Introduce histogram support (prometheus-community#435)

* Introduce histogram support

Prior to this change, the custom queries were restricted to counters and
gauges.

This change introduces a new ColumnUsage, namely HISTOGRAM, that expects
the column to contain an array of upper inclusive bounds for each
observation bucket in the emitted metric.  It also expects three more
columns to be present with the suffixes:
- `_bucket`, containing an array of cumulative counters for the
  observation buckets;
- `_sum`, the total sum of all observed values; and
- `_count`, the count of events that have been observed.

A flag has been added to the MetricMap struct to easily identify metrics
that should emit a histogram and the construction of a histogram metric
is aided by the pg.Array function and a new helper dbToUint64 function.

Finally, and example of usage is given in queries.yaml.

fixes prometheus-community#402

Signed-off-by: Corin Lawson <[email protected]>

* Introduces tests for histogram support

Prior to this change, the histogram support was untested.

This change introduces a new integration test that reads a user query
containing a number of histogram metrics.  Also, additional checks have
been added to TestBooleanConversionToValueAndString to test dbToUint64.

Signed-off-by: Corin Lawson <[email protected]>

Author: au-phiware

cmd/postgres_exporter/postgres_exporter.go

@@ -80,6 +80,7 @@ const (
 	GAUGE        ColumnUsage = iota // Use this column as a gauge
 	MAPPEDMETRIC ColumnUsage = iota // Use this column with the supplied mapping of text values
 	DURATION     ColumnUsage = iota // This column should be interpreted as a text duration (and converted to milliseconds)
+	HISTOGRAM    ColumnUsage = iota // Use this column as a histogram
 )
 
 // UnmarshalYAML implements the yaml.Unmarshaller interface.
@@ -169,6 +170,7 @@ type MetricMapNamespace struct {
 // be mapped to by the collector
 type MetricMap struct {
 	discard    bool                              // Should metric be discarded during mapping?
+	histogram  bool                              // Should metric be treated as a histogram?
 	vtype      prometheus.ValueType              // Prometheus valuetype
 	desc       *prometheus.Desc                  // Prometheus descriptor
 	conversion func(interface{}) (float64, bool) // Conversion function to turn PG result into float64
@@ -650,6 +652,27 @@ func makeDescMap(pgVersion semver.Version, serverLabels prometheus.Labels, metri
 						return dbToFloat64(in)
 					},
 				}
+			case HISTOGRAM:
+				thisMap[columnName] = MetricMap{
+					histogram: true,
+					vtype:     prometheus.UntypedValue,
+					desc:      prometheus.NewDesc(fmt.Sprintf("%s_%s", namespace, columnName), columnMapping.description, variableLabels, serverLabels),
+					conversion: func(in interface{}) (float64, bool) {
+						return dbToFloat64(in)
+					},
+				}
+				thisMap[columnName+"_bucket"] = MetricMap{
+					histogram: true,
+					discard:   true,
+				}
+				thisMap[columnName+"_sum"] = MetricMap{
+					histogram: true,
+					discard:   true,
+				}
+				thisMap[columnName+"_count"] = MetricMap{
+					histogram: true,
+					discard:   true,
+				}
 			case MAPPEDMETRIC:
 				thisMap[columnName] = MetricMap{
 					vtype: prometheus.GaugeValue,
@@ -721,6 +744,9 @@ func stringToColumnUsage(s string) (ColumnUsage, error) {
 	case "GAUGE":
 		u = GAUGE
 
