The Neon Datadog integration

What you will learn:

How to set up the integration
The full list of externally-available metrics

External docs

Available for Scale and Business Plan users, the Neon Datadog integration lets you monitor Neon database performance, resource utilization, and system health directly from Datadog's observability platform.

Feature coming soon

This feature is available by invitation only for select early-access customers. It is experimental and subject to change.

How it works

The integration leverages a list of metrics that Neon makes available for export to third-party services. By configuring the integration with your Datadog API key, Neon automatically sends metrics from your project to your selected Datadog site. Some of the key metrics include:

Connection counts — Tracks active and idle database connections.
Database size — Monitors total size of all databases in bytes.
Replication delay — Measures replication lag in bytes and seconds.
Compute metrics — Includes CPU and memory usage statistics for your compute.

note

Metrics are sent for all computes in your Neon project. For example, if you have multiple branches, each with an attached compute, metrics will be collected and sent for each compute.

Prerequisites

Before getting started, ensure the following:

You have a Neon account and project. If not, see Sign up for a Neon account.
You have a Datadog account and API key.
You know the region you selected for your Datadog account. Here's how to check: Find your Datadog region

Steps to integrate Datadog with Neon

In the Neon Console, navigate to the Integrations page in your Neon project.
Locate the Datadog card and click Add.
Enter your Datadog API key. You can generate or retrieve Datadog API Keys from your Datadog organization.
Select the Datadog site that you used when setting up your Datadog account.
Click Confirm to complete the integration.

Optionally, you can import the Neon-provided JSON configuration file into Datadog, which creates a pre-built dashboard from Neon metrics, similar to the charts available on our Monitoring page. See Import Neon dashboard

Once set up, Neon will start sending metrics to Datadog, and you can use these metrics to create custom dashboards and alerts in Datadog.

note

Neon computes only send metrics when they are active. If the Autosuspend feature is enabled and a compute is suspended due to inactivity, no metrics will be sent during the suspension. This may result in gaps in your Datadog metrics. If you notice missing data in Datadog, check if your compute is suspended. You can verify a compute's status as Idle or Active on the Branches page in the Neon console, and review Suspend compute events on the System operations tab of the Monitoring page.

Additionally, if you are setting up Neon’s Datadog integration for a project with an inactive compute, you'll need to activate the compute before it can send metrics to Datadog. To activate it, simply run a query from the Neon SQL Editor or any connected client on the branch associated with the compute.

Example usage in Datadog

Once integrated, you can create custom dashboards in Datadog by querying the metrics sent from Neon. Use Datadog's Metrics Explorer to search for metrics like connection_counts, db_total_size, and host_cpu_seconds_total. You can also set alerts based on threshold values for critical metrics.

Import the Neon dashboard

As part of the integration, Neon provides a JSON configuration file that you can import into Datadog to start with a pre-built dashboard based on a subset of Neon metrics.

neon dashboard in datadog

Here's how you can import the dashboard:

In the Neon Console, open your Datadog integration from the Integrations page.
Scroll to the bottom of the panel and copy the JSON from there.

OR

You can copy the JSON below instead.
Next, create a new dashboard in Datadog.
Open Configure, select Import dashboard JSON, then paste the Neon-provided configuration JSON.

If any of the computes in your project are active, you should start seeing data in the resulting charts right away. By default, the charts show metrics for all active endpoints in your project. You can filter results to one or more selected endpoints using the endpoint_id variable dropdown selector.

