Data migration from Neo4j to Neptune - Amazon Neptune
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Data migration from Neo4j to Neptune

When performing a migration from Neo4j to Amazon Neptune, migrating the data is a major step in the process. There are multiple approaches to migrating data. The correct approach is determined by the needs of the application, the data size, and the type of migration desired. However, many of these migrations all require assessment of the same considerations, of which several are highlighted below.


See the Migrating a Neo4j graph database to Neptune with a fully automated utility in the Amazon Database Blog for a complete, step-by-step walkthrough of one example of how to perform an offline data migration.

Assessing data migration from Neo4j to Neptune

The first step when assessing any data migration is to determine how you will migrate the data. The options depend on the architecture of the application being migrated, the data size, and the availability needs during the migration. In general, migrations tend to fall into one of two categories: online or offline.

Offline migrations tend to be the simplest to accomplish, because the application doesn't accept read or write traffic during the migration. After the application stops accepting traffic, the data can be exported, optimized, imported, and the application tested before the application is re-enabled.

Online migrations are more complex, because the application still needs to accept read and write traffic while the data is being migrated. The exact needs of each online migration may differ, but the general architecture would generally be similar to the following:

Data-model optimizations for migrating from Neo4j to Neptune

Both Neptune and Neo4j support labeled property graphs (LPG). However, Neptune has some architectural and data-model differences that you can take advantage of to optimize performance:

Optimizing node and edge IDs

Neo4j automatically generates numeric long IDs. Using Cypher you can refer to nodes by ID, but this is generally discouraged in favor of looking up nodes by an indexed property.

Neptune allows you to supply your own string-based IDs for vertices and edges. If you don't supply your own IDs, Neptune automatically generates string representations of UUIDs for new edges and vertices.

If you migrate data from Neo4j to Neptune by exporting from Neo4j and then bulk importing into Neptune, you can preserve Neo4j's IDs. The numeric values generated by Neo4j can act as user-supplied IDs when importing into Neptune, where they are represented as strings rather than numeric values.

However, there are circumstances in which you may want to promote a vertex property to become a vertex ID. Just as looking up a node using an indexed property is the fastest way to find a node in Neo4j, looking up a vertex by ID is the fastest way to find a vertex in Neptune. Therefore, if you can identify a suitable vertex property that contains unique values, you should consider replacing the vertex ~id with the nominated property value in your bulk load CSV files. If you do this, you will also have to rewrite any corresponding ~from and ~to edge values in your CSV files.

Schema constraints when migrating data from Neo4j to Neptune

Within Neptune, the only schema constraint available is the uniqueness of the ID of a node or edge. Applications that need to leverage a uniqueness constraint are encouraged to look at this approach for achieving a uniqueness constraint through specifying the node or edge ID. If the application used multiple columns as a uniqueness constraint, the ID may be set to a combination of these values. For instance, id=123, code='SEA' could be represented as ID='123_SEA') to achieve a complex uniqueness constraint.

Edge direction optimization when migrating data from Neo4j to Neptune

When nodes, edges, or properties are added to Neptune,they are automatically indexed in three different ways, with an optional fourth index. Because of how Neptune builds and uses the indices, queries that follow outgoing edges are more efficient than ones that use incoming edges. In terms of Neptune's graph data storage model, these are subject-based searches that use the SPOG index.

If, in migrating your data model and queries to Neptune, you find that your most important queries rely on traversing incoming edges where there is a high degree of fan out, you may want to consider altering your model so that these traversals follow outgoing edges instead, especially when you cannot specify which edge labels to traverse. To do so, reverse the direction of the relevant edges and update the edge labels to reflect the semantics of this direction change. For example, you might change:

person_A — parent_of — person_B to: person_B — child_of — person_A

To make this change in a bulk-load edge CSV file, simply swap the ~from and ~to column headings, and update the values of the ~label column.

As an alternative to reversing edge direction, you can enable a fourth Neptune index, the OSGP index, which makes traversing incoming edges, or object-based searches, much more efficient. However, enabling this fourth index will lower insert rates and require more storage.

