Aerospike Connect for Spark Tutorial for Python

Tested with Spark connector 3.4.0, ASDB EE EE, Java 8, Apache Spark 3.1.2, Python 3.7 and Scala 2.12.11 and Spylon


Set Aerospike, Spark, and Spark Connector Paths and Parameters

In [ ]:
# IP Address or DNS name for one host in your Aerospike cluster
AS_HOST ="localhost"
# Name of one of your namespaces. Type 'show namespaces' at the aql prompt if you are not sure
AS_NAMESPACE = "test" 
AS_PORT = 3000 # Usually 3000, but change here if not
In [ ]:
# Aerospike Spark Connector settings
import os 
AEROSPIKE_JAR_PATH ="aerospike-spark-3.4.0_spark_3.1_clientunshaded.jar"
os.environ["PYSPARK_SUBMIT_ARGS"] = '--jars ' + AEROSPIKE_JAR_PATH + ' pyspark-shell'

Alternative Setup for Running Notebook in Different Environment

Please follow the instructions below instead of the setup above if you are running this notebook in a different environment from the one provided by the Aerospike Intro-Notebooks container.

# IP Address or DNS name for one host in your Aerospike cluster
AS_HOST = "<seed-host-ip>"
# Name of one of your namespaces. Type 'show namespaces' at the aql prompt if you are not sure
AS_NAMESPACE = "<namespace>" 
AS_PORT = 3000 # Usually 3000, but change here if not

# Set SPARK_HOME path.
SPARK_HOME = '<spark-home-dir>'

# Please download the appropriate Aeropsike Connect for Spark from the [download page](  
# Set `AEROSPIKE_JAR_PATH` with path to the downloaded binary
import os 
AEROSPIKE_JAR_PATH= "<aerospike-jar-dir>/aerospike-spark-assembly-"+AEROSPIKE_SPARK_JAR_VERSION+".jar"
os.environ["PYSPARK_SUBMIT_ARGS"] = '--jars ' + AEROSPIKE_JAR_PATH + ' pyspark-shell'

Spark Initialization

In [ ]:
# Next we locate the Spark installation - this will be found using the SPARK_HOME environment variable that you will have set 

import findspark
In [ ]:
import pyspark
from pyspark.sql.types import *

Configure Aerospike properties in the Spark Session object. Please visit Configuring Aerospike Connect for Spark for more information about the properties used on this page.

In [ ]:
from pyspark.sql import SparkSession
from pyspark import SparkContext
sc = SparkContext.getOrCreate()
sc = pyspark.SparkContext(conf=conf)
spark = SparkSession(sc)
# sqlContext = SQLContext(sc)

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 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 that __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.

    schemaWithPK =  StructType([
              StructField("__key",IntegerType(), False),    
              StructField("id", IntegerType(), False),
              StructField("name", StringType(), False),
              StructField("age", IntegerType(), False),
              StructField("salary",IntegerType(), False)])
  • 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.

Flexible schema inference

Spark assumes that the underlying data store (Aerospike in this case) follows a strict schema for all the records within a table. However, Aerospike is a No-SQL DB and is schemaless. For further information on the Spark connector reconciles those differences, visit Flexible schema page

  • aerospike.schema.flexible = true (default)
  • aerospike.schema.flexible = false
In [ ]:
import random

schema = StructType( 
        StructField("id", IntegerType(), True),
        StructField("name", StringType(), True)

inputBuf = []
for  i in range(1, num_records) :
         name = "name"  + str(i)
         id_ = i 
         inputBuf.append((id_, name))
inputRDD = spark.sparkContext.parallelize(inputBuf)

#Write the Sample Data to Aerospike
inputDF \
.write \
.mode('overwrite') \
.format("aerospike")  \
.option("aerospike.writeset", "py_input_data")\
.option("aerospike.updateByKey", "id") \

aerospike.schema.flexible = true (default)

If none of the column types in the user-specified schema match the bin types of a record in Aerospike, a record with NULLs is returned in the result set.

Please use the filter() in Spark to filter out NULL records. For e.g. df.filter("gender == NULL").show(false), where df is a dataframe and gender is a field that was not specified in the user-specified schema.

If the above mismatch is limited to fewer columns in the user-specified schema then NULL would be returned for those columns in the result set. Note: there is no way to tell apart a NULL due to missing value in the original data set and the NULL due to mismatch, at this point. Hence, the user would have to treat all NULLs as missing values. The columns that are not a part of the schema will be automatically filtered out in the result set by the connector.

Please note that if any field is set to NOT nullable i.e. nullable = false, your query will error out if there’s a type mismatch between an Aerospike bin and the column type specified in the user-specified schema.

