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Aerospike Spark Connector Tutorial for Scala

Tested with Spark connector 3.1.0, Java 8, Apache Spark 3.0.2, Python 3.7 and Scala 2.12.11 and Spylon

Getting Started​

Download the appropriate Aeropsike Connect for Spark​

Set launcher.jars with path to the downloaded binary

%%init_spark 
launcher.jars = ["aerospike-spark-assembly-3.1.0.jar"] 
launcher.master = "local[*]"
//Specify the Seed Host of the Aerospike Server
val AS_HOST = "172.16.39.192:3000"

Output

Intitializing Scala interpreter ...



Spark Web UI available at http://192.168.106.119:4040
SparkContext available as 'sc' (version = 3.0.2, master = local[*], app id = local-1626732681010)
SparkSession available as 'spark'






AS_HOST: String = 172.16.39.192:3000
import scala.collection.mutable.ArrayBuffer
import org.apache.spark.sql.Row
import org.apache.spark.sql.types._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.SaveMode
import com.aerospike.spark.sql.AerospikeConnection
import org.apache.spark.sql.SparkSession

Output

import scala.collection.mutable.ArrayBuffer
import org.apache.spark.sql.Row
import org.apache.spark.sql.types._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.SaveMode
import com.aerospike.spark.sql.AerospikeConnection
import org.apache.spark.sql.SparkSession

Schema in the Spark Connector​

  • Aerospike is schemaless, however Spark adher to schema. After the schema is decided upon (either through inference or given), data within the bins must honor the types.

  • To infer the schema, the connector samples a set of records (configurable through aerospike.schema.scan) to decide the name of bins/columns and their types. This implies that the derived schema depends entirely upon sampled records.

note

__key was not part of provided schema. So how can one query using __key? We can just add __key in provided schema with appropriate type. Similarly we can add __gen or __ttl etc.** 

  val schemaWithPK: StructType = new StructType(Array(
            StructField("__key",IntegerType, nullable = false),    
            StructField("id", IntegerType, nullable = false),
            StructField("name", StringType, nullable = false),
            StructField("age", IntegerType, nullable = false),
            StructField("salary",IntegerType, nullable = false)))
info

We recommend that you provide schema for queries that involve collection data types such as lists, maps, and mixed types. Using schema inference for CDT may cause unexpected issues.**

Create sample data and write it into Aerospike Database​

//Create test data
val conf = sc.getConf.clone();

conf.set("aerospike.seedhost" , AS_HOST)
conf.set("aerospike.namespace", "test")
conf.set("aerospike.log.level", "info")
spark.close()
val spark2= SparkSession.builder().config(conf).master("local[2]").getOrCreate()

val num_records=1000
val rand = scala.util.Random


val schema: StructType = new StructType(
    Array(
    StructField("id", IntegerType, nullable = false),
    StructField("name", StringType, nullable = false),
    StructField("age", IntegerType, nullable = false),
    StructField("salary",IntegerType, nullable = false)
  ))

val inputDF = {
    val inputBuf=  new ArrayBuffer[Row]()
    for ( i <- 1 to num_records){
        val name = "name"  + i
        val age = i%100
        val salary = 50000 + rand.nextInt(50000)
        val id = i 
        val r = Row(id, name, age,salary)
        inputBuf.append(r)
    }
    val inputRDD = spark2.sparkContext.parallelize(inputBuf.toSeq)
    spark2.createDataFrame(inputRDD,schema)
}

inputDF.show(10)

//Write the Sample Data to Aerospike
inputDF.write.mode(SaveMode.Overwrite) 
.format("aerospike") //aerospike specific format
.option("aerospike.writeset", "scala_input_data") //write to this set
.option("aerospike.updateByKey", "id") //indicates which columns should be used for construction of primary key
.option("aerospike.sendKey", "true")
.save()

