Space reuse

Neo4j uses logical deletes to remove data from the database to achieve maximum performance and scalability. A logical delete means that all relevant records are marked as deleted, but the space they occupy is not immediately returned to the operating system. Instead, it is subsequently reused by the transactions creating data.

Marking a record as deleted requires writing a record update command to the transaction log files, as when something is created or updated. Therefore, when deleting large amounts of data, this leads to a storage usage growth of that particular database, because Neo4j writes records for all deleted nodes, their properties, and relationships to the transaction log.

Keep in mind that when doing DETACH DELETE on many nodes, those deletes can take up more space in the in-memory transaction state and the transaction log than you might expect.

Transactions are eventually pruned out of the transaction log files, bringing the storage usage of the log back down to the expected level. The store files, on the other hand, do not shrink when data is deleted. The space that the deleted records take up is kept in the store files. Until the space is reused, the store files are sparse and fragmented, but the performance impact of this is usually minimal.

ID files

Neo4j uses .id files for managing the space that can be reused. These files contain the set of IDs for all the deleted records in their respective files. The ID of the record uniquely identifies it within the store file. For instance, depending on the store format, the IDs of all deleted nodes are contained in neostore.nodestore.db.id or block.x1.db.id.

These .id files are maintained as part of the write transactions that interact with them. When a write transaction commits a deletion, the record’s ID is buffered in memory. The buffer keeps track of all overlapping unfinished transactions. When they complete, the ID becomes available for reuse.

The buffered IDs are flushed to the .id files as part of the checkpointing. Concurrently, the .id file changes (the ID additions and removals) are inferred from the transaction commands. This way, the recovery process ensures that the .id files are always in-sync with their store files. The same process also ensures that clustered databases have precise and transactional space reuse.

If you want to shrink the size of your database, do not delete the .id files. The store files must only be modified by the Neo4j database and the neo4j-admin tools.

Reuse of space for large property values in block format
Enterprise EditionIntroduced in 2026.01

Starting with Neo4j 2026.01, when allocating a large value of N units, if no such ID is available, the record is split into multiple smaller records. This guarantees the reuse of space for large property values stored in the block.big_values.db store file, sacrificing colocation in some cases. The level of accepted fragmentation is dynamic and depends on the fraction of unused records, i.e., no fragmentation if < 5% of the space is unused, then accepting some fragmentation for a bit higher unused percentage. If the unused percentage is high, records can be even more fragmented. Most write loads benefit from this improved space reuse, particularly those with "skewed" allocation sizes, e.g., when allocation sizes consistently grow over time. See Block format store files for more details on the big property values store.

Reclaim unused space
Enterprise Edition

You can use the neo4j-admin database copy command to create a defragmented copy of your database. The copy command creates and entirely new and independent database. If you want to run that database in a cluster, you have to re-seed the existing cluster, or seed a new cluster from that copy.

Example 1. Example of database compaction using neo4j-admin database copy

The following is a detailed example on how to check your database store usage and how to reclaim space.

Let’s use the Cypher Shell command-line tool to add 100k nodes and then see how much store they occupy.

  1. In a running Neo4j standalone instance, log in to the Cypher Shell command-line tool with your credentials.

    bin/cypher-shell -u neo4j -p <password>
    Connected to Neo4j at neo4j://localhost:7687 as user neo4j.
Type :help for a list of available commands or :exit to exit the shell.
Note that Cypher queries must end with a semicolon.

  2. Add 100k nodes to the neo4j database using the following command:

    neo4j@neo4j> foreach (x in range (1,100000) | create (n:testnode1 {id:x}));
    0 rows available after 1071 ms, consumed after another 0 ms
Added 100000 nodes, Set 100000 properties, Added 100000 labels

  3. Check the allocated ID range:

    neo4j@neo4j> MATCH (n:testnode1) RETURN ID(n) as ID order by ID limit 5;
    +----+
| ID |
+----+
| 0  |
| 1  |
| 2  |
| 3  |
| 4  |
+----+

5 rows available after 171 ms, consumed after another 84 ms

  4. Run call db.checkpoint() procedure to force a checkpoint.

    neo4j@neo4j> call db.checkpoint();
    +-----------------------------------+
| success | message                 |
+-----------------------------------+
| TRUE    | "Checkpoint completed." |
+-----------------------------------+

1 row available after 18 ms, consumed after another 407 ms

  5. In Neo4j Browser, run :sysinfo to check the total store size of neo4j.

    The reported output for the store size is 791.92 KiB, ID Allocation: Node ID 100000, Property ID 100000.

  6. Delete the above created nodes.

    neo4j@neo4j> Match (n) detach delete n;

  7. Run call db.checkpoint() procedure again.

    neo4j@neo4j> call db.checkpoint();
    +-----------------------------------+
| success | message                 |
+-----------------------------------+
| TRUE    | "Checkpoint completed." |
+-----------------------------------+

1 row available after 18 ms, consumed after another 407 ms

  8. In Neo4j Browser, run :sysinfo to check the total store size of neo4j.

    The reported output for the store size is 31.01 MiB, ID Allocation: Node ID 100000, Property ID 100000.

    By default, a checkpoint flushes any cached updates in pagecache to store files. Thus, the allocated IDs remain unchanged, and the store size increases or does not alter (if the instance restarts) despite the deletion. In a production database, where numerous load/deletes are frequently performed, the result is a significant unused space occupied by store files.

