Storage memory usage

Memgraph’s architecture has been built natively for in-memory data analysis and storage. Being ACID compliant, it ensures consistency and reliability in its core design. However, Memgraph also offers flexibility with it's other storage modes.

Storage modes

Memgraph supports three different storage modes:

• IN_MEMORY_TRANSACTIONAL - the default database storage mode that favors strongly-consistent ACID transactions using WAL files and snapshots, but requires more time and resources during data import and analysis.
• IN_MEMORY_ANALYTICAL - speeds up import and data analysis but offers no ACID guarantees besides manually created snapshots.
• ON_DISK_TRANSACTIONAL - supports ACID properties in the same way as IN_MEMORY_TRANSACTIONAL with the additional ability to store data on disk (HDD or SSD), thus trading performance for lower costs.

Start Memgraph with a specific storage mode

By default, an empty instance will start using in-memory transactional storage mode. To start Memgraph in the ON_DISK_TRANSACTIONAL or IN_MEMORY_ANALYTICAL storage node, change the --storage-mode configuration flag accordingly.

Switch storage modes

You can switch between in-memory modes within a session using the following query:

STORAGE MODE IN_MEMORY_TRANSACTIONAL;

STORAGE MODE IN_MEMORY_ANALYTICAL;

When switching modes, Memgraph will wait until all other transactions are done. If some other transactions are running in your system, you will receive a warning message, so be sure to set the log level to WARNING.

Switching from the in-memory storage mode to the on-disk storage mode is allowed when there is only one active session and the database is empty. As Memgraph Lab uses multiple sessions to run queries in parallel, it is currently impossible to switch to the on-disk storage mode within Memgraph Lab. You can change the storage mode to on-disk transactional using mgconsole, then connect to the instance with Memgraph Lab and query the instance as usual.

To change the storage mode to ON_DISK_TRANSACTIONAL, use the following query:

STORAGE MODE ON_DISK_TRANSACTIONAL;

It is forbidden to change the storage mode from ON_DISK_TRANSACTIONAL to any of the in-memory storage modes while there is data in the database as it might not fit into the RAM. To change the storage mode to any of the in-memory storages, empty the instance and restart it. An empty database will start in the default storage mode (in-memory transactional) or the storage mode defined by the --storage-mode configuration flag.

If you are running the Memgraph Enterprise Edition, you need to have STORAGE_MODE permission to change the storage mode.

By default, an empty instance will start using in-memory transactional storage mode. Non-empty database using in-memory transactional or analytical storage mode will restart in transactional storage mode. This behavior can be changed using the --storage-mode configuration flag. But, regardless of what the flag is set to, a non-empty instance in the on-disk storage mode cannot change storage mode even upon restart.

Check storage mode

You can check the current storage mode using the following query:

SHOW STORAGE INFO;

In-memory transactional storage mode (default)

IN_MEMORY_TRANSACTIONAL storage mode offers all ACID guarantees. WAL files and periodic snapshots are created automatically, and you can also create snapshots manually.

In the IN_MEMORY_TRANSACTIONAL mode, Memgraph creates a Delta object each time data is changed. Deltas are the backbone upon which Memgraph provides atomicity, consistency, isolation, and durability - ACID. By using Deltas, Memgraph creates write-ahead-logs for durability, provides isolation, consistency, and atomicity (by ensuring that everything is executed or nothing is).

Depending on the transaction isolation level, other transactions may see changes from other transactions.

In the transactional storage mode, snapshots are created periodically or manually. They capture the database state and store it on the disk. A snapshot is used to recover the database upon startup (depending on the setting of the configuration flag --data-recovery-on-startup, which defaults to true).

When Memgraph starts creating a periodic snapshot, it is not possible to manually create a snapshot, until the periodic snapshot is created. Snapshot creation requires a transactional view. This can lead to spikes in memory consumption when data is being ingested in parallel to snapshot creation. It is possible to pause the periodic snapshot background job at run-time. This can speed up ingestion time and lower memory usage.