select endpoint variable in dashboard

Dashboard JSON

Copy JSON configuration

{
"title": "Single Neon Compute metrics (with dropdown)",
"description": "",
"widgets": [
    {
    "id": 7296782684811837,
    "definition": {
        "title": "CPU",
        "title_size": "16",
        "title_align": "left",
        "show_legend": true,
        "legend_layout": "auto",
        "legend_columns": [
        "avg",
        "min",
        "max",
        "value",
        "sum"
        ],
        "type": "timeseries",
        "requests": [
        {
            "formulas": [
            {
                "alias": "Used",
                "formula": "per_minute(host_cpu_seconds_total)"
            }
            ],
            "queries": [
            {
                "name": "host_cpu_seconds_total",
                "data_source": "metrics",
                "query": "sum:host_cpu_seconds_total{!mode:idle,$endpoint_id}.as_count()"
            }
            ],
            "response_format": "timeseries",
            "style": {
            "palette": "dog_classic",
            "order_by": "values",
            "line_type": "solid",
            "line_width": "normal"
            },
            "display_type": "line"
        }
        ]
    },
    "layout": {
        "x": 0,
        "y": 0,
        "width": 5,
        "height": 2
    }
    },
    {
    "id": 3831219857468963,
    "definition": {
        "title": "RAM",
        "title_size": "16",
        "title_align": "left",
        "show_legend": true,
        "legend_layout": "auto",
        "legend_columns": [
        "avg",
        "min",
        "max",
        "value",
        "sum"
        ],
        "type": "timeseries",
        "requests": [
        {
            "formulas": [
            {
                "number_format": {
                "unit": {
                    "type": "canonical_unit",
                    "unit_name": "byte"
                }
                },
                "alias": "Cached",
                "formula": "ram_cached"
            },
            {
                "alias": "Used",
                "number_format": {
                "unit": {
                    "type": "canonical_unit",
                    "unit_name": "byte"
                }
                },
                "formula": "ram_total - ram_available"
            }
            ],
            "queries": [
            {
                "name": "ram_cached",
                "data_source": "metrics",
                "query": "avg:host_memory_cached_bytes{$endpoint_id}"
            },
            {
                "name": "ram_total",
                "data_source": "metrics",
                "query": "avg:host_memory_total_bytes{$endpoint_id}"
            },
            {
                "name": "ram_available",
                "data_source": "metrics",
                "query": "avg:host_memory_available_bytes{$endpoint_id}"
            }
            ],
            "response_format": "timeseries",
            "style": {
            "palette": "dog_classic",
            "order_by": "values",
            "line_type": "solid",
            "line_width": "normal"
            },
            "display_type": "line"
        }
        ]
    },
    "layout": {
        "x": 5,
        "y": 0,
        "width": 4,
        "height": 2
    }
    },
    {
    "id": 7513607855022102,
    "definition": {
        "title": "Connections",
        "title_size": "16",
        "title_align": "left",
        "show_legend": true,
        "legend_layout": "auto",
        "legend_columns": [
        "avg",
        "min",
        "max",
        "value",
        "sum"
        ],
        "type": "timeseries",
        "requests": [
        {
            "formulas": [
            {
                "alias": "Total",
                "formula": "connection_counts"
            },
            {
                "alias": "Active",
                "formula": "active_connection_counts"
            },
            {
                "alias": "Idle",
                "formula": "idle_connection_count"
            }
            ],
            "queries": [
            {
                "name": "connection_counts",
                "data_source": "metrics",
                "query": "avg:connection_counts{!datname:postgres,$endpoint_id}"
            },
            {
                "name": "active_connection_counts",
                "data_source": "metrics",
                "query": "avg:connection_counts{!datname:postgres,state:active}"
            },
            {
                "name": "idle_connection_count",
                "data_source": "metrics",
                "query": "avg:connection_counts{!datname:postgres,!state:active,$endpoint_id}"
            }
            ],
            "response_format": "timeseries",
            "style": {
            "palette": "dog_classic",
            "order_by": "values",
            "line_type": "solid",
            "line_width": "normal"
            },
            "display_type": "line"
        }
        ]
    },
    "layout": {
        "x": 0,
        "y": 2,
        "width": 6,
        "height": 2
    }
    },
    {
    "id": 5523349536895199,
    "definition": {
        "title": "Database size",
        "title_size": "16",
        "title_align": "left",
        "show_legend": true,
