ArangoDB Datasource for Apache Spark

ArangoDB Datasource for Apache Spark allows batch reading and writing Spark DataFrame data from and to ArangoDB, by implementing the Spark Data Source V2 API.

Reading tasks are parallelized according to the number of shards of the related ArangoDB collection, and the writing ones - depending on the source DataFrame partitions. The network traffic is load balanced across the available DB Coordinators.

Filter predicates and column selections are pushed down to the DB by dynamically generating AQL queries, which will fetch only the strictly required data, thus saving network and computational resources both on the Spark and the DB side.

The connector is usable from all the Spark supported client languages: Scala, Python, Java, and R.

This library works with all the non-EOLed ArangoDB versions.

Supported versions

There are several variants of this library, each one compatible with different Spark and Scala versions:

  • com.arangodb:arangodb-spark-datasource-2.4_2.11 (Spark 2.4, Scala 2.11)
  • com.arangodb:arangodb-spark-datasource-2.4_2.12 (Spark 2.4, Scala 2.12)
  • com.arangodb:arangodb-spark-datasource-3.1_2.12 (Spark 3.1, Scala 2.12)
  • com.arangodb:arangodb-spark-datasource-3.2_2.12 (Spark 3.2, Scala 2.12)
  • com.arangodb:arangodb-spark-datasource-3.2_2.13 (Spark 3.2, Scala 2.13)

In the following sections the ${sparkVersion} and ${scalaVersion} placeholders refer to the Spark and Scala versions.


To import ArangoDB Datasource for Apache Spark in a Maven project:


To use in an external Spark cluster, submit your application with the following parameter:


General Configuration

  • user: db user, root by default
  • password: db password
  • endpoints: list of Coordinators, e.g. c1:8529,c2:8529 (required)
  • acquireHostList: acquire the list of all known hosts in the cluster (true or false), false by default
  • protocol: communication protocol (vst or http), http by default
  • contentType: content type for driver communication (json or vpack), json by default
  • timeout: driver connect and request timeout in ms, 300000 by default
  • ssl.enabled: ssl secured driver connection (true or false), false by default
  • ssl.cert.value: Base64 encoded certificate
  • ssl.cert.type: certificate type, X.509 by default
  • ssl.cert.alias: certificate alias name, arangodb by default
  • ssl.algorithm: trust manager algorithm, SunX509 by default
  • ssl.keystore.type: keystore type, jks by default
  • ssl.protocol: SSLContext protocol, TLS by default


To use TLS secured connections to ArangoDB, set ssl.enabled to true and either:

  • provide a Base64 encoded certificate as the ssl.cert.value configuration entry and optionally set ssl.* or
  • start the Spark driver and workers with a properly configured JVM default TrustStore

Supported deployment topologies

The connector can work with a single server, a cluster and active failover deployments of ArangoDB.

Batch Read

The connector implements support for batch reading from an ArangoDB collection.

val df: DataFrame =
  .options(options) // Map[String, String]
  .schema(schema) // StructType

The connector can read data from:

  • a collection
  • an AQL cursor (query specified by the user)

When reading data from a collection, the reading job is split into many Spark tasks, one for each shard in the ArangoDB source collection. The resulting Spark DataFrame has the same number of partitions as the number of shards in the ArangoDB collection, each one containing data from the respective collection shard. The reading tasks consist of AQL queries that are load balanced across all the available ArangoDB Coordinators. Each query is related to only one shard, therefore it will be executed locally in the DB-Server holding the related shard.

When reading data from an AQL cursor, the reading job cannot be partitioned or parallelized, so it will be less scalable. This mode can be used for reading data coming from different tables, i.e. resulting from an AQL traversal query.


val spark: SparkSession = SparkSession.builder()
  .config("", "")

val df: DataFrame =
    "password" -> "test",
    "endpoints" -> "c1:8529,c2:8529,c3:8529",
    "table" -> "users"
  .schema(new StructType(
      StructField("likes", ArrayType(StringType, containsNull = false)),
      StructField("birthday", DateType, nullable = true),
      StructField("gender", StringType, nullable = false),
      StructField("name", StructType(
          StructField("first", StringType, nullable = true),
          StructField("last", StringType, nullable = false)
      ), nullable = true)

usersDF.filter(col("birthday") === "1982-12-15").show()