Output

+---+------+---+------+
| id|  name|age|salary|
+---+------+---+------+
|  1| name1|  1| 71425|
|  2| name2|  2| 64969|
|  3| name3|  3| 76504|
|  4| name4|  4| 86652|
|  5| name5|  5| 72894|
|  6| name6|  6| 80305|
|  7| name7|  7| 68467|
|  8| name8|  8| 91715|
|  9| name9|  9| 81021|
| 10|name10| 10| 85134|
+---+------+---+------+
only showing top 10 rows






conf: org.apache.spark.SparkConf = org.apache.spark.SparkConf@43fd6f26
spark2: org.apache.spark.sql.SparkSession = org.apache.spark.sql.SparkSession@32d3db7d
num_records: Int = 1000
rand: util.Random.type = scala.util.Random$@698c02f8
schema: org.apache.spark.sql.types.StructType = StructType(StructField(id,IntegerType,false), StructField(name,StringType,false), StructField(age,IntegerType,false), StructField(salary,IntegerType,false))
inputDF: org.apache.spark.sql.DataFrame = [id: int, name: string ... 2 more fields]

Using Spark SQL syntax​

/*
Aerospike DB needs a Primary key for record insertion. Hence, you must identify the primary key column 
using for example .option(“aerospike.updateByKey”, “id”), where “id” is the name of the column that you’d 
like to be the Primary key, while loading data from the DB.
*/
val insertDFWithSchema=spark2
.sqlContext
.read
.format("aerospike")
.schema(schema)
.option("aerospike.set", "scala_input_data")
.load()

val sqlView="inserttable"
insertDFWithSchema.createOrReplaceTempView(sqlView)
//
//V2 datasource doesn't allow insert into a view. 
//

spark2.sql(s"select * from $sqlView").show()

Output

+---+-------+---+------+
| id|   name|age|salary|
+---+-------+---+------+
|132|name132| 32| 54949|
|647|name647| 47| 96580|
| 45| name45| 45| 82480|
|558|name558| 58| 91583|
|608|name608|  8| 83286|
|687|name687| 87| 96887|
|372|name372| 72| 64562|
|335|name335| 35| 59378|
|911|name911| 11| 93950|
|352|name352| 52| 57325|
| 94| name94| 94| 70982|
|890|name890| 90| 55053|
|334|name334| 34| 88603|
|907|name907|  7| 87233|
|148|name148| 48| 62904|
|315|name315| 15| 64200|
|163|name163| 63| 62598|
|882|name882| 82| 63215|
|602|name602|  2| 73376|
|673|name673| 73| 98636|
+---+-------+---+------+
only showing top 20 rows






insertDFWithSchema: org.apache.spark.sql.DataFrame = [id: int, name: string ... 2 more fields]
sqlView: String = inserttable

Use connector schema inference to load data into a DataFrame without specifying any schema​

// Create a Spark DataFrame by using the Connector Schema inference mechanism

val loadedDFWithoutSchema=spark2
.sqlContext
.read
.format("aerospike")
.option("aerospike.set", "scala_input_data") //read the data from this set
.load
loadedDFWithoutSchema.printSchema()
//Notice that schema of loaded data has some additional fields. 
// When connector infers schema, it also adds internal metadata.

Output

root
 |-- __key: string (nullable = true)
 |-- __digest: binary (nullable = true)
 |-- __expiry: integer (nullable = false)
 |-- __generation: integer (nullable = false)
 |-- __ttl: integer (nullable = false)
 |-- age: long (nullable = true)
 |-- name: string (nullable = true)
 |-- salary: long (nullable = true)
 |-- id: long (nullable = true)





loadedDFWithoutSchema: org.apache.spark.sql.DataFrame = [__key: string, __digest: binary ... 7 more fields]

Load data into a DataFrame with user specified schema​

//Data can be loaded with known schema as well.
val loadedDFWithSchema=spark2
.sqlContext
.read
.format("aerospike")
.schema(schema)
.option("aerospike.set", "scala_input_data").load
loadedDFWithSchema.show(5)