+	case "HISTOGRAM":
+		u = HISTOGRAM
+
 	case "MAPPEDMETRIC":
 		u = MAPPEDMETRIC
 
@@ -772,6 +798,46 @@ func dbToFloat64(t interface{}) (float64, bool) {
 	}
 }
 
+// Convert database.sql types to uint64 for Prometheus consumption. Null types are mapped to 0. string and []byte
+// types are mapped as 0 and !ok
+func dbToUint64(t interface{}) (uint64, bool) {
+	switch v := t.(type) {
+	case uint64:
+		return v, true
+	case int64:
+		return uint64(v), true
+	case float64:
+		return uint64(v), true
+	case time.Time:
+		return uint64(v.Unix()), true
+	case []byte:
+		// Try and convert to string and then parse to a uint64
+		strV := string(v)
+		result, err := strconv.ParseUint(strV, 10, 64)
+		if err != nil {
+			log.Infoln("Could not parse []byte:", err)
+			return 0, false
+		}
+		return result, true
+	case string:
+		result, err := strconv.ParseUint(v, 10, 64)
+		if err != nil {
+			log.Infoln("Could not parse string:", err)
+			return 0, false
+		}
+		return result, true
+	case bool:
+		if v {
+			return 1, true
+		}
+		return 0, true
+	case nil:
+		return 0, true
+	default:
+		return 0, false
+	}
+}
+
 // Convert database.sql to string for Prometheus labels. Null types are mapped to empty strings.
 func dbToString(t interface{}) (string, bool) {
 	switch v := t.(type) {
@@ -1304,13 +1370,68 @@ func queryNamespaceMapping(server *Server, namespace string, mapping MetricMapNa
 					continue
 				}
 
-				value, ok := dbToFloat64(columnData[idx])
-				if !ok {
-					nonfatalErrors = append(nonfatalErrors, errors.New(fmt.Sprintln("Unexpected error parsing column: ", namespace, columnName, columnData[idx])))
-					continue
+				if metricMapping.histogram {
+					var keys []float64
+					err = pq.Array(&keys).Scan(columnData[idx])
+					if err != nil {
+						return []prometheus.Metric{}, []error{}, errors.New(fmt.Sprintln("Error retrieving", columnName, "buckets:", namespace, err))
+					}
+
+					var values []int64
+					valuesIdx, ok := columnIdx[columnName+"_bucket"]
+					if !ok {
+						nonfatalErrors = append(nonfatalErrors, errors.New(fmt.Sprintln("Missing column: ", namespace, columnName+"_bucket")))
+						continue
+					}
+					err = pq.Array(&values).Scan(columnData[valuesIdx])
+					if err != nil {
+						return []prometheus.Metric{}, []error{}, errors.New(fmt.Sprintln("Error retrieving", columnName, "bucket values:", namespace, err))
+					}
+
+					buckets := make(map[float64]uint64, len(keys))
+					for i, key := range keys {
+						if i >= len(values) {
+							break
+						}
+						buckets[key] = uint64(values[i])
+					}
+
+					idx, ok = columnIdx[columnName+"_sum"]
+					if !ok {
+						nonfatalErrors = append(nonfatalErrors, errors.New(fmt.Sprintln("Missing column: ", namespace, columnName+"_sum")))
+						continue
+					}
+					sum, ok := dbToFloat64(columnData[idx])
+					if !ok {
+						nonfatalErrors = append(nonfatalErrors, errors.New(fmt.Sprintln("Unexpected error parsing column: ", namespace, columnName+"_sum", columnData[idx])))
+						continue
+					}
+
+					idx, ok = columnIdx[columnName+"_count"]
+					if !ok {
+						nonfatalErrors = append(nonfatalErrors, errors.New(fmt.Sprintln("Missing column: ", namespace, columnName+"_count")))
+						continue
+					}
+					count, ok := dbToUint64(columnData[idx])
+					if !ok {
+						nonfatalErrors = append(nonfatalErrors, errors.New(fmt.Sprintln("Unexpected error parsing column: ", namespace, columnName+"_count", columnData[idx])))
+						continue
+					}
+
+					metric = prometheus.MustNewConstHistogram(
+						metricMapping.desc,
+						count, sum, buckets,
+						labels...,
+					)
+				} else {
+					value, ok := dbToFloat64(columnData[idx])
+					if !ok {
+						nonfatalErrors = append(nonfatalErrors, errors.New(fmt.Sprintln("Unexpected error parsing column: ", namespace, columnName, columnData[idx])))
+						continue
+					}
+					// Generate the metric
+					metric = prometheus.MustNewConstMetric(metricMapping.desc, metricMapping.vtype, value, labels...)
 				}
-				// Generate the metric
-				metric = prometheus.MustNewConstMetric(metricMapping.desc, metricMapping.vtype, value, labels...)
 			} else {
 				// Unknown metric. Report as untyped if scan to float64 works, else note an error too.
 				metricLabel := fmt.Sprintf("%s_%s", namespace, columnName)

