Essential metrics
Enterprise Edition

To ensure your applications are running smoothly, you should monitor:

The server load — the strain on the machine hosting Neo4j.
The Neo4j load — the strain on Neo4j.
The cluster health — to ensure the cluster is working as expected.
The workload of a Neo4j instance.

Reading the Performance section is recommended to better understand the metrics.

Server load metrics

Monitoring the hardware resources shows the strain on the server running Neo4j.

You can use utilities, such as the collectd daemon or systemd on Linux, to gather information about the system. These metrics can help with capacity planning as your workload grows.

Metric name

Description

CPU usage

If this is reaching 100%, you may need additional CPU capacity.

Used memory

This metric tells you if you are close to using all available memory on the server. Make sure your peaks are at 95% or below to reduce the risk of running out of memory. For more information, see Memory configuration.

Free disk space

Observe the rate of your data growth so you can plan for additional storage before you run out. This applies to all disks that Neo4j is writing to. You might also choose to write the log files to a different disk.

An out of disk event may disrupt system availability and cause the database going offline, thus creating the risk of database or log file corruption. To avoid that, system monitoring tools should be configured to monitor available disk space on all drives used for databases, indexes, and transactions logs. For more recommendations, see Disks, RAM and other tips, as well as Monitoring servers and Monitoring databases in a Neo4j cluster.

Neo4j load metrics

The Neo4j load metrics monitor the strain that Neo4j is being put under. They can help with capacity planning.

Metric name Metric Description

Metric name	Metric	Description
Heap usage	`<prefix>.dbms.vm.heap.used`	If Neo4j consistently uses 100% of the heap, increase the initial and max heap size. For more information, see Memory configuration.
Page cache	`<prefix>.dbms.page_cache.hit_ratio` and `<prefix>.dbms.page_cache.usage_ratio`	When a request misses the page cache, the data must be fetched from a much slower disk. Ideally, the hit_ratio should be above 98% most of the time. This shows how much of the allocated memory to the page cache is used. If this is at 100%, consider increasing the page cache size.
JVM garbage collection	`<prefix>.dbms.vm.gc.time.%s`	The proportion of time the JVM spends reclaiming the heap instead of doing other work. This metric can spike when the database is running low on memory. If this happens, it can halt processing and cause query execution errors. Consider increasing the size of your database if this appears to be the case.
Checkpoint time	`<prefix>.database.<db>.check_point.duration`	You should monitor the checkpoint duration to ensure it does not start to approach the interval between checkpoints. If this happens, consider the following steps to improve checkpointing performance: Raise the `db.checkpoint.iops.limit` to make checkpoints faster, but only if there is enough IOPS budget available to avoid slowing the commit process. `server.memory.pagecache.flush.buffer.enabled` / `server.memory.pagecache.flush.buffer.size_in_pages` make checkpoints faster by writing batches of data in a way that plays well with the underlying disk (with a nice multiple to the block size). Change the checkpointing policy (`db.checkpoint.*`, `db.checkpoint.interval.time`) to more frequent/smaller checkpoints, continuous checkpointing, or checkpoint by volume or `tx` count. For more information, see Checkpoint IOPS limit and Checkpointing and log pruning.

Heap usage

<prefix>.dbms.vm.heap.used

If Neo4j consistently uses 100% of the heap, increase the initial and max heap size. For more information, see Memory configuration.

Page cache

<prefix>.dbms.page_cache.hit_ratio and <prefix>.dbms.page_cache.usage_ratio

When a request misses the page cache, the data must be fetched from a much slower disk. Ideally, the hit_ratio should be above 98% most of the time. This shows how much of the allocated memory to the page cache is used. If this is at 100%, consider increasing the page cache size.

JVM garbage collection

<prefix>.dbms.vm.gc.time.%s

The proportion of time the JVM spends reclaiming the heap instead of doing other work. This metric can spike when the database is running low on memory. If this happens, it can halt processing and cause query execution errors. Consider increasing the size of your database if this appears to be the case.

Checkpoint time

<prefix>.database.<db>.check_point.duration

You should monitor the checkpoint duration to ensure it does not start to approach the interval between checkpoints. If this happens, consider the following steps to improve checkpointing performance:

Raise the db.checkpoint.iops.limit to make checkpoints faster, but only if there is enough IOPS budget available to avoid slowing the commit process.
server.memory.pagecache.flush.buffer.enabled / server.memory.pagecache.flush.buffer.size_in_pages make checkpoints faster by writing batches of data in a way that plays well with the underlying disk (with a nice multiple to the block size).
Change the checkpointing policy (db.checkpoint.*, db.checkpoint.interval.time) to more frequent/smaller checkpoints, continuous checkpointing, or checkpoint by volume or tx count. For more information, see Checkpoint IOPS limit and Checkpointing and log pruning.

Neo4j cluster health metrics

The cluster health metrics indicate the health of a cluster member at a glance. It is essential to know which instance is the leader. The leader’s load pattern differs from the followers, which should exhibit similar load patterns.

