Ensuring Metric Values are Cast to Float when they are Strings (#237)

* Casting metric values to a float; Adding a test for it as well;

* Checking for str to convert to float; Adding custom exception;

* Removing unused init param;

* Renaming exception name;

* Adjusting exception check to include ValueError; Updating Exception name; Adding a couple tests;

* Adding a couple validation tests for MetricDataRange as well;

* Fixing some linting issues I missed;

Author: Shackelford-Arden

prometheus_api_client/exceptions.py

@@ -5,3 +5,9 @@ class PrometheusApiClientException(Exception):
     """API client exception, raises when response status code != 200."""
 
     pass
+
+
+class MetricValueConversionError(Exception):
+    """Raises when we find a metric that is a string where we fail to convert it to a float."""
+
+    pass

prometheus_api_client/metric.py

@@ -3,6 +3,8 @@
 import datetime
 import pandas
 
+from prometheus_api_client.exceptions import MetricValueConversionError
+
 try:
     import matplotlib.pyplot as plt
     from pandas.plotting import register_matplotlib_converters
@@ -69,7 +71,16 @@ def __init__(self, metric, oldest_data_datetime=None):
 
             # if it is a single value metric change key name
             if "value" in metric:
-                metric["values"] = [metric["value"]]
+                datestamp = metric["value"][0]
+                metric_value = metric["value"][1]
+                if isinstance(metric_value, str):
+                    try:
+                        metric_value = float(metric_value)
+                    except (TypeError, ValueError):
+                        raise MetricValueConversionError(
+                            "Converting string metric value to float failed."
+                        )
+                metric["values"] = [[datestamp, metric_value]]
 
             self.metric_values = pandas.DataFrame(metric["values"], columns=["ds", "y"]).apply(
                 pandas.to_numeric, errors="raise"

prometheus_api_client/metric_range_df.py

@@ -3,6 +3,8 @@
 from pandas._typing import Axes, Dtype
 from typing import Optional, Sequence
 
+from prometheus_api_client.exceptions import MetricValueConversionError
+
 
 class MetricRangeDataFrame(DataFrame):
     """Subclass to format and represent Prometheus query response as pandas.DataFrame.
@@ -71,7 +73,15 @@ def __init__(
                     "data must be a range vector. Expected range vector, got instant vector"
                 )
             for t in v["values"]:
-                row_data.append({**v["metric"], "timestamp": t[0], "value": t[1]})
+                metric_value = t[1]
+                if isinstance(metric_value, str):
+                    try:
+                        metric_value = float(metric_value)
+                    except (TypeError, ValueError):
+                        raise MetricValueConversionError(
+                            "Converting string metric value to float failed."
+                        )
+                row_data.append({**v["metric"], "timestamp": t[0], "value": metric_value})
 
         # init df normally now
         super(MetricRangeDataFrame, self).__init__(

prometheus_api_client/metric_snapshot_df.py

@@ -3,6 +3,8 @@
 from pandas._typing import Axes, Dtype
 from typing import Optional, Sequence
 
+from prometheus_api_client.exceptions import MetricValueConversionError
+
 
 class MetricSnapshotDataFrame(DataFrame):
     """Subclass to format and represent Prometheus query response as pandas.DataFrame.
@@ -91,4 +93,10 @@ def __init__(
     @staticmethod
     def _get_nth_ts_value_pair(i: dict, n: int):
         val = i["values"][n] if "values" in i else i["value"]
-        return {"timestamp": val[0], "value": val[1]}
+        value = val[1]
+        if isinstance(value, str):
+            try:
+                value = float(value)
+            except (TypeError, ValueError):
+                raise MetricValueConversionError("Converting string metric value to float failed.")
+        return {"timestamp": val[0], "value": value}

tests/test_metric.py

@@ -1,7 +1,11 @@
 """Unit tests for Metrics Class."""
 import unittest
 import datetime
+
+import pytest
+
 from prometheus_api_client import Metric
+from prometheus_api_client.exceptions import MetricValueConversionError
 from .test_with_metrics import TestWithMetrics
 