cmd/postgres_exporter/postgres_exporter_integration_test.go

@@ -126,3 +126,26 @@ func (s *IntegrationSuite) TestUnknownMetricParsingDoesntCrash(c *C) {
 	// scrape the exporter and make sure it works
 	exporter.scrape(ch)
 }
+
+// TestExtendQueriesDoesntCrash tests that specifying extend.query-path doesn't
+// crash.
+func (s *IntegrationSuite) TestExtendQueriesDoesntCrash(c *C) {
+	// Setup a dummy channel to consume metrics
+	ch := make(chan prometheus.Metric, 100)
+	go func() {
+		for range ch {
+		}
+	}()
+
+	dsn := os.Getenv("DATA_SOURCE_NAME")
+	c.Assert(dsn, Not(Equals), "")
+
+	exporter := NewExporter(
+		strings.Split(dsn, ","),
+		WithUserQueriesPath("../user_queries_test.yaml"),
+	)
+	c.Assert(exporter, NotNil)
+
+	// scrape the exporter and make sure it works
+	exporter.scrape(ch)
+}

cmd/postgres_exporter/postgres_exporter_test.go

@@ -4,9 +4,11 @@ package main
 
 import (
 	"io/ioutil"
+	"math"
 	"os"
 	"reflect"
 	"testing"
+	"time"
 
 	"github.com/blang/semver"
 	"github.com/prometheus/client_golang/prometheus"
@@ -287,13 +289,30 @@ func UnsetEnvironment(c *C, d string) {
 	c.Assert(err, IsNil)
 }
 
+type isNaNChecker struct {
+	*CheckerInfo
+}
+
+var IsNaN Checker = &isNaNChecker{
+	&CheckerInfo{Name: "IsNaN", Params: []string{"value"}},
+}
+
+func (checker *isNaNChecker) Check(params []interface{}, names []string) (result bool, error string) {
+	param, ok := (params[0]).(float64)
+	if !ok {
+		return false, "obtained value type is not a float"
+	}
+	return math.IsNaN(param), ""
+}
+
 // test boolean metric type gets converted to float
 func (s *FunctionalSuite) TestBooleanConversionToValueAndString(c *C) {
 
 	type TestCase struct {
 		input          interface{}
 		expectedString string
 		expectedValue  float64
+		expectedCount  uint64
 		expectedOK     bool
 	}
 
@@ -302,19 +321,71 @@ func (s *FunctionalSuite) TestBooleanConversionToValueAndString(c *C) {
 			input:          true,
 			expectedString: "true",
 			expectedValue:  1.0,
+			expectedCount:  1,
 			expectedOK:     true,
 		},
 		{
 			input:          false,
 			expectedString: "false",
 			expectedValue:  0.0,
+			expectedCount:  0,
+			expectedOK:     true,
+		},
+		{
+			input:          nil,
+			expectedString: "",
+			expectedValue:  math.NaN(),
+			expectedCount:  0,
+			expectedOK:     true,
+		},
+		{
+			input:          TestCase{},
+			expectedString: "",
+			expectedValue:  math.NaN(),
+			expectedCount:  0,
+			expectedOK:     false,
+		},
+		{
+			input:          123.0,
+			expectedString: "123",
+			expectedValue:  123.0,
+			expectedCount:  123,
+			expectedOK:     true,
+		},
+		{
+			input:          "123",
+			expectedString: "123",
+			expectedValue:  123.0,
+			expectedCount:  123,
+			expectedOK:     true,
+		},
+		{
+			input:          []byte("123"),
+			expectedString: "123",
+			expectedValue:  123.0,
+			expectedCount:  123,
+			expectedOK:     true,
+		},
+		{
+			input:          time.Unix(1600000000, 0),
+			expectedString: "1600000000",
+			expectedValue:  1600000000.0,
+			expectedCount:  1600000000,
 			expectedOK:     true,
 		},
 	}
 