Output

+---+-------+---+------+
| id|   name|age|salary|
+---+-------+---+------+
|132|name132| 32| 54949|
|647|name647| 47| 96580|
| 45| name45| 45| 82480|
|558|name558| 58| 91583|
|608|name608|  8| 83286|
+---+-------+---+------+
only showing top 5 rows






loadedDFWithSchema: org.apache.spark.sql.DataFrame = [id: int, name: string ... 2 more fields]

Writing Sample Collection Data Types (CDT) data into Aerospike​

val complex_data_json="resources/nested_data.json"
val alias=  StructType(List(
    StructField("first_name",StringType, false),
    StructField("last_name",StringType, false)))

  val name= StructType(List(
    StructField("first_name",StringType, false),
    StructField("aliases",ArrayType(alias), false )
  ))

  val street_adress= StructType(List(
    StructField("street_name", StringType, false),
    StructField("apt_number" , IntegerType, false)))

  val address = StructType( List(
    StructField ("zip" , LongType, false),
    StructField("street", street_adress, false),
    StructField("city", StringType, false)))

  val workHistory = StructType(List(
    StructField ("company_name" , StringType, false),
    StructField( "company_address" , address, false),
    StructField("worked_from", StringType, false)))

  val person=  StructType ( List(
    StructField("name" , name, false, Metadata.empty),
    StructField("SSN", StringType, false,Metadata.empty),
    StructField("home_address", ArrayType(address), false),
    StructField("work_history", ArrayType(workHistory), false)))

val cmplx_data_with_schema=spark2.read.schema(person).json(complex_data_json)

cmplx_data_with_schema.printSchema()
cmplx_data_with_schema.write.mode(SaveMode.Overwrite) 
.format("aerospike") //aerospike specific format
.option("aerospike.seedhost", AS_HOST) //db hostname, can be added multiple hosts, delimited with ":"
.option("aerospike.namespace", "test") //use this namespace 
.option("aerospike.writeset", "scala_complex_input_data") //write to this set
.option("aerospike.updateByKey", "name.first_name") //indicates which columns should be used for construction of primary key
.save()

Output

root
 |-- name: struct (nullable = true)
 |    |-- first_name: string (nullable = true)
 |    |-- aliases: array (nullable = true)
 |    |    |-- element: struct (containsNull = true)
 |    |    |    |-- first_name: string (nullable = true)
 |    |    |    |-- last_name: string (nullable = true)
 |-- SSN: string (nullable = true)
 |-- home_address: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- zip: long (nullable = true)
 |    |    |-- street: struct (nullable = true)
 |    |    |    |-- street_name: string (nullable = true)
 |    |    |    |-- apt_number: integer (nullable = true)
 |    |    |-- city: string (nullable = true)
 |-- work_history: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- company_name: string (nullable = true)
 |    |    |-- company_address: struct (nullable = true)
 |    |    |    |-- zip: long (nullable = true)
 |    |    |    |-- street: struct (nullable = true)
 |    |    |    |    |-- street_name: string (nullable = true)
 |    |    |    |    |-- apt_number: integer (nullable = true)
 |    |    |    |-- city: string (nullable = true)
 |    |    |-- worked_from: string (nullable = true)






complex_data_json: String = resources/nested_data.json
alias: org.apache.spark.sql.types.StructType = StructType(StructField(first_name,StringType,false), StructField(last_name,StringType,false))
name: org.apache.spark.sql.types.StructType = StructType(StructField(first_name,StringType,false), StructField(aliases,ArrayType(StructType(StructField(first_name,StringType,false), StructField(last_name,StringType,false)),true),false))
street_adress: org.apache.spark.sql.types.StructType = StructType(StructField(street_name,StringType,false), StructField(apt_number,IntegerType,false))
address: org.apache.spark.sql.types.StructType = StructType(StructField(zip,LongType,false), StructField(street,StructType(StructField(street_name,StringType,false), StructField(apt_number,IntegerType,false)),fal...