Manual snapshots are created by running the CREATE SNAPSHOT; query.

In-memory analytical storage mode

In the transactional storage mode, Memgraph is fully ACID compliant which could cause memory spikes during data import because each time data is changed Memgraph creates Delta objects to provides atomicity, consistency, isolation, and durability.

But Deltas also require memory (56B per change), especially when there are a lot of changes (for example, during import with the LOAD CSV clause). By switching the storage mode to IN_MEMORY_ANALYTICAL mode disables the creation of Deltas thus drastically speeding up import with lower memory consumption - up to 6 times faster import and less memory consumption.

As Deltas are the foundation for ACID properties, they provide information on what has changed in different concurrent transactions. They also cause conflicts between transactions when multiple transactions change the same graph object. A typical scenario would be adding a relationship to the same node or adding a property to the same node. In the analytical storage mode, Memgraph does not create Deltas, and therefore, there won't be any conflicts between transactions. Adding relationships is thread-safe, which means that multiple transactions can add relationships to the same node without any conflicts. That leads to a very efficient import process in scenarios with a dense graph and supernode.

If you want to enable ACID compliance, you can switch back to IN_MEMORY_TRANSACTIONAL and continue with regular work on the database or you can take advantage of the low memory costs of the analytical mode to run analytical queries that will not change the data, but be aware that no backup is created automatically, and there are no ACID guarantees besides manually created snapshots. There are no WAL files created nor periodic snapshots. Users can create a snapshot manually.

Implications

In the analytical storage mode, there are no ACID guarantees and other transactions can see the changes of ongoing transactions. Also, a transaction can see the changes it is doing. This means that the transactions can be committed in random orders, and the updates to the data, in the end, might not be correct.

In the practical sense, not having ACID properties leads to the following implications:

• Atomicity - if your write transaction fails for any reason, the changes are not rolled back.
• Consistency - database consistency is not guaranteed as transactions may fail and leave the database in an inconsistent state.
• Isolation - if you have a long-running write transaction, other transactions can see the changes of ongoing transactions even if changes are not committed.
• Durability - Memgraph does not create WAL files and periodic snapshots in the analytical storage mode. They need to be created manually.

The absence of isolation guarantees also implies that changes by parallel transactions may affect the correctness of query procedures that read from the graph. The most critical case is that of the procedure returning a graph element (node or relationship), or a container type holding those. If the returned graph element has been deleted by a parallel transaction, the built-in behavior is as follows:

• procedures: skip all records that contain any deleted value
• functions: return a null value

Please note that deleting same part of the graph from parallel transaction will lead to undefined behavior.

Users developing custom query procedures and functions intended to work in the analytical storage mode should use API methods to check if Memgraph is running in a transactional (ACID-compliant) storage mode. If not, the query module APIs let you check whether graph elements (nodes and relationships) are deleted, and whether containers (maps, lists and paths) that may hold graph elements don’t contain any deleted values.

Memgraph uses snapshots and write-ahead logs to ensure data durability and backup. Snapshots capture the database state and store it on the disk. A snapshot is then used to recover the database upon startup (depending on the setting of the configuration flag --data-recovery-on-startup, which defaults to true).

In Memgraph, snapshots are created periodically or manually with CREATE SNAPSHOT; Cypher query.

In the analytical storage mode, WAL files and periodic snapshots are not created.

Before switching back to the in-memory transactional storage mode create a snapshot manually. In the in-memory analytical storage mode, Memgraph guarantees that creating a snapshot is the only transaction present in the system, and all the other transactions will wait until the snapshot is created to ensure its validity. Once Memgraph switches to the in-memory transactional mode, it will restore data from the snapshot file and create a WAL for all new updates, if not otherwise instructed by the config file.

These are some of the implications of not having ACID properties. But if you do not have write-heavy workload, and you want to run analytical queries that will not change the data, you can take advantage of the low memory costs of the analytical mode.

At the moment, the in-memory analytical storage mode doesn't support replication and high availability.

