data.VisTypeLogs, string(meta.PreferredVisualization))
logsFieldMap := make(map[string]*data.Field)
for _, field := range logsFrame.Fields {
logsFieldMap[field.Name] = field
}
require.Contains(t, logsFieldMap, "testtime")
require.Equal(t, data.FieldTypeNullableTime, logsFieldMap["testtime"].Type())
require.Contains(t, logsFieldMap, "host")
require.Equal(t, data.FieldTypeNullableString, logsFieldMap["host"].Type())
require.Contains(t, logsFieldMap, "line")
require.Equal(t, data.FieldTypeNullableString, logsFieldMap["line"].Type())
require.Contains(t, logsFieldMap, "number")
require.Equal(t, data.FieldTypeNullableFloat64, logsFieldMap["number"].Type())
require.Contains(t, logsFieldMap, "_source")
require.Equal(t, data.FieldTypeNullableJSON, logsFieldMap["_source"].Type())
actualJson1, err := json.Marshal(logsFieldMap["_source"].At(0).(*json.RawMessage))
require.NoError(t, err)
actualJson2, err := json.Marshal(logsFieldMap["_source"].At(1).(*json.RawMessage))
require.NoError(t, err)
expectedJson1 := `
{
"fields.lvl": "debug",
"host": "djisaodjsoad",
"level": "debug",
"line": "hello, i am a message",
"number": 1,
"testtime": "2019-06-24T09:51:19.765Z",
"line": "hello, i am a message"
}
`
expectedJson2 := `
{
"testtime": "2019-06-24T09:52:19.765Z",
"host": "dsalkdakdop",
"number": 2,
"line": "hello, i am also message",
"level": "error",
"fields.lvl": "info"
}`
require.JSONEq(t, expectedJson1, string(actualJson1))
require.JSONEq(t, expectedJson2, string(actualJson2))
})
t.Run("creates correct level field", func(t *testing.T) {
result, err := queryDataTest(query, response)
require.NoError(t, err)
require.Len(t, result.response.Responses, 1)
frames := result.response.Responses["A"].Frames
require.True(t, len(frames) > 0)
requireFrameLength(t, frames[0], 2)
fieldMap := make(map[string]*data.Field)
for _, field := range frames[0].Fields {
fieldMap[field.Name] = field
}
require.Contains(t, fieldMap, "level")
field := fieldMap["level"]
requireStringAt(t, "debug", field, 0)
requireStringAt(t, "error", field, 1)
})
})
t.Run("Empty response", func(t *testing.T) {
query := []byte(`
[
{
"refId": "A",
"metrics": [{ "type": "logs", "id": "2" }],
"bucketAggs": [],
"key": "Q-1561369883389-0.7611823271062786-0",
"query": "hello AND message"
}
]
`)
response := []byte(`
{
"responses": [
{
"hits": { "hits": [] },
"aggregations": {},
"status": 200
}
]
}
`)
result, err := queryDataTest(query, response)
require.NoError(t, err)
require.Len(t, result.response.Responses, 1)
frames := result.response.Responses["A"].Frames
require.Len(t, frames, 1)
})
t.Run("Log query with nested fields", func(t *testing.T) {
targets := map[string]string{
"A": `{
"metrics": [{ "type": "logs" }]
}`,
}
response := `{
"responses":[
{
"hits":{
"total":{
"value":109,
"relation":"eq"
},
"max_score":null,
"hits":[
{
"_index":"logs-2023.02.08",
"_id":"GB2UMYYBfCQ-FCMjayJa",
"_score":null,
"_source":{
"@timestamp":"2023-02-08T15:10:55.830Z",
"line":"log text [479231733]",
"counter":"109",
"float":58.253758485091,
"label":"val1",
"lvl":"info",