Load Complex Data Types (CDT) into a DataFrame with user specified schema​

val loadedComplexDFWithSchema=spark2
.sqlContext
.read
.format("aerospike")
.option("aerospike.set", "scala_complex_input_data") //read the data from this set
.schema(person)
.load

loadedComplexDFWithSchema.show(2)
loadedComplexDFWithSchema.printSchema()
loadedComplexDFWithSchema.cache()
//Please note the difference in types of loaded data in both cases. With schema, we extactly infer complex types.

Output

+--------------------+-----------+--------------------+--------------------+
|                name|        SSN|        home_address|        work_history|
+--------------------+-----------+--------------------+--------------------+
|[Kurt, [[Jacob, R...|533-07-8760|[[37634, [Hammond...|[[Atkins Group, [...|
|[Jamie, [[Patrici...|569-31-4715|[[53379, [James I...|[[Brown, Miller a...|
+--------------------+-----------+--------------------+--------------------+
only showing top 2 rows

root
 |-- name: struct (nullable = false)
 |    |-- first_name: string (nullable = false)
 |    |-- aliases: array (nullable = false)
 |    |    |-- element: struct (containsNull = true)
 |    |    |    |-- first_name: string (nullable = false)
 |    |    |    |-- last_name: string (nullable = false)
 |-- SSN: string (nullable = false)
 |-- home_address: array (nullable = false)
 |    |-- element: struct (containsNull = true)
 |    |    |-- zip: long (nullable = false)
 |    |    |-- street: struct (nullable = false)
 |    |    |    |-- street_name: string (nullable = false)
 |    |    |    |-- apt_number: integer (nullable = false)
 |    |    |-- city: string (nullable = false)
 |-- work_history: array (nullable = false)
 |    |-- element: struct (containsNull = true)
 |    |    |-- company_name: string (nullable = false)
 |    |    |-- company_address: struct (nullable = false)
 |    |    |    |-- zip: long (nullable = false)
 |    |    |    |-- street: struct (nullable = false)
 |    |    |    |    |-- street_name: string (nullable = false)
 |    |    |    |    |-- apt_number: integer (nullable = false)
 |    |    |    |-- city: string (nullable = false)
 |    |    |-- worked_from: string (nullable = false)






loadedComplexDFWithSchema: org.apache.spark.sql.DataFrame = [name: struct<first_name: string, aliases: array<struct<first_name:string,last_name:string>>>, SSN: string ... 2 more fields]
res5: loadedComplexDFWithSchema.type = [name: struct<first_name: string, aliases: array<struct<first_name:string,last_name:string>>>, SSN: string ... 2 more fields]

Querying Aerospike Data using SparkSQL​

Things to keep in mind​

  1. Queries that involve Primary Key or Digest in the predicate trigger aerospike_batch_get() and run extremely fast. For e.g. a query containing __key or __digest with, with no OR between two bins.
  2. All other queries may entail a full scan of the Aerospike DB if they can’t be converted to Aerospike batchget.

Queries that include Primary Key in the Predicate​

In case of batchget queries we can also apply filters upon metadata columns like __gen or __ttl etc. To do so, these columns should be exposed through schema (if schema provided). 

val batchGet1= spark2.sqlContext
.read
.format("aerospike")
.option("aerospike.set", "scala_input_data")
.option("aerospike.keyType", "int") //used to hint primary key(PK) type when schema is not provided.
.load.where("__key = 829")
batchGet1.show()
//Please be aware Aerospike database supports only equality test with PKs in primary key query. 
//So, a where clause with "__key >10", would result in scan query!

