Prometheus Remote-Write

The Streamfold Prometheus Remote-Write source will act as a receiver for Prometheus Remote-Write requests.

Overview

Prometheus is an open source metrics and alerting tool for system and application monitoring. Prometheus stores all data as time series streams of timestamped values belonging to the same metric and the same set of labeled dimensions (tags).

Configuration

Protocol Buffers over HTTP

Prometheus Remote-Write requests will be accepted at the following endpoint: POST /streamfold/prometheus/write

Response Codes

The source will respond with the follow codes:

• 204: status no content on accepted
• 400: invalid format, may be due to content type or encoding not matching, or malformed payload (see below)
• 401: invalid or missing authentication
• 404: wrong path or method
• 429: too many requests, back pressure in system (TBD)
• 500: misc server error

Authentication

The following authentication methods will be supported:

• Basic auth

  • Username: streamfold
  • Password: <sf-ingress-token>

• Bearer

  • Authorization: Bearer <sf-ingress-token>

• Streamfold Token Header

  • X-Streamfold-Token: <sf-ingress-token>

Content Encoding

Must be set to snappy

Content Type:

Must be set to application/x-protobuf

Event Meta

The Prometheus Remote-Write source will use the following meta data:

• http: map to the incoming HTTP path and header data
• type: metric
• source->type : prometheus_remote_write

Example Converted Payload

{
	"_timestamp": "2024-06-06T09:39:43.475231000-07:00",
	"_ingested_timestamp": "2024-06-06T09:39:43.475231000-07:00",
	"_raw": "",
	"_partition": "cbf29ce484222325",
	"_meta": {
		"source": {
			"type": "prometheus_remote_write",
			"id": "1",
			"name": "prometheus_remote_write"
		},
		"http": {
			"path": "/streamfold/prometheus/write",
			"method": "POST",
			"headers": {
				"Content-Type": [
					"application/x-protobuf"
				],
				"Content-Encoding": [
					"snappy"
				]
			}
		},
		"peer": {
			"client_ip": ""
		},
		"type": "metric"
	},
	"metadata": [
		{
			"type": 0,
			"metric_family_name": "a",
			"help": "b",
			"unit": "seconds"
		},
		{
			"unit": "errors",
			"type": 1,
			"metric_family_name": "c",
			"help": "d"
		}
	],
	"timeseries": [
		{
			"exemplars": [
				{
					"labels": [
						{
							"name": "f",
							"value": "g"
						}
					],
					"value": 1.000000,
					"timestamp": 0
				}
			],
			"histograms": [
				{
					"positive_spans": [
						{
							"offset": 0,
							"length": 1
						}
					],
					"count": 0.000000,
					"negative_spans": [
						{
							"offset": 0,
							"length": 1
						}
					],
					"negative_deltas": [
						-1
					],
					"negative_counts": [],
					"count_original_type": 0,
					"sum": 20.000000,
					"zero_threshold": 0.000000,
					"reset_hint": 0,
					"positive_deltas": [
						1
					],
					"positive_counts": [],
					"timestamp": 0,
					"schema": 2,
					"zero_count": 0.000000,
					"zero_count_original_type": 0
				},
				{
					"zero_count_original_type": 1,
					"negative_spans": [
						{
							"length": 1,
							"offset": 0
						}
					],
					"timestamp": 1,
					"count": 0.000000,
					"sum": 20.000000,
					"zero_threshold": 0.000000,
					"zero_count": 0.000000,
					"count_original_type": 1,
					"schema": 2,
					"positive_spans": [
						{
							"offset": 0,
							"length": 1
						}
					],
					"positive_deltas": [],
					"negative_deltas": [],
					"negative_counts": [
						-1.000000
					],
					"positive_counts": [
						1.000000
					],
					"reset_hint": 0
				}
			],
			"labels": [
				{
					"name": "__name__",
					"value": "test_metric1"
				},
				{
					"name": "b",
					"value": "c"
				}
			],
			"samples": [
				{
					"value": 1.000000,
					"timestamp": 0
				}
			]
		},
		{
			"labels": [
				{
					"name": "__name__",
					"value": "test_metric2"
				},
				{
					"name": "foo",
					"value": "bar"
				}
			],
			"samples": [
				{
					"value": 2.000000,
					"timestamp": 1
				}
			],
			"exemplars": [
				{
					"labels": [
						{
							"name": "h",
							"value": "i"
						}
					],
					"value": 2.000000,
					"timestamp": 1
				}
			],
			"histograms": [
				{
					"reset_hint": 0,
					"timestamp": 2,
					"schema": 2,
					"zero_count": 0.000000,
					"positive_spans": [
						{
							"offset": 0,
							"length": 1
						}
					],
					"count": 0.000000,
					"count_original_type": 0,
					"negative_spans": [
						{
							"length": 1,
							"offset": 0
						}
					],
					"positive_counts": [],
					"zero_threshold": 0.000000,
					"zero_count_original_type": 0,
					"negative_counts": [],
					"sum": 20.000000,
					"negative_deltas": [
						-1
					],
					"positive_deltas": [
						1
					]
				},
				{
					"count_original_type": 1,
					"zero_threshold": 0.000000,
					"negative_counts": [
						-1.000000
					],
					"positive_spans": [
						{
							"offset": 0,
							"length": 1
						}
					],
					"positive_counts": [
						1.000000
					],
					"reset_hint": 0,
					"schema": 2,
					"negative_spans": [
						{
							"offset": 0,
							"length": 1
						}
					],
					"positive_deltas": [],
					"count": 0.000000,
					"sum": 20.000000,
					"zero_count": 0.000000,
					"zero_count_original_type": 1,
					"negative_deltas": [],
					"timestamp": 3
				}
			]
		}
	]
}