"location":"17.089705232090438, 41.62861966340297",
"nested": {
"field": {
"double_nested": "value"
}
},
"shapes":[
{
"type":"triangle"
},
{
"type":"square"
}
],
"xyz": null
},
"sort":[
1675869055830,
4
]
},
{
"_index":"logs-2023.02.08",
"_id":"Fx2UMYYBfCQ-FCMjZyJ_",
"_score":null,
"_source":{
"@timestamp":"2023-02-08T15:10:54.835Z",
"line":"log text with ANSI \u001b[31mpart of the text\u001b[0m [493139080]",
"counter":"108",
"float":54.5977098233944,
"label":"val1",
"lvl":"info",
"location":"19.766305918490463, 40.42639175509792",
"nested": {
"field": {
"double_nested": "value"
}
},
"shapes":[
{
"type":"triangle"
},
{
"type":"square"
}
],
"xyz": "def"
},
"sort":[
1675869054835,
7
]
}
]
},
"status":200
}
]
}`
result, err := parseTestResponse(targets, response)
require.NoError(t, err)
require.Len(t, result.Responses, 1)
queryRes := result.Responses["A"]
require.NotNil(t, queryRes)
dataframes := queryRes.Frames
require.Len(t, dataframes, 1)
frame := dataframes[0]
require.Equal(t, 12, len(frame.Fields))
// Fields have the correct length
require.Equal(t, 2, frame.Fields[0].Len())
// First field is timeField
require.Equal(t, data.FieldTypeNullableTime, frame.Fields[0].Type())
// Second is log line
require.Equal(t, data.FieldTypeNullableString, frame.Fields[1].Type())
require.Equal(t, "line", frame.Fields[1].Name)
// Correctly renames lvl field to level
require.Equal(t, "level", frame.Fields[6].Name)
// Correctly uses string types
require.Equal(t, data.FieldTypeNullableString, frame.Fields[1].Type())
// Correctly detects float64 types
require.Equal(t, data.FieldTypeNullableFloat64, frame.Fields[4].Type())
// Correctly detects json types
require.Equal(t, data.FieldTypeNullableJSON, frame.Fields[2].Type())
// Correctly flattens fields
require.Equal(t, "nested.field.double_nested", frame.Fields[8].Name)
require.Equal(t, data.FieldTypeNullableString, frame.Fields[8].Type())
// Correctly detects type even if first value is null
require.Equal(t, data.FieldTypeNullableJSON, frame.Fields[10].Type())
})
}
func TestProcessRawDataResponse(t *testing.T) {
t.Run("Simple raw data query", func(t *testing.T) {
targets := map[string]string{
"A": `{
"metrics": [{ "type": "raw_data" }]
}`,
}
response := `{
"responses":[
{
"hits":{
"total":{
"value":109,
"relation":"eq"
},
"max_score":null,
"hits":[
{
"_index":"logs-2023.02.08",
"_id":"GB2UMYYBfCQ-FCMjayJa",
"_score":null,
"_source":{
"@timestamp":"2023-02-08T15:10:55.830Z",
"line":"log text [479231733]",
"counter":"109",
"float":58.253758485091,
"label":"val1",
"level":"info",
"location":"17.089705232090438, 41.62861966340297",
"nested": {
"field": {
"double_nested": "value"
}
},
"shapes":[
{
"type":"triangle"
},
{
"type":"square"
}
],
"xyz": null
},
"sort":[
1675869055830,
4
]
},
{
"_index":"logs-2023.02.08",
"_id":"Fx2UMYYBfCQ-FCMjZyJ_",
"_score":null,
"_source":{
"@timestamp":"2023-02-08T15:10:54.835Z",
"line":"log text with ANSI \u001b[31mpart of the text\u001b[0m [493139080]",
"counter":"108",
"float":54.5977098233944,
"label":"val1",
"level":"info",