Output

+-----+--------------------+--------+------------+-----+---+-------+------+---+
|__key|            __digest|__expiry|__generation|__ttl|age|   name|salary| id|
+-----+--------------------+--------+------------+-----+---+-------+------+---+
|  829|[C0 B6 C4 DE 68 D...|       0|           1|   -1| 29|name829| 92238|829|
+-----+--------------------+--------+------------+-----+---+-------+------+---+






batchGet1: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [__key: int, __digest: binary ... 7 more fields]
//In this query we are doing *OR* between PK subqueries 

val somePrimaryKeys= 1.to(10).toSeq
val someMoreKeys= 12.to(14).toSeq
val batchGet2= spark2.sqlContext
.read
.format("aerospike")
.option("aerospike.set", "scala_input_data")
.option("aerospike.keyType", "int") //used to hint primary key(PK) type when inferred without schema.
.load.where((col("__key") isin (somePrimaryKeys:_*)) || ( col("__key") isin (someMoreKeys:_*) ))
batchGet2.show(15)
//We should got in total 13 records.

Output

+-----+--------------------+--------+------------+-----+---+------+------+---+
|__key|            __digest|__expiry|__generation|__ttl|age|  name|salary| id|
+-----+--------------------+--------+------------+-----+---+------+------+---+
|    1|[89 31 AB FE 54 D...|       0|           1|   -1|  1| name1| 71425|  1|
|    4|[93 F1 65 F0 E8 9...|       0|           1|   -1|  4| name4| 86652|  4|
|    3|[D4 A1 0B A5 12 0...|       0|           1|   -1|  3| name3| 76504|  3|
|    7|[30 94 D4 E7 9E 8...|       0|           1|   -1|  7| name7| 68467|  7|
|    5|[3E F5 94 A9 3A A...|       0|           1|   -1|  5| name5| 72894|  5|
|   14|[06 66 ED 38 08 F...|       0|           1|   -1| 14|name14| 53533| 14|
|   13|[EA 78 AB 39 FC C...|       0|           1|   -1| 13|name13| 98475| 13|
|    2|[41 DB A8 23 03 4...|       0|           1|   -1|  2| name2| 64969|  2|
|    8|[60 AB E7 17 C8 5...|       0|           1|   -1|  8| name8| 91715|  8|
|    9|[1B 6D CD D8 D2 5...|       0|           1|   -1|  9| name9| 81021|  9|
|    6|[C2 4D 37 CC 2B 2...|       0|           1|   -1|  6| name6| 80305|  6|
|   12|[F8 4E EC 27 8F 1...|       0|           1|   -1| 12|name12| 80583| 12|
|   10|[8D 0F 84 CD B0 7...|       0|           1|   -1| 10|name10| 85134| 10|
+-----+--------------------+--------+------------+-----+---+------+------+---+






somePrimaryKeys: scala.collection.immutable.Range = Range 1 to 10
someMoreKeys: scala.collection.immutable.Range = Range 12 to 14
batchGet2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [__key: int, __digest: binary ... 7 more fields]

Queries that do not include Primary Key in the Predicate​


val somePrimaryKeys= 1.to(10).toSeq
val scanQuery1= spark2.sqlContext
.read
.format("aerospike")
.option("aerospike.set", "scala_input_data")
.option("aerospike.keyType", "int") //used to hint primary key(PK) type when inferred without schema.
.load.where((col("__key") isin (somePrimaryKeys:_*)) || ( col("age") >50 ))

scanQuery1.show()

//Since there is OR between PKs and Bin. It will be treated as Scan query. 
//Primary keys are not stored in bins(by default), hence only filters corresponding to bins are honored.