In [ ]:
schemaIncorrect = StructType( 
        StructField("id", IntegerType(), True),
        StructField("name", IntegerType(), True)  ##Note incorrect type of name bin

flexSchemaInference=spark \
.read \
.format("aerospike") \
.schema(schemaIncorrect) \
.option("aerospike.set", "py_input_data").load()

##notice all the contents of name column is null due to schema mismatch and aerospike.schema.flexible = true (by default)

aerospike.schema.flexible = false

If a mismatch between the user-specified schema and the schema of a record in Aerospike is detected at the bin/column level, your query will error out.

In [ ]:
#When strict matching is set, we will get an exception due to type mismatch with schema provided.

    errorDFStrictSchemaInference=spark \
    .read \
    .format("aerospike") \
    .schema(schemaIncorrect) \
    .option("aerospike.schema.flexible" ,"false") \
    .option("aerospike.set", "py_input_data").load()
except Exception as e:    
#This will throw error due to type mismatch 

Create sample data

In [ ]:
# We create age vs salary data, using three different Gaussian distributions
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import math

# Make sure we get the same results every time this workbook is run
# Otherwise we are occasionally exposed to results not working out as expected

# Create covariance matrix from std devs + correlation
def covariance_matrix(std_dev_1,std_dev_2,correlation):
    return [[std_dev_1 ** 2, correlation * std_dev_1 * std_dev_2], 
           [correlation * std_dev_1 * std_dev_2, std_dev_2 ** 2]]

# Return a bivariate sample given means/std dev/correlation
def age_salary_sample(distribution_params,sample_size):
    mean = [distribution_params["age_mean"], distribution_params["salary_mean"]]
    cov = covariance_matrix(distribution_params["age_std_dev"],distribution_params["salary_std_dev"],
    return np.random.multivariate_normal(mean, cov, sample_size).T

# Define the characteristics of our age/salary distribution
age_salary_distribution_1 = {"age_mean":25,"salary_mean":50000,

age_salary_distribution_2 = {"age_mean":45,"salary_mean":80000,

age_salary_distribution_3 = {"age_mean":35,"salary_mean":70000,

distribution_data = [age_salary_distribution_1,age_salary_distribution_2,age_salary_distribution_3]

# Sample age/salary data for each distributions
sample_size_1 = 100;
sample_size_2 = 120;
sample_size_3 = 80;
sample_sizes = [sample_size_1,sample_size_2,sample_size_3]
group_1_ages,group_1_salaries = age_salary_sample(age_salary_distribution_1,sample_size=sample_size_1)
group_2_ages,group_2_salaries = age_salary_sample(age_salary_distribution_2,sample_size=sample_size_2)
group_3_ages,group_3_salaries = age_salary_sample(age_salary_distribution_3,sample_size=sample_size_3)


print("Data created")

Display simulated age/salary data

In [ ]:
# Plot the sample data
group_1_colour, group_2_colour, group_3_colour ='red','blue', 'pink'

plt.scatter(group_1_ages,group_1_salaries,c=group_1_colour,label="Group 1")
plt.scatter(group_2_ages,group_2_salaries,c=group_2_colour,label="Group 2")
plt.scatter(group_3_ages,group_3_salaries,c=group_3_colour,label="Group 3")

plt.legend(loc='upper left')

Save data to Aerospike

In [ ]:
# Turn the above records into a Data Frame
# First of all, create an array of arrays
inputBuf = []

for  i in range(0, len(ages)) :
     id = i + 1 # Avoid counting from zero
     name = "Individual: {:03d}".format(id)
     # Note we need to make sure values are typed correctly
     # salary will have type numpy.float64 - if it is not cast as below, an error will be thrown
     age = float(ages[i])
     salary = int(salaries[i])
     inputBuf.append((id, name,age,salary))

# Convert to an RDD 
inputRDD = spark.sparkContext.parallelize(inputBuf)
# Convert to a data frame using a schema
schema = StructType([
    StructField("id", IntegerType(), True),
    StructField("name", StringType(), True),
    StructField("age", DoubleType(), True),
    StructField("salary",IntegerType(), True)


#Write the data frame to Aerospike, the id field is used as the primary key
inputDF \
.write \
.mode('overwrite') \
.format("aerospike")  \
.option("aerospike.set", "salary_data")\
.option("aerospike.updateByKey", "id") \

Using Spark SQL syntax to insert data

In [ ]:
#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.  

insertDFWithSchema=spark \
.read \
.format("aerospike") \
.schema(schema) \
.option("aerospike.set", "salary_data") \
.option("aerospike.updateByKey", "id") \