To reclaim that unused space, you can use the neo4j-admin database copy command to create a defragmented copy of your database. Use the system database and stop the neo4j database before running the command.

  1. Invoke the neo4j-admin database copy command to create a copy of your neo4j database.

    bin/neo4j-admin database copy neo4j neo4jcopy1 --compact-node-store --verbose
    Starting to copy store, output will be saved to: $neo4j_home/logs/neo4j-admin-copy-2020-11-04.11.30.57.log
2020-10-23 11:40:00.749+0000 INFO [StoreCopy] ### Copy Data ###
2020-10-23 11:40:00.750+0000 INFO [StoreCopy] Source: $neo4j_home/data/databases/neo4j (page cache 8m) (page cache 8m)
2020-10-23 11:40:00.750+0000 INFO [StoreCopy] Target: $neo4j_home/data/databases/neo4jcopy1 (page cache 8m)
2020-10-23 11:40:00.750+0000 INFO [StoreCopy] Empty database created, will start importing readable data from the source.
2020-10-23 11:40:02.397+0000 INFO [o.n.i.b.ImportLogic] Import starting
Nodes, started 2020-11-04 11:31:00.088+0000
[*Nodes:?? 7.969MiB---------------------------------------------------------------------------] 100K ∆ 100K
Done in 632ms
Prepare node index, started 2020-11-04 11:31:00.735+0000
[*DETECT:7.969MiB-----------------------------------------------------------------------------]    0 ∆    0
Done in 79ms
Relationships, started 2020-11-04 11:31:00.819+0000
[*Relationships:?? 7.969MiB-------------------------------------------------------------------]    0 ∆    0
Done in 37ms
Node Degrees, started 2020-11-04 11:31:01.162+0000
[*>:??----------------------------------------------------------------------------------------]    0 ∆    0
Done in 12ms
Relationship --> Relationship 1/1, started 2020-11-04 11:31:01.207+0000
[*>:??----------------------------------------------------------------------------------------]    0 ∆    0
Done in 0ms
RelationshipGroup 1/1, started 2020-11-04 11:31:01.232+0000
[*>:??----------------------------------------------------------------------------------------]    0 ∆    0
Done in 10ms
Node --> Relationship, started 2020-11-04 11:31:01.245+0000
[*>:??----------------------------------------------------------------------------------------]    0 ∆    0
Done in 10ms
Relationship <-- Relationship 1/1, started 2020-11-04 11:31:01.287+0000
[*>:??----------------------------------------------------------------------------------------]    0 ∆    0
Done in 0ms
Count groups, started 2020-11-04 11:31:01.549+0000
[*>:??----------------------------------------------------------------------------------------]    0 ∆    0
Done in 0ms
Node --> Group, started 2020-11-04 11:31:01.579+0000
[*>:??----------------------------------------------------------------------------------------]    0 ∆    0
Done in 1ms
Node counts and label index build, started 2020-11-04 11:31:01.986+0000
[*>:??----------------------------------------------------------------------------------------]    0 ∆    0
Done in 11ms
Relationship counts, started 2020-11-04 11:31:02.034+0000
[*>:??----------------------------------------------------------------------------------------]    0 ∆    0
Done in 0ms

IMPORT DONE in 3s 345ms.
Imported:
  0 nodes
  0 relationships
  0 properties
Peak memory usage: 7.969MiB
2020-11-04 11:31:02.835+0000 INFO [o.n.i.b.ImportLogic] Import completed successfully, took 3s 345ms. Imported:
  0 nodes
  0 relationships
  0 properties
2020-11-04 11:31:03.330+0000 INFO [StoreCopy] Import summary: Copying of 100704 records took 5 seconds (20140 rec/s). Unused Records 100704 (100%) Removed Records 0 (0%)
2020-11-04 11:31:03.330+0000 INFO [StoreCopy] ### Extracting schema ###
2020-11-04 11:31:03.330+0000 INFO [StoreCopy] Trying to extract schema...
2020-11-04 11:31:03.338+0000 INFO [StoreCopy] ... found 0 schema definitions.

    The example resulted in a compact and consistent store (any inconsistent nodes, properties, relationships are not copied over to the newly created store).

  2. Use the system database and create the neo4jcopy1 database.

    neo4j@system> create database neo4jcopy1;
    0 rows available after 60 ms, consumed after another 0 ms

  3. Verify that the neo4jcopy1 database is online.

    neo4j@system> show databases;
    +-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| name         | type       | aliases | access       | address          | role      | writer | requestedStatus | currentStatus | statusMessage | default | home  | constituents |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| "neo4j"      | "standard" | []      | "read-write" | "localhost:7687" | "primary" | TRUE   | "offline"       | "offline"     | ""            | TRUE    | TRUE  | []           |
| "neo4jcopy1" | "standard" | []      | "read-write" | "localhost:7687" | "primary" | TRUE   | "online"        | "online"      | ""            | FALSE   | FALSE | []           |
| "system"     | "system"   | []      | "read-write" | "localhost:7687" | "primary" | TRUE   | "online"        | "online"      | ""            | FALSE   | FALSE | []           |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

3 rows available after 2 ms, consumed after another 1 ms

  4. In Neo4j Browser, run :sysinfo to check the total store size of neo4jcopy1.

    The reported output for the store size after the compaction is 800.68 KiB, ID Allocation: Node ID 0, Property ID 0.

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).