Output

+-----+--------------------+--------+------------+-----+---+-------+------+---+
|__key|            __digest|__expiry|__generation|__ttl|age|   name|salary| id|
+-----+--------------------+--------+------------+-----+---+-------+------+---+
|  558|[14 80 A2 9D D2 E...|       0|           1|   -1| 58|name558| 91583|558|
|  687|[1A 30 21 88 39 A...|       0|           1|   -1| 87|name687| 96887|687|
|  372|[1B 40 51 DD 64 F...|       0|           1|   -1| 72|name372| 64562|372|
|  352|[23 A0 99 06 1F 7...|       0|           1|   -1| 52|name352| 57325|352|
|   94|[26 E0 C4 85 CE 9...|       0|           1|   -1| 94| name94| 70982| 94|
|  890|[26 30 F7 1A D3 A...|       0|           1|   -1| 90|name890| 55053|890|
|  163|[3E D0 72 42 15 9...|       0|           1|   -1| 63|name163| 62598|163|
|  882|[3E C0 28 CE F2 5...|       0|           1|   -1| 82|name882| 63215|882|
|  673|[45 10 C1 D6 80 3...|       0|           1|   -1| 73|name673| 98636|673|
|  991|[47 A0 D4 EC 12 1...|       0|           1|   -1| 91|name991| 92557|991|
|  293|[48 40 20 B0 E6 D...|       0|           1|   -1| 93|name293| 82140|293|
|  679|[57 80 24 4F 1D 3...|       0|           1|   -1| 79|name679| 66054|679|
|  153|[5D E0 05 75 BF 3...|       0|           1|   -1| 53|name153| 79642|153|
|  485|[6B 80 7E E1 A4 5...|       0|           1|   -1| 85|name485| 54741|485|
|  997|[72 10 81 9D E2 E...|       0|           1|   -1| 97|name997| 80278|997|
|  482|[85 B0 B1 3F 49 A...|       0|           1|   -1| 82|name482| 79900|482|
|  166|[8A 00 3E 64 19 D...|       0|           1|   -1| 66|name166| 70542|166|
|  590|[8C 20 A4 28 BE 7...|       0|           1|   -1| 90|name590| 67077|590|
|  689|[9B 00 70 22 F0 8...|       0|           1|   -1| 89|name689| 93761|689|
|  895|[9D A0 9D 91 AE 8...|       0|           1|   -1| 95|name895| 76241|895|
+-----+--------------------+--------+------------+-----+---+-------+------+---+
only showing top 20 rows






somePrimaryKeys: scala.collection.immutable.Range = Range 1 to 10
scanQuery1: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [__key: int, __digest: binary ... 7 more fields]

Sampling from Aerospike DB​

  • Sample specified number of records from Aerospike to considerably reduce data movement between Aerospike and the Spark clusters. Depending on the aerospike.partition.factor setting, you may get more records than desired. Please use this property in conjunction with Spark limit() function to get the specified number of records. The sample read is not randomized, so sample more than you need and use the Spark sample() function to randomize if you see fit. You can use it in conjunction with aerospike.recordspersecond to control the load on the Aerospike server while sampling.

  • For more information, please see documentation page.

//number_of_spark_partitions (num_sp)=2^{aerospike.partition.factor}
//total number of records = Math.ceil((float)aerospike.sample.size/num_sp) * (num_sp) 
//use lower partition factor for more accurate sampling
val setname="scala_input_data"
val sample_size=101

val df3=spark2.read.format("aerospike")
.option("aerospike.partition.factor","2")
.option("aerospike.set",setname)
.option("aerospike.sample.size","101") //allows to sample approximately spacific number of record.  
.load()

val df4=spark2.read.format("aerospike")
.option("aerospike.partition.factor","6")
.option("aerospike.set",setname)
.option("aerospike.sample.size","101") //allows to sample approximately spacific number of record.  
.load()

//Notice that more records were read than requested due to the underlying partitioning logic related to the partition factor as described earlier, hence we use Spark limit() function additionally to return the desired number of records.
val count3=df3.count()
val count4=df4.count()


//Note how limit got only 101 record from df4 which have 128 records.
val dfWithLimit=df4.limit(101)
val limitCount=dfWithLimit.count()

Output

setname: String = scala_input_data
sample_size: Int = 101
df3: org.apache.spark.sql.DataFrame = [__key: string, __digest: binary ... 7 more fields]
df4: org.apache.spark.sql.DataFrame = [__key: string, __digest: binary ... 7 more fields]
count3: Long = 104
count4: Long = 128
dfWithLimit: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [__key: string, __digest: binary ... 7 more fields]
limitCount: Long = 101