 
@@ -96,6 +100,27 @@ def test_oldest_data_datetime_with_timedelta(self):  # noqa D102
             expected_start_time, new_metric.start_time, "Incorrect Start time after addition"
         )
 
+    def test_init_valid_string_metric_value(self):
+        """Ensures metric values provided as strings are properly cast to a numeric value (in this case, a float)."""
+        test_metric = Metric(
+            metric={
+                "metric": {"__name__": "test_metric", "fake": "data",},
+                "value": [1627485628.789, "26.82068965517243"],
+            }
+        )
+
+        self.assertTrue(isinstance(test_metric, Metric))
+
+    def test_init_invalid_string_metric_value(self):
+        """Ensures metric values provided as strings are properly cast to a numeric value (in this case, a float)."""
+        with pytest.raises(MetricValueConversionError):
+            Metric(
+                metric={
+                    "metric": {"__name__": "test_metric", "fake": "data",},
+                    "value": [1627485628.789, "26.8206896551724326.82068965517243"],
+                }
+            )
+
 
 if __name__ == "__main__":
     unittest.main()

tests/test_metric_range_df.py

@@ -1,7 +1,10 @@
 """Unit Tests for MetricRangeDataFrame."""
 import unittest
 import pandas as pd
+import pytest
+
 from prometheus_api_client import MetricRangeDataFrame
+from prometheus_api_client.exceptions import MetricValueConversionError
 from .test_with_metrics import TestWithMetrics
 
 
@@ -90,6 +93,27 @@ def test_init_single_metric(self):
             "incorrect dataframe shape when initialized with single json list",
         )
 
+    def test_init_invalid_string_value(self):
+        """Ensures metric values provided as concatenated strings are caught with a meaningful exception."""
+        with pytest.raises(MetricValueConversionError):
+            MetricRangeDataFrame(
+                {
+                    "metric": {"__name__": "test_metric", "fake": "data",},
+                    "values": [[1627485628.789, "26.8206896551724326.82068965517243"]],
+                }
+            )
+
+    def test_init_valid_string_value(self):
+        """Ensures metric values provided as a string but are valid floats are processed properly."""
+        results = MetricRangeDataFrame(
+            {
+                "metric": {"__name__": "test_metric", "fake": "data",},
+                "values": [[1627485628.789, "26.82068965517243"]],
+            }
+        )
+
+        self.assertEqual((1, 3), results.shape)
+
 
 if __name__ == "__main__":
     unittest.main()

tests/test_metric_snapshot_df.py

@@ -2,7 +2,11 @@
 import unittest
 import json
 import os
+
+import pytest
+
 from prometheus_api_client import MetricSnapshotDataFrame
+from prometheus_api_client.exceptions import MetricValueConversionError
 from pandas.api.types import is_datetime64_any_dtype as is_dtype_datetime
 
 
@@ -90,6 +94,29 @@ def test_init_single_metric(self):
             "incorrect dataframe shape when initialized with single json list",
         )
 
+    def test_init_multiple_metrics(self):
+        """Ensures metric values provided as strings are properly cast to a numeric value (in this case, a float)."""
+        raw_data = [
+            {"metric": {"fake": "data",}, "value": [1627485628.789, "26.82068965517243"],},
+            {"metric": {"fake": "data",}, "value": [1627485628.789, "26.82068965517243"],},
+        ]
+
+        test_df = MetricSnapshotDataFrame(data=raw_data)
+
+        self.assertTrue(isinstance(test_df["value"][0], float))
+
+    def test_init_invalid_float_error(self):
+        """Ensures metric values provided as strings are properly cast to a numeric value (in this case, a float)."""
+        raw_data = [
+            {
+                "metric": {"fake": "data",},
+                "value": [1627485628.789, "26.8206896551724326.82068965517243"],
+            },
+        ]
+
+        with pytest.raises(MetricValueConversionError):
+            MetricSnapshotDataFrame(data=raw_data)
+
 
 if __name__ == "__main__":
     unittest.main()