Filtering optimization when migrating data from Neo4j to Neptune

Neptune is optimized to work best when properties are filtered to the most selective property available. When multiple filters are used, the set of matching items is found for each and then the overlap of all matching sets is calculated. When possible, combining multiple properties into a single property minimizes the number of index lookups and decreases the latency of a query.

For example, this query uses two index look-ups and a join:

MATCH (n) WHERE n.first_name='John' AND n.last_name='Doe' RETURN n

This query retrieves the same information using a single index look-up:


Neptune supports different data types than Neo4j does.

Neo4j data-type mappings into data types that Neptune supports
  • Logical:   Boolean

    Map this in Neptune to Bool or Boolean.

  • Numeric:   Number

    Map this in Neptune to the narrowest of the following Neptune openCypher types that can support all values of the numeric property in question:

    Byte Short Integer Long Float Double
  • Text:   String

    Map this in Neptune to String.

  • Point in time:

    Date Time LocalTime DateTime LocalDateTime

    Map these in Neptune to Date as UTC, using one of the following ISO-8601 formats that Neptune supports:

    yyyy-MM-dd yyyy-MM-ddTHH:mm yyyy-MM-ddTHH:mm:ss yyyy-MM-ddTHH:mm:ssZ
  • Time duration:   Duration

    Map this in Neptune to a numeric value for date arithmetic, if necessary.

  • Spatial:   Point

    Map this in Neptune into component numeric values, each of which then becomes a separate property, or express as a String value to be interpreted by the client application. Note that Neptune's full-text search integration using OpenSearch lets you index geolocation properties.

Migrating multivalued properties from Neo4j to Neptune

Neo4j allows homogeneous lists of simple types to be stored as properties of both nodes and edges. These lists can contain duplicate values.

Neptune, however, allows only set or single cardinality for vertex properties, and single cardinality for edge properties in property graph data. As a result, there is no straightforward migration of Neo4j node list properties that contain duplicate values into Neptune vertex properties, or of Neo4j relationship-list properties into Neptune edge properties.

Some possible strategies for migrating Neo4j multivalued node properties with duplicate values into Neptune are as follows:

  • Discard the duplicate values and convert the multivalued Neo4j node property to a set cardinality Neptune vertex property. Note that the Neptune set may not then reflect the order of items in the original Neo4j multivalued property.

  • Convert the multivalued Neo4j node property to a string representation of a JSON-formatted list in a Neptune vertex string property.

  • Extract each of the multivalued property values into a separate vertex with a value property, and connect those vertices to the parent vertex using an edge labelled with the property name.

Similarly, possible strategies for migrating Neo4j multivalued relationship properties into Neptune are as follows:

  • Convert the multivalued Neo4j relationship property to a string representation of a JSON-formatted list and store it as a Neptune edge string property.

  • Refactor the Neo4j relationship into incoming and outgoing Neptune edges attached to an intermediate vertex. Extract each of the multivalued relationship property values into a separate vertex with a value property and those vertices to this intermediate vertex using an edge labelled with the property name.

Note that a string representation of a JSON-formatted list is opaque to the openCypher query language, although openCypher includes a CONTAINS predicate that allows for simple searches inside string values.

Exporting data from Neo4j when migrating to Neptune

When exporting data from Neo4j, use the APOC procedures to export either to CSV or to GraphML. Although it's possible to export to other formats, there are open-source tools for converting CSV data exported from Neo4j to Neptune bulk-load format, and also open-source tools for converting GraphML data exported from Neo4j to Neptune bulk-load format.

You can also export data directly into Amazon S3 using the various APOC procedures. Exporting to an Amazon S3 bucket is disabled by default, but it can be enabled using the procedures highlighted in Exporting to Amazon S3 in the Neo4j APOC documentation.

Importing data from Neo4j when migrating to Neptune

You can import data into Neptune either by using the Neptune bulk loader or by using application logic in a supported query language such as openCypher.

The Neptune bulk loader is the preferred approach to importing large amounts of data because it provides optimized import performance if you follow best practices. The bulk loader supports two different CSV formats, to which data exported from Neo4j can be converted using the the open-source utilities mentioned above in the Exporting data section.

You can also use openCypher to import data with custom logic for parsing, transforming, and importing. You can submit the openCypher queries either through the HTTPS endpoint (which is recommended) or by using the bolt driver.