# Frequently asked questions ## Memgraph 101 ### What is Memgraph? Memgraph is an **open-source in-memory graph database** built for teams that expect highly performant, advanced analytical insights - as compatible with your current infrastructure as Neo4j (but up to 8x faster). Memgraph is powered by a query engine built in C/C++ to handle real-time use cases at an enterprise scale. Memgraph supports **strongly consistent ACID transactions** and uses the standardized **Cypher query language** over Bolt protocol for structuring, manipulating, and exploring data. ### What are the benefits of being an in-memory graph database? When data is stored on disk, the computer has to physically read it from the disk and transfer it to the RAM before it can be processed. This process is relatively slow because it involves several physical processes, such as seeking the right location on the disk and waiting for the data to be read. Writing the data is also much slower for the same reasons. Storing data in the computer's RAM eliminates the need for these physical processes, and data can be accessed and added almost instantly. Therefore, in-memory graph databases are ideal for applications requiring fast data processing, real-time analytics, and quick response times. ### What use cases is Memgraph best suited for? Memgraph is best suited for use cases with complex data relationships that require real-time processing and high scalability. ### How do I know my use case has complex data relationships? In relational databases, complex data relationships arise when data from different tables is related or somehow interconnected. Because data is spread across multiple tables, querying it requires hopping from one table to another and joining it with slow and resource-intensive join operations. The complexity of join operations can increase exponentially as the number of tables increases and as the links between various tables are no longer neatly structured following a clearly set pattern. It is no longer sufficient to join just two or three tables but hop through more than seven tables to find the correct link between the data and gain valuable analytics. Examples of complex data are deep hierarchical relationships between data, such as parent-child relationships or many-to-many relationships between different tables. ### How does Memgraph compare to other graph databases regarding performance? Memgraph is designed to be a high-performance graph database, and it typically outperforms many other graph databases in terms of speed and scalability. Key factors contributing to Memgraph's performance are its in-memory architecture and a performant query engine written in C++. Memgraph also offers a variety of tools and features to help optimize query performance, including label and label-property indexes and a custom visualization library. Check our [benchmark](https://memgraph.com/benchgraph/) comparing Memgraph and Neo4j. ### Is Memgraph a distributed database? At the moment, Memgraph supports horizontal scaling of read queries by using [replication](https://memgraph.com/docs/clustering/replication). Horizontal scaling of write queries requires sharding of data which Memgraph doesn’t support because once data is sharded in a cluster it becomes a complex system that is less performant, error-prone and requires advanced management, thus requiring more infrastructural and DevOps resources. Single-machine systems are easier to manage, and consequently, the cost of maintenance and manpower is lower. ### How does Memgraph ensures persistency and durability? Although Memgraph is an in-memory database, the data is persistent and durable. This means data will not be lost upon reboot. Memgraph uses two mechanisms to ensure the [durability](https://memgraph.com/docs/configuration/data-durability-and-backup) of stored data and make disaster recovery possible: * write-ahead logging (WAL) * periodic snapshot creation Each database modification is recorded in a log file before being written to the database. Therefore, the log file contains all steps needed to reconstruct the DB’s most recent state. Memgraph also periodically takes snapshots during runtime to write the entire data storage to the drive. On startup, the database state is recovered from the most recent snapshot file. The timestamp of the snapshot is compared with the latest update recorded in the WAL file and, if the snapshot is less recent, the state of the DB will be completely recovered using the WAL file. ### How does Memgraph ensure high availability? Memgraph ensures high availability by using [replication](https://memgraph.com/docs/clustering/replication). Replication involves replicating data from one MAIN instance to one or several REPLICA instances. If a MAIN instance fails, another REPLICA instance can be upgraded and serve as the MAIN instance, thus ensuring continuous data