"location":"19.766305918490463, 40.42639175509792",
"nested": {
"field": {
"double_nested": "value"
}
},
"shapes":[
{
"type":"triangle"
},
{
"type":"square"
}
],
"xyz": "def"
},
"sort":[
1675869054835,
7
]
}
]
},
"status":200
}
]
}`
result, err := parseTestResponse(targets, response)
require.NoError(t, err)
require.Len(t, result.Responses, 1)
queryRes := result.Responses["A"]
require.NotNil(t, queryRes)
dataframes := queryRes.Frames
require.Len(t, dataframes, 1)
frame := dataframes[0]
require.Equal(t, 10, len(frame.Fields))
// Fields have the correct length
require.Equal(t, 2, frame.Fields[0].Len())
// First field is timeField
require.Equal(t, data.FieldTypeNullableTime, frame.Fields[0].Type())
// Correctly uses string types
require.Equal(t, data.FieldTypeNullableString, frame.Fields[1].Type())
// Correctly detects float64 types
require.Equal(t, data.FieldTypeNullableFloat64, frame.Fields[2].Type())
// Correctly flattens fields
require.Equal(t, "nested.field.double_nested", frame.Fields[7].Name)
require.Equal(t, data.FieldTypeNullableString, frame.Fields[7].Type())
// Correctly detects type even if first value is null
require.Equal(t, data.FieldTypeNullableString, frame.Fields[9].Type())
})
t.Run("Raw data query filterable fields", func(t *testing.T) {
query := []byte(`
[
{
"refId": "A",
"metrics": [{ "type": "raw_data", "id": "1" }],
"bucketAggs": []
}
]
`)
response := []byte(`
{
"responses": [
{
"hits": {
"total": { "relation": "eq", "value": 1 },
"hits": [
{
"_id": "1",
"_type": "_doc",
"_index": "index",
"_source": { "sourceProp": "asd" }
}
]
}
}
]
}
`)
result, err := queryDataTest(query, response)
require.NoError(t, err)
require.Len(t, result.response.Responses, 1)
frames := result.response.Responses["A"].Frames
require.True(t, len(frames) > 0)
for _, field := range frames[0].Fields {
trueValue := true
filterableConfig := data.FieldConfig{Filterable: &trueValue}
// we need to test that the only changed setting is `filterable`
require.Equal(t, filterableConfig, *field.Config)
}
})
}
func TestProcessRawDocumentResponse(t *testing.T) {
t.Run("Simple raw document query", func(t *testing.T) {
query := []byte(`
[
{
"refId": "A",
"metrics": [{ "type": "raw_document", "id": "1" }],
"bucketAggs": []
}
]
`)
response := []byte(`
{
"responses": [
{
"hits": {
"total": 100,
"hits": [
{
"_id": "1",
"_type": "type",
"_index": "index",
"_source": { "sourceProp": "asd" },
"fields": { "fieldProp": "field" }
},
{
"_source": { "sourceProp": "asd2" },
"fields": { "fieldProp": "field2" }
}
]
}
}
]
}
`)
result, err := queryDataTest(query, response)
require.NoError(t, err)
require.Len(t, result.response.Responses, 1)
frames := result.response.Responses["A"].Frames
require.Len(t, frames, 1)
fields := frames[0].Fields
require.Len(t, fields, 1)
f := fields[0]
require.Equal(t, data.FieldTypeNullableJSON, f.Type())
require.Equal(t, 2, f.Len())
v := f.At(0).(*json.RawMessage)
var jsonData map[string]interface{}
err = json.Unmarshal(*v, &jsonData)
require.NoError(t, err)
require.Equal(t, "asd", jsonData["sourceProp"])
require.Equal(t, "field", jsonData["fieldProp"])