Read Configuration

  • database: database name, _system by default
  • table: datasource ArangoDB collection name, ignored if query is specified. Either table or query is required.
  • query: custom AQL read query. If set, table will be ignored. Either table or query is required.
  • batchSize: reading batch size, 10000 by default
  • sampleSize: sample size prefetched for schema inference, only used if read schema is not provided, 1000 by default
  • fillBlockCache: specifies whether the query should store the data it reads in the RocksDB block cache (true or false), false by default
  • stream: specifies whether the query should be executed lazily, true by default
  • mode: allows setting a mode for dealing with corrupt records during parsing:
    • PERMISSIVE : win case of a corrupted record, the malformed string is put into a field configured by columnNameOfCorruptRecord, and sets malformed fields to null. To keep corrupt records, a user can set a string type field named columnNameOfCorruptRecord in a user-defined schema. If a schema does not have the field, it drops corrupt records during parsing. When inferring a schema, it implicitly adds the columnNameOfCorruptRecord field in an output schema
    • DROPMALFORMED: ignores the whole corrupted records
    • FAILFAST: throws an exception in case of corrupted records
  • columnNameOfCorruptRecord: allows renaming the new field having malformed string created by the PERMISSIVE mode

Predicate and Projection Pushdown

The connector can convert some Spark SQL filter predicates into AQL predicates and push their execution down to the data source. In this way, ArangoDB can apply the filters and return only the matching documents.

The following filter predicates (implementations of org.apache.spark.sql.sources.Filter) are pushed down:

  • And
  • Or
  • Not
  • EqualTo
  • EqualNullSafe
  • IsNull
  • IsNotNull
  • GreaterThan
  • GreaterThanOrEqualFilter
  • LessThan
  • LessThanOrEqualFilter
  • StringStartsWithFilter
  • StringEndsWithFilter
  • StringContainsFilter
  • InFilter

Furthermore, the connector will push down the subset of columns required by the Spark query, so that only the relevant documents fields will be returned.

Predicate and projection pushdowns are only performed while reading an ArangoDB collection (set by the table configuration parameter). In case of a batch read from a custom query (set by the query configuration parameter), no pushdown optimizations are performed.

Read Resiliency

The data of each partition is read using an AQL cursor. If any error occurs, the read task of the related partition will fail. Depending on the Spark configuration, the task could be retried.

Batch Write

The connector implements support for batch writing to ArangoDB collection.

import org.apache.spark.sql.DataFrame

val df: DataFrame = //...
    "password" -> "test",
    "endpoints" -> "c1:8529,c2:8529,c3:8529",
    "table" -> "users"

Write tasks are load balanced across the available ArangoDB Coordinators. The data saved into the ArangoDB is sharded according to the related target collection definition and is different from the Spark DataFrame partitioning.


On writing, org.apache.spark.sql.SaveMode is used to specify the expected behavior in case the target collection already exists.

Spark 2.4 implementation supports all save modes with the following semantics:

  • Append: the target collection is created, if it does not exist.
  • Overwrite: the target collection is created, if it does not exist, otherwise it is truncated. Use it in combination with the confirmTruncate write configuration parameter.
  • ErrorIfExists: the target collection is created, if it does not exist, otherwise an AnalysisException is thrown.
  • Ignore: the target collection is created, if it does not exist, otherwise no write is performed.

Spark 3 implementations support:

  • Append: the target collection is created, if it does not exist.
  • Overwrite: the target collection is created, if it does not exist, otherwise it is truncated. Use it in combination with the confirmTruncate write configuration parameter.

In Spark 3 implementations, the ErrorIfExists and Ignore save modes behave the same as Append.

Use the overwriteMode write configuration parameter to specify the document overwrite behavior (if a document with the same _key already exists).

Write Configuration

  • table: target ArangoDB collection name (required)
  • batchSize: writing batch size, 10000 by default
  • byteBatchSize: byte batch size threshold, only considered for contentType=json, 8388608 by default (8 MB)
  • table.shards: number of shards of the created collection (in case of the Append or Overwrite SaveMode)
  • table.type: type (document or edge) of the created collection (in case of the Append or Overwrite SaveMode), document by default
  • waitForSync: specifies whether to wait until the documents have been synced to disk (true or false), false by default
  • confirmTruncate: confirms to truncate table when using the Overwrite SaveMode, false by default
  • overwriteMode: configures the behavior in case a document with the specified _key value already exists. It is only considered for Append SaveMode.
    • ignore (default for SaveMode other than Append): it will not be written
    • replace: it will be overwritten with the specified document value
    • update: it will be patched (partially updated) with the specified document value. The overwrite mode can be further controlled via the keepNull and mergeObjects parameter. keepNull will also be automatically set to true, so that null values are kept in the saved documents and not used to remove existing document fields (as for default ArangoDB upsert behavior).
    • conflict (default for the Append SaveMode): return a unique constraint violation error so that the insert operation fails
  • mergeObjects: in case overwriteMode is set to update, controls whether objects (not arrays) will be merged.
    • true (default): objects will be merged
    • false: existing document fields will be overwritten
  • keepNull: in case overwriteMode is set to update
    • true (default): null values are saved within the document (by default)
    • false: null values are used to delete the corresponding existing attributes
  • retry.maxAttempts: max attempts for retrying write requests in case they are idempotent, 10 by default
  • retry.minDelay: min delay in ms between write requests retries, 0 by default
  • retry.maxDelay: max delay in ms between write requests retries, 0 by default