On-disk transactional storage mode

In the on-disk transactional storage mode, disk is used as a physical storage which allows you to save more data than the capacity of your RAM. This helps keep the hardware costs to a minimum, but you should except slower performance when compared to the in-memory transactional storage mode. Keep in mind that while executing queries, all the graph objects used in the transactions still need to be able to fit in the RAM, or Memgraph will throw an exception.

Memgraph uses RocksDB as a background storage to serialize nodes and relationships into a key-value format. The used architecture is also known as "larger than memory" as it enables in-memory databases to save more data than the main memory can hold, without the performance overhead caused by the buffer pool.

The imported data is residing on the disk, while the main memory contains two caches, one executing operations on main RocksDB instance and the other for operations that require indexes. In both cases, Memgraph's custom SkipList cache is used, which allows a multithreaded read-write access pattern.

Implications

Concurrent execution of transactions is supported differently for on-disk storage than for in-memory. The in-memory storage mode relies on Delta objects which store the exact versions of data at the specific moment in time. Therefore, the in-memory storage mode uses a pessimistic approach and immediately checks whether there is a conflict between two transactions.

In the on-disk storage mode, the cache is used per transaction. This significantly simplifies object management since there is no need to question certain object's validity, but it also requires the optimistic approach for conflict resolution between transactions.

In the on-disk storage mode, the conflict is checked at the transaction's commit time with the help of RocksDB's transaction support. This also implies that Deltas are cleared after each transaction, which can optimize memory usage during execution. Deltas are still used to fully support Cypher's semantic of the write queries. The design of the on-disk storage also simplifies the process of garbage collection, since all the data is on disk.

The on-disk storage mode supports only snapshot isolation level. Mostly because it's the Memgraph viewpoint that snapshot isolation should be the default isolation level for most applications relying on databases. But the snapshot isolation level also simplifies the query's execution flow since no data is transferred to the disk until the commit of the transaction.

Label and label-property indexes are stored in separate RocksDB instances as key-value pairs so that the access to the data is faster. Whenever the indexed node is accessed, it's stored into a separate in-memory cache to maximize the reading speed.

When it comes to constraints, the existence constraints don't use context from the disk since the validity of nodes can be checked by looking only at this single node. On the other side, uniqueness constraints require a different approach. For a node to be valid, the engine needs to iterate through all other nodes under constraint and check whether a conflict exists. To speed up this iteration process, nodes under constraint are stored into a separate RocksDB instance to eliminate the cost of iterating over nodes which are not under constraint.

In the on-disk storage mode, durability is supported by RocksDB since it keeps its own WAL (opens in a new tab) files. Memgraph persists the metadata used in the implementation of the on-disk storage.

If the workload is larger than memory, a single transaction must fit into the memory. A memory tracker tracks all allocations happening throughout the transaction's lifetime. Disk space also has to be carefully managed. Since the timestamp is serialized together with the raw node and relationship data, the engine needs to ensure that when the new version of the same node is stored, the old one is deleted.

At the moment, the on-disk storage doesn't support replication and high availability.

Data formats

Below is the format in which data is serialized to the disk.

Vertex format for main disk storage: Key - label1, label2, ... | vertex gid | commit_timestamp Value - properties

Edge format for the main disk storage: Key - edge gid Value - src_vertex_gid | dst_vertex_gid | edge_type | properties

To achieve fast expansions, we also store connectivity index for outcoming and incoming edges on disk: Outcoming connectivity index: Key - src_vertex_gid Value - out_edge_gid1, out_edge_gid2, ...

Incoming connectivity index: Key - dst_vertex_gid Value - in_edge_gid1, in_edge_gid2, ...

Format for label index on disk:

Key - indexing label | vertex gid | commit_timestamp

Value - label1_id, label2_id, ... | properties

Value does not contain indexing label.

Format for label-property index on disk:

Key - indexing label | indexing property | vertex gid | commit_timestamp

Value - label1_id, label2_id, ... | properties

Value does not contain indexing label.

