Transaction logging

Neo4j keeps track of all write operations to each database to ensure data consistency and enable recovery.

Transaction log files

A transaction log file contains a sequence of records with all changes made to a particular database as part of each transaction, including data, indexes, and constraints.

The transaction log serves multiple purposes, including providing differential backups and supporting cluster operations. At a minimum, the most recent non-empty transaction log is retained for any given configuration. It is important to note that transaction logs are unrelated to log monitoring.

The transaction logging configuration is set per database and can be configured using the following configuration settings:

Configure transaction log location

By default, transaction logs for a database are located at <NEO4J_HOME>/data/transactions/<database-name>.

The root directory where those folders are located is configured by server.directories.transaction.logs.root. The value is a path. If relative, it is resolved from server.directories.data. For maximum performance, it is recommended to configure transaction logs to be stored on a dedicated device.

Configure transaction log preallocation

You can specify if Neo4j should try to preallocate logical log files in advance using the parameter db.tx_log.preallocate. By default, it is true. Log preallocation optimizes the filesystem by ensuring there is room to accommodate newly generated files and avoid file-level fragmentation. This configuration setting is dynamic and can be changed at runtime.

Configure transaction log rotation size

You can specify how much space a single transaction log file can roughly occupy using db.tx_log.rotation.size. By default, it is set to 256 MiB, which means that after a transaction log file reaches this size, it is rotated and a new one is created. The minimum accepted value is 128K (128 KiB). This configuration setting is dynamic and can be changed at runtime.

This setting influences how much space can be reclaimed by all checkpoint strategies under the following:

To reclaim a given file, the newest checkpoint for the transaction log must exist in another file. So if you have a huge transaction log, then it is likely that your latest checkpoint is in the same file, making it impossible to reclaim said file. For information about checkpointing, see Control transaction log pruning.

Configure transaction log retention policy

Manually deleting transaction log files is not supported.

You can control the number of transaction logs that Neo4j keeps to back up the database using the parameter db.tx_log.rotation.retention_policy. This configuration setting is dynamic and can be changed at runtime. For more information about how to do it, see Update dynamic settings.

The default value is 2 days 2G, which means Neo4j keeps logical logs that contain any transaction committed within 2 days from the current time and within the allocated log space (2G) and prunes the ones that are older or larger.

Other possible ways to configure the log retention policy are:

db.tx_log.rotation.retention_policy=true|keep_all — keep transaction logs indefinitely.

This option is not recommended due to the effectively unbounded storage usage. Old transaction logs cannot be safely archived or removed by external jobs since safe log pruning requires knowledge about the most recent successful checkpoint.

db.tx_log.rotation.retention_policy=false|keep_none — keep only the most recent non-empty log.

Log pruning is called only after checkpoint completion to ensure at least one checkpoint and points to a valid place in the transaction log data. In reality, this means that all transaction logs created between checkpoints are kept for some time, and only after a checkpoint, the pruning strategy removes them. For more details on how to speed up checkpointing, see Control transaction log pruning. To force a checkpoint, run the procedure CALL db.checkpoint().

This option is not recommended in production Enterprise Edition environments, as differential backups rely on the presence of the transaction logs since the last backup.

<number><optional unit> <type> <optional space restriction> where valid units are K, M, and G, and valid types are files, size, txs, entries, hours, and days. Valid optional space restriction is a logical log space restriction like 1G. For example, 2 days 1G limits the logical log space on the disk to 1G at most 2 days per database.

Table 1. Types that can be used to control log retention
Type	Description	Example
`files`	The number of the most recent transaction log files to keep after pruning.	`db.tx_log.rotation.retention_policy=10 files`
`size`	The max disk size of the transaction log files to keep after pruning. For example, `500M size` leaves at least 500M worth of files behind.	`db.tx_log.rotation.retention_policy=300M size`
`txs or entries`	The number of transactions (in the files) to keep after pruning, regardless of file count or size. `txs` and `entries` are synonymous. If set, the policy keeps the 500k latest transactions from each database and prunes any older transactions.	`db.tx_log.rotation.retention_policy=500k txs`
`hours`	Keep logs that contain any transaction committed within the specified number of hours from the current time. The value of `10 hours` ensures that at least 10 hours' worth of transactions is present in the logs.	`db.tx_log.rotation.retention_policy=10 hours`
`days`	Keep logs that contain any transaction committed within the specified number of days from the current time.	`db.tx_log.rotation.retention_policy=30 days`
`days and size`	Keep logs that contain any transaction committed within the specified number of days from the current time and within the allocated log space.	`db.tx_log.rotation.retention_policy=2 days 1G`

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).