FAQ and troubleshooting

No. There is no shared code or approach between the two, and they take very different approaches. Cypher^® for Apache Spark/Morpheus took the approach of providing an interpreter that could execute Cypher queries within the Spark environment, and provided a native graph representation for Spark. By contrast, this connector does not provide that functionality and focuses on doing reads and writes back and forth between Neo4j and Spark. Via this connector, all Cypher code is executed strictly within Neo4j. The Spark environment operates in terms of DataFrames as it always did, and this connector does not provide graph API primitives for Spark.

The Spark connector fundamentally writes data to Neo4j in batches. Neo4j is a transactional database, and all modifications are made within a transaction. Those transactions in turn have overhead.

The two simplest ways of increasing write performance are the following:

It is important to keep in mind that Neo4j scales writes vertically and reads horizontally. In the Causal Cluster Model, only the cluster leader (1 machine) may accept writes. For this reason, focus on getting the best hardware and performance on your cluster leader to maximize write throughput.

Unfortunately, this is a known issue and is there for Neo4j 3.x and Neo4j 4.0. From Neo4j 4.1 onwards, you get the same order as it’s specified in the return statement.

The Neo4j Community site is a great place to ask questions, talk with other users who use the connector, and get help from Neo4j experts. You can also write in the Neo4j Discord channel.

In some cases, Neo4j rejects write transactions due to a deadlock exception that you may see in the stacktrace.

This Neo4j Knowledge Base article describes the issue.

Typically this is caused by too much parallelism in writing to Neo4j. For example, when you write a relationship (:A)-[:REL]→(:B), this creates a 'lock' in the database on both nodes. If some simultaneous other thread is attempting to write to those nodes too often, deadlock exceptions can result and a transaction fails.

In general, the solution is to repartition the DataFrame before writing it to Neo4j, to avoid multiple partitioned writes from locking the same nodes and relationships.

You might be getting an error like the following one or similar, with different types:

This is typically due to a field having different types on the same nodes label. You can solve it by installing APOC: this removes the error, but all the values for that field are cast to String. This happens because Spark is not schema-free and needs each column always to have its own type.

You can read more here.

If you are getting this error while trying to write to Neo4j, be aware that the current version of the connector doesn’t support SaveMode.ErrorIfExists on Spark 3, and that is the default save mode. So please, change the save mode to one of SaveMode.Append or SaveMode.Overwrite.

We are working to fully support all the save modes on Spark 3.

You may get one of the following types of error:

This means that your Spark version doesn’t match the Spark version on the connector. Refer to this page to know which version you need.

This might happen when creating a new graph using the GDS library. The issue here is that the query is run the first time to extract the DataFrame schema and then is run again to get the data.

To avoid this issue you can use the user defined schema approach.

Aura customers connecting from Databricks may encounter SSL handshake errors due to Databricks' custom Java security settings removing support for some encryption ciphers.

See the Aura support article Connecting to Aura with Databricks for more information.