Edge import mode

Memgraph on-disk storage supports the EDGE IMPORT MODE to import relationships with high throughput. When the database is in this mode, only relationships can be created and modified. Any operation on nodes other than read are prohibited and will throw an exception.

To enable the mode run:

EDGE IMPORT MODE ACTIVE;

To disable the mode run:

EDGE IMPORT MODE INACTIVE;

Import data using `LOAD CSV` clause

When importing data using a CSV file, import nodes and relationships seperately. Currently, to import relationships as fast as possible, don't create indexes.

Here is an example how to import data using the LOAD CSV clause.

First, import the nodes:

LOAD CSV FROM "/path-to/people_nodes.csv" WITH HEADER AS row
CREATE (p:Person {id: row.id, name: row.name});

Activate the EDGE IMPORT MODE:

EDGE IMPORT MODE ACTIVE;

Import the relationships:

LOAD CSV FROM "/path-to/people_relationships.csv" WITH HEADER AS row
MATCH (p1:Person {id: row.id_from}), (p2:Person {id: row.id_to})
CREATE (p1)-[:IS_FRIENDS_WITH]->(p2);

Deactivate the EDGE IMPORT MODE:

EDGE IMPORT MODE INACTIVE;

Import data using Cypher queries

For files containing Cypher queries (.cypherql), create all nodes, then activate the EDGE IMPORT MODE and create relationships. Indexes can be created before or after activating the EDGE IMPORT MODE. It's best to create a label-property index on node properties used to match nodes that will be connected with a relationship.

Here as an exampe of a CYPHERL file:

CREATE (n:User {id: 1});
CREATE (n:User {id: 2});
CREATE (n:User {id: 3});
CREATE (n:User {id: 4});
EDGE IMPORT MODE ACTIVE;
CREATE INDEX ON :User(id);
MATCH (n:User {id: 1}), (m:User {id: 2}) CREATE (n)-[r:FRIENDS {id: 1}]->(m);
MATCH (n:User {id: 3}), (m:User {id: 4}) CREATE (n)-[r:FRIENDS {id: 2}]->(m);
EDGE IMPORT MODE INACTIVE;

Calculate storage memory usage

Estimating Memgraph's storage memory usage is not entirely straightforward because it depends on a lot of variables, but it is possible to do so quite accurately.

If you want to estimate IN_MEMORY_TRASNACTIONAL storage mode memory usage in the in-memory transactional storage mode, use the following formula:

$\texttt{StorageRAMUsage} = \texttt{NumberOfVertices} \times 212\text{B} + \texttt{NumberOfEdges} \times 162\text{B}$

Let's test this formula on the Marvel Comic Universe Social Network dataset (opens in a new tab), which is also available as a dataset inside Memgraph Lab and contains 21,723 vertices and 682,943 edges.

According to the formula, storage memory usage should be:

$\begin{aligned} \texttt{StorageRAMUsage} &= 21,723 \times 260\text{B} + 682,943 \times 180\text{B} \\ &= 5,647,980\text{B} + 122,929,740\text{B}\\ &= 128,577,720\text{B} \approx 125\text{MB} \end{aligned}$

Now, let's run an empty Memgraph instance on a x86 Ubuntu. It consumes ~75MB of RAM due to baseline runtime overhead. Once the dataset is loaded, RAM usage rises up to ~260MB. Memory usage primarily consists of storage and query execution memory usage. After executing FREE MEMORY query to force the cleanup of query execution, the RAM usage drops to ~200MB. If the baseline runtime overhead of 75MB is subtracted from the total memory usage of the dataset, which is 200MB, and storage memory usage comes up to ~125MB, which shows that the formula is correct.

The calculation in detail

Let's dive deeper into the IN_MEMORY_TRASNACTIONAL storage mode memory usage values. Because Memgraph works on the x86 architecture, calculations are based on the x86 Linux memory usage.