availability. ### How many cores does Memgraph utilizes? Memgraph is designed to utilize all available CPU cores on a machine to process queries and perform other operations in parallel, significantly improving performance and reducing query response times. ### What are the minimum and recommended requirements to run a on-premise instance? To run Memgraph on-premise, you require an Intel Xeon, AMD Opteron/Epyc, ARM machines, Apple M1, Amazon Graviton server or desktop processor, at least 1 GB of RAM and disk and at least 1 vCPU. We recommend using a server processor, at least 16 GB of ECC RAM, the same amount of disk storage and at least 8 vCPUs or 4 physical cores. ### How much RAM do I need for my graph? We recommend twice as many GB of RAM as the data size. If you have 8 GB of data, we recommend having at least 16 GB of RAM. Of course, the actual memory needs depend on the complexity of executed queries and the number of concurrent users/requests. The more graph objects query needs to return as a result, the more RAM will be required. The recommendation complicates under environments such as k8s because there is an additional overhead to run the engine. In addition, each cloud provider has a different configuration so that also has to be take into account. E.g. [here is the link on how k8s is using resources under Azure](https://learn.microsoft.com/en-us/azure/aks/node-resource-reservations). Please make sure you understand the underlying infrastructure and reserve enough resources for Memgraph to operate. To calculate the Memgraph RAM instance requirements based on your data, check out [how Memgraph uses memory](https://memgraph.com/docs/fundamentals/storage-memory-usage) or use the [Storage memory calculator](https://memgraph.com/storage-calculator). ### Are there any graph size limits? Memgraph vertically scales effortlessly up to 1B nodes and 10B edges. The only limit is the size of your RAM. We recommend twice as many GB of RAM as the data size. If you have 8 GB of data, we recommend having at least 16 GB of RAM. Of course, the actual memory needs depend on the complexity of executed queries. The more graph objects query needs to return as a result, the more RAM will be required. ### What is the difference between Memgraph and Memgraph Platform? There are three official Docker images for Memgraph: - `memgraph/memgraph` - the most basic MemgraphDB instance used to run a database instance and a command-line interface. - `memgraph/lab` - the image contains a web interface of [Memgraph Lab](https://memgraph.com/docs/data-visualization), a visualization software that helps you explore data stored in Memgraph - `memgraph/memgraph-mage` - the image contains a MemgraphDB instance and the command-line interface together with all the newest graph algorithms and utility procedures. The MAGE graph algorithm library also includes [NVIDIA cuGraph](https://github.com/rapidsai/cugraph) GPU-powered graph algorithms. To use them, you need a specific kind of `memgraph-mage` image, so check the [documentation](https://memgraph.com/docs/advanced-algorithms/install-mage) or [DockerHub](https://hub.docker.com/r/memgraph/memgraph-mage/tags?page=1&name=cugraph) for tags. The easiest way to install Memgraph is to run [Memgraph Platform](https://memgraph.com/docs/getting-started) which will run `memgraph/memgraph-mage` and `memgraph/lab` Docker images thus installing all the tools in the Memgraph ecosystem. ### Do I need to define a schema before importing data? It is not necessary to define any data schema to import data. Data will be imported into the database regardless of the number of properties and their types. You can enforce property [uniqueness](https://memgraph.com/docs/fundamentals/constraints) and [existence](https://memgraph.com/docs/fundamentals/constraints). ### Are there any educational materials available? You can try running queries on preloaded datasets in [Memgraph Playground](https://playground.memgraph.com/). If you need help with Cypher queries, check out the [querying manual](https://memgraph.com/docs/querying). Check out the [Resource hub](https://memgraph.com/resource-hub) for data modeling and Cypher e-mail courses, whitepapers, [blogs](https://memgraph.com/blog) or watch one of our [webinars](https://memgraph.com/webinars). For all the other questions and help, feel free to join our community on [Discord](https://discord.com/invite/memgraph). ### Can I try out Memgraph Enterprise before making a decision? Yes, Memgraph offers a free 30-day Memgraph Enterprise Trial. Send a request via [the form](https://memgraph.com/enterprise-trial). ### Does Memgraph offer professional services such as data modelling, development, integration and similar? It depends on the scope of the project and the requirements. [Contact us](https://memgraph.com/enterprise-trial) for more information. ## Memgraph database ### What is the fastest way