Write Resiliency

The data of each partition is saved in batches using the ArangoDB API for inserting multiple documents. This operation is not atomic, therefore some documents could be successfully written to the database, while others could fail. To make the job more resilient to temporary errors (i.e. connectivity problems), in case of failure the request will be retried (with another Coordinator), if the provided configuration allows idempotent requests, namely:

  • the schema of the dataframe has a not nullable _key field and
  • overwriteMode is set to one of the following values:
    • replace
    • ignore
    • update with keep.null=true

A failing batch-saving request is retried once for every Coordinator. After that, if still failing, the write task for the related partition is aborted. According to the Spark configuration, the task can be retried and rescheduled on a different executor, if the provided write configuration allows idempotent requests (as described above).

If a task ultimately fails and is aborted, the entire write job will be aborted as well. Depending on the SaveMode configuration, the following cleanup operations will be performed:

  • Append: no cleanup is performed and the underlying data source may require manual cleanup. DataWriteAbortException is thrown.
  • Overwrite: the target collection will be truncated.
  • ErrorIfExists: the target collection will be dropped.
  • Ignore: if the collection did not exist before, it will be dropped; otherwise, nothing will be done.

Write requirements

When writing to an edge collection (table.type=edge), the schema of the Dataframe being written must have:

  • a non nullable string field named _from, and
  • a non nullable string field named _to

Write Limitations

  • Batch writes are not performed atomically, so sometimes (i.e. in case of overwrite.mode: conflict) several documents in the batch may be written and others may return an exception (i.e. due to a conflicting key).
  • Writing records with the _key attribute is only allowed on collections sharded by _key.
  • In case of the Append save mode, failed jobs cannot be rolled back and the underlying data source may require manual cleanup.
  • Speculative execution of tasks only works for idempotent write configurations. See Write Resiliency for more details.
  • Speculative execution of tasks can cause concurrent writes to the same documents, resulting in write-write conflicts or lock timeouts

Mapping Configuration

Serialization and deserialization of Spark Dataframe Row to and from JSON (or Velocypack) can be customized using the following options:

  • ignoreNullFields: whether to ignore null fields during serialization, false by default (only supported in Spark 3.x)

Supported Spark data types

The following Spark SQL data types (subtypes of org.apache.spark.sql.types.Filter) are supported for reading, writing and filter pushdown.

  • Numeric types:
    • ByteType
    • ShortType
    • IntegerType
    • LongType
    • FloatType
    • DoubleType
  • String types:
    • StringType
  • Boolean types:
    • BooleanType
  • Datetime types:
    • TimestampType
    • DateType
  • Complex types:
    • ArrayType
    • MapType (only with key type StringType)
    • StructType

Connect to the ArangoGraph Insights Platform

To connect to SSL secured deployments using X.509 Base64 encoded CA certificate (ArangoGraph):

  val options = Map(
  "database" -> "<dbname>",
  "user" -> "<username>",
  "password" -> "<passwd>",
  "endpoints" -> "<endpoint>:<port>",
  "ssl.cert.value" -> "<base64 encoded CA certificate>",
  "ssl.enabled" -> "true",
  "table" -> "<table>"

// read
val myDF =

// write
import org.apache.spark.sql.DataFrame
val df: DataFrame = //...

Current limitations

  • For contentType=vpack, implicit deserialization casts don’t work well, i.e. reading a document having a field with a numeric value whereas the related read schema requires a string value for such a field.
  • Dates and timestamps fields are interpreted to be in a UTC time zone.
  • In Spark 2.4, for corrupted records in batch reading, partial results are not supported. All fields other than the field configured by columnNameOfCorruptRecord are set to null (SPARK-26303).
  • In read jobs using stream=true (default), possible AQL warnings are only logged at the end of each read task (BTS-671).
  • Spark SQL DecimalType fields are not supported in write jobs when using contentType=json.
  • Spark SQL DecimalType values are written to the database as strings.
  • byteBatchSize is only considered for contentType=json (DE-226)


Check out our demo to learn more about ArangoDB Datasource for Apache Spark.