Metric name Metric Description

Metric name	Metric	Description
Leader	`<prefix>.database.<db>.cluster.raft.is_leader`	Track this for each database primary. It reports `0` if it is not the leader and `1` if it is the leader. The sum of all of these should always be `1`. However, there are transient periods in which the sum can be more than `1` because more than one member thinks it is the leader.
Transaction workload	`<prefix>.database.<db>.transaction.last_committed_tx_id`	The ID of the last committed transaction. Track this for each Neo4j instance. It might break into separate charts. It should show one line, ever-increasing, and if one of the lines levels off or falls behind, it is clear that this instance is no longer replicating data, and action is needed to rectify the situation.

Leader

<prefix>.database.<db>.cluster.raft.is_leader

Track this for each database primary. It reports 0 if it is not the leader and 1 if it is the leader. The sum of all of these should always be 1. However, there are transient periods in which the sum can be more than 1 because more than one member thinks it is the leader.

Transaction workload

<prefix>.database.<db>.transaction.last_committed_tx_id

The ID of the last committed transaction. Track this for each Neo4j instance. It might break into separate charts. It should show one line, ever-increasing, and if one of the lines levels off or falls behind, it is clear that this instance is no longer replicating data, and action is needed to rectify the situation.

See more about how to Monitor cluster endpoints for status information.

Workload metrics

These metrics help monitor the workload of a Neo4j instance. The absolute values of these depend on the sort of workload you expect.

Metric name Metric Description

Metric name	Metric	Description
Bolt connections	`<prefix>.dbms.bolt.connections_running`	The number of connections that are currently executing Cypher and returning results.
Total nodes/relationships	`<prefix>.database.<db>.count.node` and `<prefix>.database.<db>.count.relationship`	(Not enabled by default) Total number of distinct relationship types. Total number of distinct property names. Total number of relationships. Total number of nodes.
Throughput	`<prefix>.database.<db>.db.query.execution.latency.millis`	This metric produces a histogram of 99th and 95th percentile transaction latencies. Useful for identifying spikes or increases in the data load.

Bolt connections

<prefix>.dbms.bolt.connections_running

The number of connections that are currently executing Cypher and returning results.

Total nodes/relationships

<prefix>.database.<db>.count.node and <prefix>.database.<db>.count.relationship

(Not enabled by default) Total number of distinct relationship types. Total number of distinct property names. Total number of relationships. Total number of nodes.

Throughput

<prefix>.database.<db>.db.query.execution.latency.millis

This metric produces a histogram of 99th and 95th percentile transaction latencies. Useful for identifying spikes or increases in the data load.

For the complete list of all available metrics in Neo4j, see Metrics reference.