to import data into Memgraph? Currently, the fastest way to import data is from a Parquet file with a [LOAD PARQUET clause](https://memgraph.com/docs/data-migration/parquet). Check out the [best practices for importing data](https://memgraph.com/docs/data-migration/best-practices). [Other import methods](https://memgraph.com/docs/data-migration) include importing data from CSV, JSON, JSONL and CYPHERL files, migrating from relational databases, or connecting to a data stream. ### How to import data from MySQL or PostgreSQL? You can migrate from [MySQL](https://memgraph.com/docs/data-migration/migrate-from-rdbms) or [PostgreSQL](https://memgraph.com/docs/data-migration/migrate-from-rdbms) using [CSV files](https://memgraph.com/docs/data-migration/csv). ### What file formats does Memgraph support for import? You can import data from [CSV](https://memgraph.com/docs/data-migration/csv), [PARQUET](https://memgraph.com/docs/data-migration/parquet) [JSON and JSONL](https://memgraph.com/docs/data-migration/json) or [CYPHERL](https://memgraph.com/docs/data-migration/cypherl) files. CSV files can be imported in on-premise instances using the [LOAD CSV clause](https://memgraph.com/docs/data-migration/csv), PARQUET files can be imported using the [LOAD PARQUET](https://memgraph.com/docs/data-migration/parquet) and JSON(L) files can be imported using a [json_util](https://memgraph.com/docs/advanced-algorithms/available-algorithms/json_util) module from the MAGE library. On a Cloud instance, data from CSV and JSON files can be imported only from a remote address. CYPHERL file contains Cypher queries necessary for creating nodes and relationships. ### What data formats does Memgraph support for export? You can export data to JSON or CYPHERL files. Query results can be exported to a CSV file. Data can be exported to a JSON file from on-premise instances using an [export_util](https://memgraph.com/docs/advanced-algorithms/available-algorithms/export_util) module from the MAGE library. The same module can be used to export query results to a CSV file. CYPHERL file contains Cypher queries necessary for creating nodes and relationships and you can export files via Memgraph Lab. ### Can Memgraph database ingest streaming data? Yes, you can [connect your instance](https://memgraph.com/docs/data-streams) to Kafka, Redpanda or Pulsar streams and ingest data. You will need to write a transformation module that will instruct Memgraph on how to transform the incoming messages and consume them correctly. ### Is data automatically indexed during import? No, data is not automatically indexed during import. It would be best to [create label or label-property indexes](https://memgraph.com/docs/fundamentals/indexes) manually, preferably before importing relationships to speed up relationship import as well. ### What languages can be used to communicate with the database? At the moment, you can [connect to a Memgraph instance](https://memgraph.com/docs/client-libraries) using the Bolt protocol and query the database using C#, C/C++, Go, Haskell, Java, JavaScript, Node.js, PHP, Python, Ruby, and Rust. ### Can I create logically separated graphs within the same database instance? You can create logically separated graphs within the same instance by [using different labels](https://memgraph.com/docs/querying/read-and-modify-data). Each node can have multiple labels and [the cost of labels is 8B per label](https://memgraph.com/docs/fundamentals/storage-memory-usage) (but the memory is allocated dynamically, so 3 labels take up as much memory as 4, and 5-7 labels take as much space as 8, etc.) You can use the same technique to save multilayer networks. Memgraph Enterprise supports [multi-tenant architecture](https://memgraph.com/docs/database-management/multi-tenancy). ### Can I run MAGE modules and algorithms on just a part of the graph/subgraph? You can [run MAGE modules and algorithms on subgraphs](https://memgraph.com/docs/advanced-algorithms/run-algorithms) by using the [project() function](https://memgraph.com/docs/querying/functions). ### How can I visualize query results? You can use Memgraph Lab, a visual user interface that enables you to: * visualize graph data using [the Orb library](https://github.com/memgraph/orb) * write and execute Cypher queries * import and export data * view and optimize query performance * develop query modules in Python * manage connections to streams. ### How can I speed up query execution? Indexing is not performed automatically upon data import, so try creating an [index](https://memgraph.com/docs/fundamentals/indexes) on a label or label-property. Creating an index on a label-property will not create an index on a label the property corresponds to, only on the property. Also, enforcing a uniqueness constraint on a property will not create an index on that property. Also, there are several features you can employ to [speed up query execution](https://memgraph.com/docs/querying/best-practices), such as analysing the graph and hinting at indexes. ### Does replication affect performance? Replication can impact performance in several ways. When using SYNC replica, the replication is done at the time of commit. This means that each commit takes longer, as the data needs to be replicated before the commit has been finalized. A single replica can receive only one package at a time, meaning that replication is a bottleneck for highly parallel workflows. ### How can I check storage information? You can check storage information by running [SHOW STORAGE INFO;](https://memgraph.com/docs/database-management/server-stats), which returns instance-level (process-wide) metrics such as resident and tracked memory, disk usage and the configured memory limits. To get per-database metrics such as the number of stored nodes and relationships, run `SHOW STORAGE INFO ON CURRENT DATABASE` (or `SHOW STORAGE INFO ON DATABASE `). ### Where does Memgraph save or preview logs? By default, Memgraph saves the log at `/var/log/memgraph/memgraph.log`. Check the [documentation about accessing logs](https://memgraph.com/docs/configuration/logs#access-logs). You can check the logs using Memgraph Lab (the visual interface). Memgraph Lab listens to logs on the 7444 port. You can also use this web socket port 7444 and listen to the logs from your custom system. Log level and location can be modified using [configuration flags](https://memgraph.com/docs/database-management/configuration). ### Do I need to know Cypher to query the database? You don't need to know Cypher to query the database. You can use [GQLAlchemy](https://github.com/memgraph/gqlalchemy), an Object Graph Mapper (OGM). OGM provides a developer-friendly workflow for writing object-oriented notation to communicate to a graph database. Instead of writing Cypher queries, you can write Python code, which the OGM will automatically translate into Cypher queries. It supports both Memgraph and Neo4j. ### Where can I find the documentation for older releases of Memgraph? For easy browsing of documentation for versions between Memgraph 2.0 and 2.10.1, you can use [the documentation archive](https://memgraph-archive-docs.netlify.app/). For comprehensive documentation spanning versions from 1.3.0 to 2.10.1, refer to the [archived GitHub repository](https://github.com/memgraph/docs/tree/master/memgraph_versioned_docs). ### Is Memgraph available on AWS? Yes, Memgraph is available for access through the [AWS Marketplace](https://aws.amazon.com/marketplace/seller-profile?id=59fd38c6-06e9-447f-909d-b7c6beedb44c). ## Cypher ### Are there any differences in Cypher implementation between Memgraph and Neo4j? Although we tried to implement openCypher query language as closely to the language reference as possible, we made some changes that can enhance the user experience. You can find the differences listed in the [Cypher manual](https://memgraph.com/docs/querying/differences-in-cypher-implementations). ### Can I expand Cypher query language with custom procedures? Yes, you can expand the Cypher query language with custom procedures grouped in query modules. Modules can be written in C/C++ and Python (which also has a mock API). For more details, check out the documentation on [query modules](https://memgraph.com/docs/advanced-algorithms). ### Are there plans to add support for Graph Query Language (GQL)? While GQL is not yet on our short or mid-term roadmap, we are actively following the standard and have a long-term vision to support it. Fortunately, its language constructs are quite similar to Cypher, making future adoption more seamless. ## MAGE graph library ### What is MAGE? [**Memgraph Advanced Graph Extensions (MAGE)**](https://memgraph.com/docs/advanced-algorithms) is an open-source repository that contains graph algorithms and utility modules. It encourages developers to share innovative and useful [query modules](https://memgraph.com/docs/custom-query-modules) (custom Cypher procedures) the whole community can benefit from. It corresponds to APOC in Neo4j, except it's free and open source. The MAGE library also includes dynamic algorithms specially designed for analyzing real-time data, NetworkX and igraph integrations, Elasticsearch synchronization module and NVIDIA GPU-powered algorithms. Check [the full list of modules](https://memgraph.com/docs/advanced-algorithms/available-algorithms), and if there is a specific procedure you can't find in the MAGE library which you would like to use, please [let us know](https://memgraph.com/docs/help-center). ### What are query modules? [Query modules](https://memgraph.com/docs/custom-query-modules) are collections of custom Cypher procedures that extend the basic functionalities of the Cypher query language. Each query module consists of procedures connected by a common theme (for example, community detection). [MAGE graph library](https://memgraph.com/docs/advanced-algorithms/) gathers a number of implemented graph algorithms and utility modules. Still, if you need a specific procedure unavailable in MAGE, you can [implement it using Python or C/C++ API](https://memgraph.com/docs/custom-query-modules) and [contribute to the library](https://memgraph.com/docs/custom-query-modules/contributing) or [contact us](https://memgraph.com/docs/help-center). ## Memgraph Lab visual user interface ### What is Memgraph Lab? [Memgraph Lab](https://memgraph.com/docs/data-visualization) is a lightweight and intuitive visual user interface that enables you to: - write and execute Cypher queries and algorithms - visualize graph data using [the Orb library](https://github.com/memgraph/orb) - import and export data - generate data schema - view and optimize query performance - develop custom procedures in Python - manage stream connections. ### Can I customize the visual appearance of my graph results? Yes, you can customize the visual appearance of your graph results by using [the Graph Style Script language](https://memgraph.com/docs/data-visualization/graph-style-script). You can add images to nodes, change their shape, size and color. Change the line appearance of relationships and their thickness. For a complete list of available features, consult [the GSS reference guide](https://memgraph.com/docs/data-visualization/graph-style-script). ## Memgraph Cloud ### What is the pricing? You can estimate the cost of Memgraph Cloud's service by selecting your cloud region and instance size with our [Cost Calculator](https://cloud.memgraph.com/pricing), or you can check them out at [Project rates](https://memgraph.com/docs/getting-started/install-memgraph/memgraph-cloud). ### How can I redeem a coupon that I got for Memgraph Cloud? First, you need to [add a credit card](https://memgraph.com/docs/getting-started/install-memgraph/memgraph-cloud) to your account. Then you can [redeem](https://memgraph.com/docs/getting-started/install-memgraph/memgraph-cloud) the coupon. ### What will happen to my instance after the free trial? Instance will be stopped for next 7 days. If you wish to continue, [add a payment method](https://memgraph.com/docs/getting-started/install-memgraph/memgraph-cloud). ### Why can't I create more than 3 projects and 5 snapshots? That is the initial limit for new users. If you want to create more projects, [let us know](https://memgraph.com/docs/help-center). ### Is it possible to upgrade a project to use more RAM? Yes, it is. You can find detailed instructions in [Memgraph Cloud documentation](https://memgraph.com/docs/getting-started/install-memgraph/memgraph-cloud). ### I've created a project with 2GB RAM, but Memgraph Labs says there is only 1.54GB available. Why is that so? A par of RAM is allocated to the operating system and other services. They usually take 13-15% of the total RAM. Approximate free RAM is: - 1GB RAM Memgraph Cloud project has about 860 MB free RAM - 2GB RAM Memgraph Cloud project has about 1.60 GB free RAM - 4GB RAM Memgraph Cloud project has about 3.40 GB free RAM - 8GB RAM Memgraph Cloud project has about 6.7 GB free RAM - 16GB RAM Memgraph Cloud project has about 14 GB free RAM - 32GB RAM Memgraph Cloud project has about 28 GB free RAM ### I've created a new project, and when I try to connect to the instance, I get an error: Unable to connect. Upon creating a project, Memgraph loads all the MAGE algorithms, so it takes some time to load them all. Wait 30 seconds, and then try to connect again. ### I’ve paused my project and resumed it, but my Memgraph’s IP is different now? When you pause your project, usually the IP stays the same, but sometimes your IP can be released and a new one will be allocated. You can always check the IP in the connection details. ### How can I retrieve my project password? If you have forgotten your project password, we can't help you. We don't have a way of finding out or resetting your project password. ### How can I connect to my project? You can connect to an instance running within the Memgraph Cloud project via **Memgraph Lab**, a visual interface, **mgconsole**, command-line interface, or one of many drivers. You can find detailed instructions in [Memgraph Cloud documentation](https://memgraph.com/docs/getting-started/install-memgraph/memgraph-cloud). ### How can I backup my project? A project is backed up by creating a snapshot with Amazon EBS. You cannot create a snapshot if you are using a 14-day free trial version of Memgraph Cloud. You can find detailed instructions in [Memgraph Cloud documentation](https://memgraph.com/docs/getting-started/install-memgraph/memgraph-cloud). ### Is AWS available? Yes, Memgraph cloud is running at AWS. ### Is GCP available? No, at the moment, Memgraph cloud is not available on the Google Cloud Platform.