})
t.Run("More complex raw document query", func(t *testing.T) {
targets := map[string]string{
"A": `{
"metrics": [{ "type": "raw_document" }]
}`,
}
response := `{
"responses":[
{
"hits":{
"total":{
"value":109,
"relation":"eq"
},
"max_score":null,
"hits":[
{
"_index":"logs-2023.02.08",
"_id":"GB2UMYYBfCQ-FCMjayJa",
"_score":null,
"fields": {
"test_field":"A"
},
"_source":{
"@timestamp":"2023-02-08T15:10:55.830Z",
"line":"log text [479231733]",
"counter":"109",
"float":58.253758485091,
"label":"val1",
"level":"info",
"location":"17.089705232090438, 41.62861966340297",
"nested": {
"field": {
"double_nested": "value"
}
}
}
},
{
"_index":"logs-2023.02.08",
"_id":"Fx2UMYYBfCQ-FCMjZyJ_",
"_score":null,
"fields": {
"test_field":"A"
},
"_source":{
"@timestamp":"2023-02-08T15:10:54.835Z",
"line":"log text with ANSI \u001b[31mpart of the text\u001b[0m [493139080]",
"counter":"108",
"float":54.5977098233944,
"label":"val1",
"level":"info",
"location":"19.766305918490463, 40.42639175509792",
"nested": {
"field": {
"double_nested": "value1"
}
}
}
}
]
},
"status":200
}
]
}`
result, err := parseTestResponse(targets, response)
require.NoError(t, err)
require.Len(t, result.Responses, 1)
queryRes := result.Responses["A"]
require.NotNil(t, queryRes)
dataframes := queryRes.Frames
require.Len(t, dataframes, 1)
frame := dataframes[0]
require.Equal(t, 1, len(frame.Fields))
//Fields have the correct length
require.Equal(t, 2, frame.Fields[0].Len())
// The only field is the raw document
require.Equal(t, data.FieldTypeNullableJSON, frame.Fields[0].Type())
require.Equal(t, "A", frame.Fields[0].Name)
})
}
func TestProcessBuckets(t *testing.T) {
t.Run("Percentiles", func(t *testing.T) {
t.Run("Percentiles without date histogram", func(t *testing.T) {
query := []byte(`
[
{
"refId": "A",
"metrics": [
{
"type": "percentiles",
"field": "value",
"settings": { "percents": ["75", "90"] },
"id": "1"
}
],
"bucketAggs": [{ "type": "terms", "field": "id", "id": "3" }]
}
]
`)
response := []byte(`
{
"responses": [
{
"aggregations": {
"3": {
"buckets": [
{
"1": { "values": { "90": 5.5, "75": 3.3 } },
"doc_count": 10,
"key": "id1"
},
{
"1": { "values": { "75": 2.3, "90": 4.5 } },
"doc_count": 15,
"key": "id2"
}
]
}
}
}
]
}
`)
result, err := queryDataTest(query, response)
require.NoError(t, err)
require.Len(t, result.response.Responses, 1)
frames := result.response.Responses["A"].Frames
require.Len(t, frames, 1)
requireFrameLength(t, frames[0], 2)
require.Len(t, frames[0].Fields, 3)
f1 := frames[0].Fields[0]
f2 := frames[0].Fields[1]
f3 := frames[0].Fields[2]
require.Equal(t, "id", f1.Name)
require.Equal(t, "p75 value", f2.Name)
require.Equal(t, "p90 value", f3.Name)
requireStringAt(t, "id1", f1, 0)
requireStringAt(t, "id2", f1, 1)
requireFloatAt(t, 3.3, f2, 0)
requireFloatAt(t, 2.3, f2, 1)
requireFloatAt(t, 5.5, f3, 0)
requireFloatAt(t, 4.5, f3, 1)
})
t.Run("percentiles 2 frames", func(t *testing.T) {
query := []byte(`
[
{
"refId": "A",
"metrics": [
{
"type": "percentiles",
"settings": { "percents": ["75", "90"] },