 	for _, cs := range cases {
 		value, ok := dbToFloat64(cs.input)
-		c.Assert(value, Equals, cs.expectedValue)
+		if math.IsNaN(cs.expectedValue) {
+			c.Assert(value, IsNaN)
+		} else {
+			c.Assert(value, Equals, cs.expectedValue)
+		}
+		c.Assert(ok, Equals, cs.expectedOK)
+
+		count, ok := dbToUint64(cs.input)
+		c.Assert(count, Equals, cs.expectedCount)
 		c.Assert(ok, Equals, cs.expectedOK)
 
 		str, ok := dbToString(cs.input)

cmd/postgres_exporter/tests/user_queries_test.yaml

@@ -0,0 +1,51 @@
+random:
+  query: |
+    WITH data AS (SELECT floor(random()*10) AS d FROM generate_series(1,100)),
+         metrics AS (SELECT SUM(d) AS sum, COUNT(*) AS count FROM data),
+         buckets AS (SELECT le, SUM(CASE WHEN d <= le THEN 1 ELSE 0 END) AS d
+                     FROM data, UNNEST(ARRAY[1, 2, 4, 8]) AS le GROUP BY le)
+    SELECT
+      sum AS histogram_sum,
+      count AS histogram_count,
+      ARRAY_AGG(le) AS histogram,
+      ARRAY_AGG(d) AS histogram_bucket,
+      ARRAY_AGG(le) AS missing,
+      ARRAY_AGG(le) AS missing_sum,
+      ARRAY_AGG(d) AS missing_sum_bucket,
+      ARRAY_AGG(le) AS missing_count,
+      ARRAY_AGG(d) AS missing_count_bucket,
+      sum AS missing_count_sum,
+      ARRAY_AGG(le) AS unexpected_sum,
+      ARRAY_AGG(d) AS unexpected_sum_bucket,
+      'data' AS unexpected_sum_sum,
+      ARRAY_AGG(le) AS unexpected_count,
+      ARRAY_AGG(d) AS unexpected_count_bucket,
+      sum AS unexpected_count_sum,
+      'nan'::varchar AS unexpected_count_count,
+      ARRAY_AGG(le) AS unexpected_bytes,
+      ARRAY_AGG(d) AS unexpected_bytes_bucket,
+      sum AS unexpected_bytes_sum,
+      'nan'::bytea AS unexpected_bytes_count
+    FROM metrics, buckets GROUP BY 1,2
+  metrics:
+    - histogram:
+        usage: "HISTOGRAM"
+        description: "Random data"
+    - missing:
+        usage: "HISTOGRAM"
+        description: "nonfatal error"
+    - missing_sum:
+        usage: "HISTOGRAM"
+        description: "nonfatal error"
+    - missing_count:
+        usage: "HISTOGRAM"
+        description: "nonfatal error"
+    - unexpected_sum:
+        usage: "HISTOGRAM"
+        description: "nonfatal error"
+    - unexpected_count:
+        usage: "HISTOGRAM"
+        description: "nonfatal error"
+    - unexpected_bytes:
+        usage: "HISTOGRAM"
+        description: "nonfatal error"