Each Vertex and Edge object has a pointer to a Delta object. The Delta object stores all changes on a certain Vertex or Edge and that's why Vertex and Edge memory usage will be increased by the memory of the Delta objects they are pointing to. If there are few updates, there are also few Delta objects because the latest data is stored in the object. But, if the database has a lot of concurrent operations, many Delta objects will be created. Of course, the Delta objects will be kept in memory as long as needed, and a bit more, because of the internal GC inefficiencies.

Delta memory layout

Each Delta object has a least 56B.

Vertex memory layout

Each Vertex object has at least 88B + 56B for the Delta object, in total, a minimum of 144B.

Each additional label takes 4B.

Keep in mind that three labels take as much space as four labels, and five to seven labels take as much space as eight labels, etc., due to the dynamic memory allocation.

Edge memory layout

Each Edge object has at least 40B + 56B for the Delta object, in total, a minimum of 96B.

SkipList memory layout

Each object (Vertex, Edge) is placed inside a data structure called a SkipList. The SkipList has an additional overhead in terms of SkipListNode structure and next_pointers. Each SkipListNode has an additional 8B element overhead and another 8B for each of the next_pointers.

It is impossible to know the exact number of next_pointers upfront, and consequently the total size, but it's never more than double the number of objects because the number of pointers is generated by binomial distribution (take a look at the source code (opens in a new tab) for details).

Index memory layout

Each LabelIndex::Entry object has exactly 16B.

Depending on the actual value stored, each LabelPropertyIndex::Entry has at least 72B.

Objects of both types are placed into the SkipList.

Each index object in total

• SkipListNode<LabelIndex::Entry> object has 24B.
• SkipListNode<LabelPropertyIndex::Entry> has at least 80B.
• Each SkipListNode has an additional 16B because of the next_pointers.

Properties

All properties use 1B for metadata - type, size of property ID and the size of payload in the case of NULL and BOOLEAN values, or size of payload size indicator for other types (how big is the stored value, for example, integers can be 1B, 2B 4B or 8b depending on their value).

Then they take up another byte for storing property ID, which means each property takes up at least 2B. After those 2B, some properties (for example, STRING values) store addition metadata. And lastly, all properties store the value. So the layout of each property is:

$\texttt{propertySize} = \texttt{basicMetadata} + \texttt{propertyID} + [\texttt{additionalMetadata}] + \texttt{value}.$

Marvel dataset use case

The Marvel dataset consists of Hero, Comic and ComicSeries labels, which are indexed. There are also three label-property indexes - on the name property of Hero and Comic vertices, and on the title property of ComicSeries vertices. The ComicSeries vertices also have the publishYear property.

There are 6487 Hero and 12,661 Comic vertices with the property name. That's 19,148 vertices in total. To calculate how much storage those vertices and properties occupy, we are going to use the following formula:

$\texttt{NumberOfVertices} \times (\texttt{Vertex} + \texttt{properties} + \texttt{SkipListNode} + \texttt{next\_pointers} + \texttt{Delta}).$

Let's assume the name on average has $3\text{B}+10\text{B} = 13\text{B}$ (each name is on average 10 characters long). One the average values are included, the calculation is:

$19,148 \times (88\text{B} + 13\text{B} + 16\text{B} + 16\text{B} + 56\text{B}) = 19,148 \times 189\text{B} = 3,618,972\text{B}.$

The remaining 2,584 vertices are the ComicSeries vertices with the title and publishYear properties. Let's assume that the title property is approximately the same length as the name property. The publishYear property is a list of integers. The average length of the publishYear list is 2.17, let's round it up to 3 elements. Since the year is an integer, 2B for each integer will be more than enough, plus the 2B for the metadata. Therefore, each list occupies $3 \times 2\text{B} \times 2\text{B} = 12\text{B}$. Using the same formula as above, but being careful to include both title and publishYear properties, the calculation is:

$2584 \times (88\text{B} + 13\text{B} + 12\text{B} + 16\text{B} + 16\text{B} + 56\text{B}) = 2584 \times 201\text{B} = 519,384\text{B}.$

In total, $4,138,356\text{B}$ to store vertices.