Glossary

allocator: A component in the cluster that allocates databases to servers according to the topology constraints specified and an allocation strategy.
asynchronous replication: Asynchronous replication is used by secondary copies to poll for new transactions, which means they cannot be guaranteed to have received the most recent transactions. This enables efficient scale-out of read-performance.
Aura instance: A fully-managed DBMS represented by a single instance ID, that is running in the Neo4j Aura cloud.
auto-commit transaction: An automatically committed transaction that contains a single query.
Bolt protocol: Bolt is a protocol used for interaction between Neo4j instances and drivers.
bookmark: A marker the client can request from the cluster to ensure that it is able to read its own writes so that the application’s state is consistent and only databases that have a copy of the bookmark are permitted to respond.
category (Bloom): A category is based on a node label and is defined in a Perspective as a way of visually distinguishing nodes with the same label(s).
causal consistency: All servers in a cluster agree on the order in which transactions take place. The position of a server on the causal chain can be guaranteed using a bookmark.
cluster: A Neo4j DBMS that spans multiple servers working together to increase fault tolerance and/or read scalability. Databases on a cluster may be configured to replicate across servers in the cluster thus achieving read scalability or high availability.
client application: Software that interacts with a Neo4j server.
commit: A commit is the successful completion of a transaction, which ensures durability of any changes made. For more details, visit Operations Manual → Transaction management.
composite database: Composite databases are the means to access partitioned graph data with a single Cypher query.
constraint: Constraints are sets of data modeling rules that ensure the data is consistent and reliable.
Cypher®: Neo4j’s graph query language.
data model: A data model defines how information is organized in a database. A good data model will make querying and understanding your data easier. In Neo4j, the data models have a graph structure.
database: A database is a container used by the DBMS to manage and store graph data. The physical structure of data is controlled by the database.
database vs graph: Databases are the physical containers of graph data. Graphs are the logical structure of data in Neo4j.
Database Management System: Database Management System, or DBMS, capable of managing multiple databases. A DBMS may run on a single server, or span several servers configured as a cluster.
database schema: The prescribed property existence and datatypes for nodes and relationships.
deallocate: An act of removing a database from a server or a server from a cluster without loss of data or reduced fault tolerance.
degree (of a node): The number of relationships of a specific node; loops are counted twice.
disaster recovery: A manual intervention to restore availability of a cluster, or databases within a cluster.
driver: A software library that provides access to Neo4j from a particular programming language.
election: In the event that the Raft leader becomes unresponsive, followers automatically trigger an election and vote for a new leader.
entity: A node or a relationship.
expression (Cypher): A component of a Cypher query which produces values. It may be used in projections, as a predicate, or when setting properties on graph elements.
fabric: Fabric is the architectural design of a unified system that provides a single access point to local or distributed graph data.
fault tolerance: A guarantee that a cluster can maintain a database’s persistence and availability in the event of one or more servers failing.
follower: A primary copy of a database acting as a follower, receives and acknowledges synchronous writes from the leader.
Generative AI (GenAI): A type of artificial intelligence (AI) system that generates text, images, or other media in response to prompts.
graph: A logical representation of a set of nodes where some pairs are connected by relationships.
index: Data structure that improves read performance of a database.
knowledge graph: A specific type of graph that has an organizing principle so that a user (or a computer system) can reason about the underlying data. The organizing principle provides an additional layer of structure that adds context to support knowledge discovery.
label: Marks a node as a member of a named and indexed subset. A node may be assigned zero or more labels.
leader: A single primary copy of a database is designated as the leader. It receives all write transactions from clients and replicates writes synchronously to followers and asynchronously to secondary copies of the database.
main database: In terms of Neo4j Enterprise Studio, the database(s) containing the user’s data. Can exist in the same Neo4j deployment as the tool asset database.
motif: A description of a specific pattern within a graph.
node: A node represents an entity or discrete object in your graph data model. Nodes can be connected by relationships, hold data in properties, and are classified by labels.
operator: A symbol representing a mathematical or logical operation.
parameter: Named value provided when running a Cypher statement.
path: A sequence of nodes and the relationships connecting them, that does not contain duplicate relationships. Several paths can match a pattern.
pattern: A specific arrangement of nodes and relationships that can be matched in a graph. A pattern follows a motif.
perspective (Bloom): A Perspective defines a certain business view or domain that can be found in the target Neo4j graph. A single Neo4j graph can be viewed through different Perspectives, each tailored for a different business purpose.
primary: A copy of the database that is able to process write transactions and is eligible to be elected as a leader. It participates in fault tolerant writes as it is part of the majority required to acknowledge and commit write transactions.
primary vs secondary: In a cluster, databases can operate in either primary or secondary mode. Primary databases are able to process write and read transactions, ensuring fault tolerance. Secondary databases are replicated asynchronously from primaries, and their main purpose is to provide read scaling within the cluster.
project (Aura): An isolated environment in the unified Aura console that contains its own database instances, configurations, and resources. Preceded by tenant in the classic Aura console.
property: Properties are key-value pairs that are used for storing data on nodes and relationships.
query (Cypher): A statement that retrieves or writes information to a database.
Raft group: A group of servers that are participating in hosting a particular database in primary mode.
Raft group member: A server that is participating in a Raft group. A server can be a member of one or more groups.
Raft log: A shared log between all Raft group members that is guaranteed to be consistently updated and viewed by those members. The log contains both database data and operational state of the Raft group.
Raft protocol: The networking mechanism that enables a database to replicate its data across multiple servers to give high availability for accessing the data and high durability to the data stored.
read scaling: Distributing query load by creating additional database copies hosted in secondary mode (read-only).
relationship: A relationship represents a connection between nodes in your graph data model. Relationships connect a source node to a target node, hold data in properties, and are classified by type.
secondary: An asynchronously replicated copy of the database that provides read scaling within the cluster.
seed: A seed is a database dump or a full backup used to create a database on a cluster. This is sometimes called seeding.
server: A physical machine, a virtual machine, or a container running an instance of Neo4j. Servers can be standalone or part of a cluster.
session: A causally linked sequence of transactions.
session consistency: An alternative name for Neo4j’s causal consistency.
standalone: A single server running Neo4j and not part of a cluster.
synchronous replication: Synchronous replication requires the leader primary to replicate a transaction and block the commit until a quorum of the follower primaries acknowledges that the transaction is successfully replicated. Once the transaction is replicated, the commit is allowed to proceed. This ensures data durability and consistency within the cluster.
system database: A database used by Neo4j to store system information.
tenant (Aura): An isolated environment in the classic Aura console that contains its own database instances, configurations, and resources. Replaced by project in the unified Aura console.
tool asset database: In terms of Neo4j Enterprise Studio, the database where tools' assets are stored. This can be in the same Neo4j deployment as the main database(s) or in a separate deployment.
topology: A configuration that describes how the copies of a database should be spread across the servers in a cluster, see primary mode and secondary mode.
transaction: A transaction comprises a unit of work performed against a database. It is treated in a coherent and reliable way, independent of other transactions. Transactions comply with the ACID consistency model (atomic, consistent, isolated, and durable).

Essential metricsEnterprise Edition

Server load metrics

Neo4j load metrics

Neo4j cluster health metrics

Workload metrics

Glossary

Essential metrics
Enterprise Edition