"id": "1",
"field": "@value"
}
],
"bucketAggs": [
{ "type": "date_histogram", "field": "@timestamp", "id": "3" }
]
}
]
`)
response := []byte(`
{
"responses": [
{
"aggregations": {
"3": {
"buckets": [
{
"1": { "values": { "75": 3.3, "90": 5.5 } },
"doc_count": 10,
"key": 1000
},
{
"1": { "values": { "75": 2.3, "90": 4.5 } },
"doc_count": 15,
"key": 2000
}
]
}
}
}
]
}
`)
result, err := queryDataTest(query, response)
require.NoError(t, err)
require.Len(t, result.response.Responses, 1)
frames := result.response.Responses["A"].Frames
require.Len(t, frames, 2)
requireFrameLength(t, frames[0], 2)
requireTimeSeriesName(t, "p75 @value", frames[0])
requireTimeSeriesName(t, "p90 @value", frames[1])
requireNumberValue(t, 3.3, frames[0], 0)
requireTimeValue(t, 1000, frames[0], 0)
requireNumberValue(t, 4.5, frames[1], 1)
})
t.Run("With percentiles", func(t *testing.T) {
targets := map[string]string{
"A": `{
"metrics": [{ "type": "percentiles", "settings": { "percents": [75, 90] }, "id": "1" }],
"bucketAggs": [{ "type": "date_histogram", "field": "@timestamp", "id": "3" }]
}`,
}
response := `{
"responses": [
{
"aggregations": {
"3": {
"buckets": [
{
"1": { "values": { "75": 3.3, "90": 5.5 } },
"doc_count": 10,
"key": 1000
},
{
"1": { "values": { "75": 2.3, "90": 4.5 } },
"doc_count": 15,
"key": 2000
}
]
}
}
}
]
}`
result, err := parseTestResponse(targets, response)
require.NoError(t, err)
require.Len(t, result.Responses, 1)
queryRes := result.Responses["A"]
require.NotNil(t, queryRes)
dataframes := queryRes.Frames
require.NoError(t, err)
require.Len(t, dataframes, 2)
frame := dataframes[0]
require.Len(t, frame.Fields, 2)
require.Equal(t, frame.Fields[0].Name, data.TimeSeriesTimeFieldName)
require.Equal(t, frame.Fields[0].Len(), 2)
require.Equal(t, frame.Fields[1].Name, data.TimeSeriesValueFieldName)
require.Equal(t, frame.Fields[1].Len(), 2)
assert.Equal(t, frame.Fields[1].Config.DisplayNameFromDS, "p75")
frame = dataframes[1]
require.Len(t, frame.Fields, 2)
require.Equal(t, frame.Fields[0].Name, data.TimeSeriesTimeFieldName)
require.Equal(t, frame.Fields[0].Len(), 2)
require.Equal(t, frame.Fields[1].Name, data.TimeSeriesValueFieldName)
require.Equal(t, frame.Fields[1].Len(), 2)
assert.Equal(t, frame.Fields[1].Config.DisplayNameFromDS, "p90")
})
})
t.Run("Histograms", func(t *testing.T) {
t.Run("Histogram simple", func(t *testing.T) {
query := []byte(`
[
{
"refId": "A",
"metrics": [{ "type": "count", "id": "1" }],
"bucketAggs": [{ "type": "histogram", "field": "bytes", "id": "3" }]
}
]
`)
response := []byte(`
{
"responses": [
{
"aggregations": {
"3": {
"buckets": [
{ "doc_count": 1, "key": 1000 },
{ "doc_count": 3, "key": 2000 },
{ "doc_count": 2, "key": 1000 }
]
}
}
}
]
}
`)
result, err := queryDataTest(query, response)
require.NoError(t, err)
require.Len(t, result.response.Responses, 1)
frames := result.response.Responses["A"].Frames
require.Len(t, frames, 1)
requireFrameLength(t, frames[0], 3)
fields := frames[0].Fields
require.Len(t, fields, 2)
field1 := fields[0]
field2 := fields[1]