        "legend_layout": "auto",
        "legend_columns": [
        "avg",
        "min",
        "max",
        "value",
        "sum"
        ],
        "type": "timeseries",
        "requests": [
        {
            "formulas": [
            {
                "number_format": {
                "unit": {
                    "type": "canonical_unit",
                    "unit_name": "byte"
                }
                },
                "formula": "pg_stats_userdb"
            },
            {
                "number_format": {
                "unit": {
                    "type": "canonical_unit",
                    "unit_name": "byte"
                }
                },
                "alias": "Size of all databases",
                "formula": "pg_stats_userdb"
            },
            {
                "alias": "Max size",
                "number_format": {
                "unit": {
                    "type": "canonical_unit",
                    "unit_name": "byte"
                }
                },
                "formula": "db_total_size * 1024 * 1024"
            }
            ],
            "queries": [
            {
                "name": "pg_stats_userdb",
                "data_source": "metrics",
                "query": "sum:pg_stats_userdb{kind:db_size,$endpoint_id} by {datname}"
            },
            {
                "name": "db_total_size",
                "data_source": "metrics",
                "query": "sum:db_total_size{$endpoint_id}"
            },
            {
                "name": "max_cluster_size",
                "data_source": "metrics",
                "query": "avg:max_cluster_size{$endpoint_id}"
            }
            ],
            "response_format": "timeseries",
            "style": {
            "palette": "dog_classic",
            "order_by": "values",
            "line_type": "solid",
            "line_width": "normal"
            },
            "display_type": "line"
        }
        ],
        "yaxis": {
        "include_zero": false,
        "scale": "log"
        }
    },
    "layout": {
        "x": 0,
        "y": 4,
        "width": 5,
        "height": 3
    }
    },
    {
    "id": 5728659221127513,
    "definition": {
        "title": "Changed rows",
        "title_size": "16",
        "title_align": "left",
        "show_legend": true,
        "legend_layout": "auto",
        "legend_columns": [
        "avg",
        "min",
        "max",
        "value",
        "sum"
        ],
        "type": "timeseries",
        "requests": [
        {
            "formulas": [
            {
                "alias": "Rows inserted",
                "formula": "diff(inserted_rows)"
            },
            {
                "alias": "Rows deleted",
                "formula": "diff(deleted_rows)"
            },
            {
                "alias": "Rows updated",
                "formula": "diff(updated_rows)"
            }
            ],
            "queries": [
            {
                "name": "inserted_rows",
                "data_source": "metrics",
                "query": "avg:pg_stats_userdb{kind:inserted,$endpoint_id}"
            },
            {
                "name": "deleted_rows",
                "data_source": "metrics",
                "query": "avg:pg_stats_userdb{kind:deleted,$endpoint_id}"
            },
            {
                "name": "updated_rows",
                "data_source": "metrics",
                "query": "avg:pg_stats_userdb{kind:updated,$endpoint_id}"
            }
            ],
            "response_format": "timeseries",
            "style": {
            "palette": "dog_classic",
            "order_by": "values",
            "line_type": "solid",
            "line_width": "normal"
            },
            "display_type": "line"
        }
        ]
    },
    "layout": {
        "x": 5,
        "y": 4,
        "width": 5,
        "height": 3
    }
    },
    {
    "id": 1608572645458648,
    "definition": {
        "title": "Deadlocks",
        "title_size": "16",
        "title_align": "left",
        "show_legend": true,
        "legend_layout": "auto",
        "legend_columns": [
        "avg",
        "min",
        "max",
        "value",
        "sum"
        ],
        "type": "timeseries",
        "requests": [
        {
            "formulas": [
            {
                "alias": "Deadlocks",
                "formula": "deadlocks"
            }
            ],
            "queries": [
            {
                "name": "deadlocks",
                "data_source": "metrics",
                "query": "avg:pg_stats_userdb{kind:deadlocks,$endpoint_id}"
            }
            ],
            "response_format": "timeseries",
            "style": {
            "palette": "dog_classic",
            "order_by": "values",
            "line_type": "solid",
            "line_width": "normal"
            },
            "display_type": "line"
        }
        ]
    },
    "layout": {
        "x": 0,
        "y": 7,
        "width": 4,
        "height": 2
    }
    }
],
"template_variables": [
    {
    "name": "endpoint_id",
    "prefix": "endpoint_id",
    "available_values": [],
    "default": "*"
    }
],
"layout_type": "ordered",
"notify_list": [],
"reflow_type": "fixed"
}