The edges don't have any properties on them, so the formula is as follows:

$\texttt{NumberOfEdges} \times (\texttt{Edge} + \texttt{SkipListNode} + \texttt{next\_pointers} + \texttt{Delta}).$

There are 682,943 edges in the Marvel dataset. Hence, we have:

$682,943 \times (40\text{B}+16\text{B}+16\text{B}+56\text{B}) = 682,943 \times 128\text{B} = 87,416,704\text{B}.$

Next, Hero, Comic and ComicSeries labels have label indexes. To calculate how much space they take up, use the following formula:

$\texttt{NumberOfLabelIndices} \times \texttt{NumberOfVertices} \times (\texttt{SkipListNode<LabelIndex::Entry>} + \texttt{next\_pointers}).$

Since there are three label indexes, we have the following calculation:

$3 \times 21,723 \times (24\text{B}+16\text{B}) = 65,169 \times 40\text{B} = 2,606,760\text{B}.$

For label-property index, labeled property needs to be taken into account. Property name is indexed on Hero and Comic vertices, while property title is indexed on ComicSeries vertices. We already assumed that the title property is approximately the same length as the name property.

Here is the formula:

$\texttt{NumberOfLabelPropertyIndices} \times \texttt{NumberOfVertices} \times (\texttt{SkipListNode<LabelIndex::Entry>} + \texttt{property} + \texttt{next\_pointers}).$

When the appropriate values are included, the calculation is:

$3 \times 21,723 \times (80\text{B}+13\text{B}+16\text{B})= 65,169 \times 109\text{B} = 7,103,421\text{B}.$

Now let's sum up everything we calculated:

$4,138,356\text{B} + 87,416,704\text{B} + 2,606,760\text{B} + 7,103,421\text{B} = 101,265,241 \text{B} \approx 97\text{MB}.$

Bear in mind the number can vary because objects can have higher overhead due to the additional data.

Control memory usage

In Memgraph, you can control memory usage by limiting, inspecting and deallocating memory.

You can control the memory usage of:

• a whole instance by setting the --memory-limit within the configuration file
• a query by including the QUERY MEMORY clause at the end of a query
• a procedure by including the PROCEDURE MEMORY clause

Control instance memory usage

By setting the --memory-limit flag in the configuration file, you can set the maximum amount of memory (in MiB) that a Memgraph instance can allocate during its runtime. If the memory limit is exceeded, only the queries that don't require additional memory are allowed. If the memory limit is exceeded while a query is running, the query is aborted and its transaction becomes invalid.

If the flag is set to 0, it will use the default values. Default values are:

• 90% of the total memory if the system doesn't have swap memory.
• 100% of the total memory if the system has swap memory.

Control query memory usage

Query execution also uses up RAM. All allocations inside query are counted from intermediary results to allocations made while creating nodes and relationships. In some cases, intermediate results are aggregated to return valid query results and the query execution memory can end up using a large amount of RAM. Keep in mind that query execution memory monotonically grows in size during the execution, and it's freed once the query execution is done. A general rule of thumb is to have double the RAM than what the actual dataset is occupying.

Each Cypher query can include the following clause at the end:

QUERY MEMORY ( UNLIMITED | LIMIT num (KB | MB) )

If you use the LIMIT option, you have to specify the amount of memory a query can allocate for its execution. You can use this clause in a query only once at the end of the query. The limit is applied to the entire query.

Examples:

MATCH (n) RETURN (n) QUERY MEMORY LIMIT 10 KB;

MATCH (n) RETURN (n) QUERY MEMORY UNLIMITED;

Control procedure memory usage

Each procedure call can contain the following clause:

PROCEDURE MEMORY ( UNLIMITED | LIMIT num ( KB | MB) )

If you use the LIMIT option, you can specify the amount of memory that the called procedure can allocate for its execution. If you use the UNLIMITED option, no memory restrictions will be imposed when the procedure is called. If you don't specify the clause, the memory limit is set to a default value of 100 MB.

One procedure call can have only one PROCEDURE MEMORY clause at the end of the call. If a query contains multiple procedure calls, each call can have its own limit specification.