require.Equal(t, "bytes", field1.Name)
trueValue := true
filterableConfig := data.FieldConfig{Filterable: &trueValue}
// we need to test that the only changed setting is `filterable`
require.Equal(t, filterableConfig, *field1.Config)
require.Equal(t, "Count", field2.Name)
// we need to test that the fieldConfig is "empty"
require.Nil(t, field2.Config)
})
t.Run("Histogram response", func(t *testing.T) {
targets := map[string]string{
"A": `{
"metrics": [{ "type": "count", "id": "1" }],
"bucketAggs": [{ "type": "histogram", "field": "bytes", "id": "3" }]
}`,
}
response := `{
"responses": [
{
"aggregations": {
"3": {
"buckets": [{ "doc_count": 1, "key": 1000 }, { "doc_count": 3, "key": 2000 }, { "doc_count": 2, "key": 3000 }]
}
}
}
]
}`
result, err := parseTestResponse(targets, response)
require.NoError(t, err)
require.Len(t, result.Responses, 1)
queryRes := result.Responses["A"]
require.NotNil(t, queryRes)
dataframes := queryRes.Frames
require.NoError(t, err)
require.Len(t, dataframes, 1)
})
})
t.Run("Terms", func(t *testing.T) {
t.Run("Terms with two bucket_script", func(t *testing.T) {
targets := map[string]string{
"A": `{
"metrics": [
{ "id": "1", "type": "sum", "field": "@value" },
{ "id": "3", "type": "max", "field": "@value" },
{
"id": "4",
"pipelineVariables": [{ "name": "var1", "pipelineAgg": "1" }, { "name": "var2", "pipelineAgg": "3" }],
"settings": { "script": "params.var1 * params.var2" },
"type": "bucket_script"
},
{
"id": "5",
"pipelineVariables": [{ "name": "var1", "pipelineAgg": "1" }, { "name": "var2", "pipelineAgg": "3" }],
"settings": { "script": "params.var1 * params.var2 * 2" },
"type": "bucket_script"
}
],
"bucketAggs": [{ "type": "terms", "field": "@timestamp", "id": "2" }]
}`,
}
response := `{
"responses": [
{
"aggregations": {
"2": {
"buckets": [
{
"1": { "value": 2 },
"3": { "value": 3 },
"4": { "value": 6 },
"5": { "value": 24 },
"doc_count": 60,
"key": 1000
},
{
"1": { "value": 3 },
"3": { "value": 4 },
"4": { "value": 12 },
"5": { "value": 48 },
"doc_count": 60,
"key": 2000
}
]
}
}
}
]
}`
result, err := parseTestResponse(targets, response)
require.NoError(t, err)
require.Len(t, result.Responses, 1)
queryRes := result.Responses["A"]
require.NotNil(t, queryRes)
dataframes := queryRes.Frames
require.NoError(t, err)
require.Len(t, dataframes, 1)
frame := dataframes[0]
require.Len(t, frame.Fields, 5)
require.Equal(t, frame.Fields[0].Name, "@timestamp")
require.Equal(t, frame.Fields[0].Len(), 2)
require.Equal(t, frame.Fields[1].Name, "Sum")
require.Equal(t, frame.Fields[1].Len(), 2)
require.Equal(t, frame.Fields[2].Name, "Max")
require.Equal(t, frame.Fields[2].Len(), 2)
require.Equal(t, frame.Fields[3].Name, "params.var1 * params.var2")
require.Equal(t, frame.Fields[3].Len(), 2)
require.Equal(t, frame.Fields[4].Name, "params.var1 * params.var2 * 2")
require.Equal(t, frame.Fields[4].Len(), 2)
require.Nil(t, frame.Fields[1].Config)
})
t.Run("With max and multiple terms agg", func(t *testing.T) {
targets := map[string]string{
"A": `{
"metrics": [
{
"type": "count",
"field": "counter",
"id": "1"
}