Available metrics

Neon makes the following metrics available for export to third parties; for now, availability is limited to the Datadog integration but will soon be expanded to other providers.

All metrics include the following labels:

project_id
endpoint_id
compute_id
job

Here's an example of the metric db_total_size with all labels:

db_total_size{project_id="square-daffodil-12345678", endpoint_id="ep-aged-art-260862", compute_id="compute-shrill-blaze-b4hry7fg", job="sql-metrics"} 10485760

note

In Datadog, metric labels are referred to as tags. See Getting Started with Tags in the Datadog Docs.

Name	Job	Description
connection_counts	sql-metrics	Total number of database connections. The `state` label indicates whether the connection is `active` (executing queries), `idle` (awaiting queries), or in a variety of other states derived from the pg_stat_activity Postgres view.
db_total_size	sql-metrics	Total size of all databases in your project, measured in bytes.
lfc_approximate_working_set_size_windows	sql-metrics	Approximate working set size in pages of 8192 bytes. The metric is tracked over time windows (5m, 15m, 1h) to gauge access patterns. Duration values: `duration="5m"`, `duration="15m"`, `duration="1h"`.
lfc_cache_size_limit	sql-metrics	The limit on the size of the Local File Cache (LFC), measured in bytes.
lfc_hits	sql-metrics	The number of times requested data was found in the LFC (cache hit). Higher cache hit rates indicate efficient memory use.
lfc_misses	sql-metrics	The number of times requested data was not found in the LFC (cache miss), forcing a read from slower storage. High miss rates may indicate insufficient compute size.
lfc_used	sql-metrics	The amount of space currently used in the LFC, measured in 1MB chunks. It reflects how much of the cache limit is being utilized.
lfc_writes	sql-metrics	The number of write operations to the LFC.
max_cluster_size	sql-metrics	The `neon.max_cluster_size` setting in MB.
pg_stats_userdb	sql-metrics	Aggregated metrics from the pg_stat_database Postgres view. We collect stats from the oldest non-system databases based on their creation time, but not for all databases. Only the first X databases (sorted by creation time) are included. datname: The name of the database kind: The type of value being reported. One of the following: db_size: The size of the database on disk, in bytes (pg_database_size(datname)) deadlocks: The number of deadlocks detected inserted: The number of rows inserted (tup_inserted) updated: The number of rows updated (tup_updated) deleted: The number of rows deleted (tup_deleted)
replication_delay_bytes	sql-metrics	The number of bytes between the last received LSN (`Log Sequence Number`) and the last replayed one. Large values indiciate replication lag.
replication_delay_seconds	sql-metrics	Time since the last `LSN` was replayed.
host_cpu_seconds_total	compute-host-metrics	The number of CPU seconds accumulated in different operating modes (user, system, idle, etc.).
host_load1	compute-host-metrics	System load averaged over the last 1 minute. Example: for 0.25 vCPU, `host_load1` of `0.25` means full utilization, >0.25 indicates waiting processes.
host_load5	compute-host-metrics	System load averaged over the last 5 minutes.
host_load15	compute-host-metrics	System load averaged over the last 15 minutes.
host_memory_active_bytes	compute-host-metrics	The number of bytes of active main memory.
host_memory_available_bytes	compute-host-metrics	The number of bytes of main memory available.
host_memory_buffers_bytes	compute-host-metrics	The number of bytes of main memory used by buffers.
host_memory_cached_bytes	compute-host-metrics	The number of bytes of main memory used by cached blocks.
host_memory_free_bytes	compute-host-metrics	The number of bytes of main memory not used.
host_memory_shared_bytes	compute-host-metrics	The number of bytes of main memory shared between processes.
host_memory_swap_free_bytes	compute-host-metrics	The number of free bytes of swap space.
host_memory_swap_total_bytes	compute-host-metrics	The total number of bytes of swap space.
host_memory_swap_used_bytes	compute-host-metrics	The number of used bytes of swap space.
host_memory_swapped_in_bytes_total	compute-host-metrics	The number of bytes that have been swapped into main memory.
host_memory_swapped_out_bytes_total	compute-host-metrics	The number of bytes that have been swapped out from main memory.
host_memory_total_bytes	compute-host-metrics	The total number of bytes of main memory.
host_memory_used_bytes	compute-host-metrics	The number of bytes of main memory used by programs or caches.

Feedback and future improvements

We’re always looking to improve! If you have feature requests or feedback, please let us know via the Feedback form in the Neon Console or on our Discord channel.

Need help?

Join our Discord Server to ask questions or see what others are doing with Neon. Users on paid plans can open a support ticket from the console. For more detail, see Getting Support.