Examples:

CALL example.procedure(arg1, arg2, ...) PROCEDURE MEMORY LIMIT 100 KB YIELD result;

CALL example.procedure(arg1, arg2, ...) PROCEDURE MEMORY LIMIT 100 MB YIELD result;

CALL example.procedure(arg1, arg2, ...) PROCEDURE MEMORY UNLIMITED YIELD result;

Inspect memory usage

Run the following query to get information about the currently used storage mode, memory usage, disk usage, allocated memory and the allocation limit:

SHOW STORAGE INFO;

+--------------------------------+--------------------------------+
| storage info                   | value                          |
+--------------------------------+--------------------------------+
| "name"                         | "memgraph"                     |
| "vertex_count"                 | 0                              |
| "edge_count"                   | 0                              |
| "average_degree"               | 0                              |
| "vm_max_map_count"             | 453125                         |
| "memory_res"                   | "43.16MiB"                     |
| "peak_memory_res"              | "43.16MiB"                     |
| "unreleased_delta_objects"     | 0                              |
| "disk_usage"                   | "104.46KiB"                    |
| "memory_tracked"               | "8.52MiB"                      |
| "allocation_limit"             | "58.55GiB"                     |
| "global_isolation_level"       | "SNAPSHOT_ISOLATION"           |
| "session_isolation_level"      | ""                             |
| "next_session_isolation_level" | ""                             |
| "storage_mode"                 | "IN_MEMORY_TRANSACTIONAL"      |
+--------------------------------+--------------------------------+

Find out more about SHOW STORAGE INFO query on Server stats.

Reduce memory usage

Here are several tips how you can reduce memory usage and increase scalability:

1. Consider removing unused label indexes by executing DROP INDEX ON :Label;
2. Consider removing unused label-property indexes by executing DROP INDEX ON :Label(property);
3. If you don't have properties on relationships, disable them in the configuration file by setting the --storage-properties-on-edges flag to false. This can significantly reduce memory usage because effectively Edge objects will not be created, and all information will be inlined under Vertex objects. You can disable properties on relationships with a non-empty database, if the relationships are without properties.
4. Inspect query plans and optimize them

Property compression

Another way to reduce memory usage is through property compression. This feature compresses the entire PropertyStore object, meaning it compresses all compressible properties in the database. When property store compression is enabled, it checks if the PropertyStore of a vertex can be compressed, and if it can, it compresses it using the well-known compression library zlib.

To enable compression, set the --storage-property-store-compression-enabled flag to true. The default value of this flag is false.

Additionally, users can control the compression level with the --storage-property-store-compression-level flag. The default value is mid, but two other values, low and high, are also available. The low setting compresses properties minimally, reducing memory usage slightly, while the high setting compresses properties the most, trying to maximize memory savings. However, there’s a tradeoff to consider: higher compression levels can reduce performance. Therefore, users focused on maintaining optimal performance may prefer not to set it to high.

Deallocating memory

Memgraph has a garbage collector that deallocates unused objects, thus freeing the memory. The rate of the garbage collection in seconds can be specified in the configuration file by setting the --storage-gc-cycle-sec.

You can free up memory by using the following query:

FREE MEMORY;

This query tries to clean up as much unused memory as possible without affecting currently running transactions.

Virtual memory

Memgraph uses a jemalloc (opens in a new tab) allocator to handle all allocations and to track allocations happening in the system.

The default operating system limits on mmap counts are likely to be too low, which may result in out-of-memory exceptions.

To find out the current mmap count of a running Memgraph process use the following command, where $PID is the process ID of the running Memgraph process:

wc -l /proc/$PID/maps

On Linux, you can increase the limits by running the following command as root:

sysctl -w vm.max_map_count=262144

To increase this value permanently, update the vm.max_map_count setting in /etc/sysctl.conf. To verify, reboot the system and run sysctl vm.max_map_count.

The RPM and Debian packages will configure this setting automatically so you don't need to do any more configurations.