# V2 datasource doesn't allow insert into a view. 
spark.sql("select * from inserttable").show()

Load data into a DataFrame without specifying any Schema (uses schema inference)

In [ ]:
# Create a Spark DataFrame by using the Connector Schema inference mechanism
# The fields preceded with __ are metadata fields - key/digest/expiry/generation/ttl
# By default you just get everything, with no column ordering, which is why it looks untidy
# Note we don't get anything in the 'key' field as we have not chosen to save as a bin.
# Use .option("aerospike.sendKey", True) to do this

loadedDFWithoutSchema = ("aerospike") \
    .option("aerospike.set", "salary_data") \

Load data into a DataFrame using user specified schema

In [ ]:
# If we explicitly set the schema, using the previously created schema object
# we effectively type the rows in the Data Frame

loadedDFWithSchema=spark \
.read \
.format("aerospike") \
.schema(schema) \
.option("aerospike.set", "salary_data").load()

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.

In [ ]:
#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
sample_size=101"aerospike") \
.option("aerospike.partition.factor","2") \
.option("aerospike.set",setname) \
.option("aerospike.sample.size","101") \
.load()"aerospike") \
.option("aerospike.partition.factor","6") \
.option("aerospike.set",setname) \
.option("aerospike.sample.size","101") \

#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.

#Note how limit got only 101 records from df4.

print("count3= ", count3, " count4= ", count4, " limitCount=", limitCount)

Working with Collection Data Types (CDT) in Aerospike

Save JSON into Aerospike using a schema

In [ ]:
# Schema specification
aliases_type = StructType([

id_type = StructType([

street_adress_type = StructType([

address_type = StructType([

workHistory_type = StructType([
    StructField ("company_name",StringType(),False),
    StructField( "company_address",address_type,False),

person_type = StructType([

# JSON data location

# Read data in using prepared schema

# Save data to Aerospike
cmplx_data_with_schema \
.write \
.mode('overwrite') \
.format("aerospike")  \
.option("aerospike.writeset", "complex_input_data") \
.option("aerospike.updateByKey", "SSN") \

Retrieve CDT from Aerospike into a DataFrame using schema

In [ ]:
loadedComplexDFWithSchema=spark \
.read \
.format("aerospike") \
.option("aerospike.set", "complex_input_data") \
.schema(person_type) \

Data Exploration with Aerospike

In [ ]:
import pandas
import matplotlib
import matplotlib.pyplot as plt

#convert Spark df to pandas df
pdf = loadedDFWithSchema.toPandas()

# Describe the data

In [ ]:
#Histogram - Age
age_min, age_max = int(np.amin(pdf['age'])), math.ceil(np.amax(pdf['age']))
age_bucket_size = 5

#Histogram - Salary
salary_min, salary_max = int(np.amin(pdf['salary'])), math.ceil(np.amax(pdf['salary']))
salary_bucket_size = 5000

# Heatmap
age_bucket_count = math.ceil((age_max - age_min)/age_bucket_size)
salary_bucket_count = math.ceil((salary_max - salary_min)/salary_bucket_size)

x = [[0 for i in range(salary_bucket_count)] for j in range(age_bucket_count)]
for i in range(len(pdf['age'])):
    age_bucket = math.floor((pdf['age'][i] - age_min)/age_bucket_size)
    salary_bucket = math.floor((pdf['salary'][i] - salary_min)/salary_bucket_size)
    x[age_bucket][salary_bucket] += 1

plt.title("Salary/Age distribution heatmap")
plt.xlabel("Salary in '000s")

plt.imshow(x, cmap='YlOrRd', interpolation='nearest',extent=[salary_min/1000,salary_max/1000,age_min,age_max],

Quering Aerospike Data using SparkSQL


  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

With batch get queries we can apply filters on metadata columns such as __gen or __ttl. To do this, these columns should be exposed through the schema.

In [ ]:
# Basic PKey query
batchGet1= spark \
.read \
.format("aerospike") \
.option("aerospike.set", "salary_data") \
.option("aerospike.keyType", "int") \
.load().where("__key = 100") \
#Note ASDB only supports equality test with PKs in primary key query. 
#So, a where clause with "__key >10", would result in scan query!
In [ ]:
# Batch get, primary key based query
from pyspark.sql.functions import col
somePrimaryKeys= list(range(1,10))
someMoreKeys= list(range(12,14))
batchGet2= spark \
.read \
.format("aerospike") \
.option("aerospike.set", "salary_data") \
.option("aerospike.keyType", "int") \
.load().where((col("__key").isin(somePrimaryKeys)) | ( col("__key").isin(someMoreKeys)))

batchget query using __digest

  • __digest can have only two types BinaryType(default type) or StringType.
  • If schema is not provided and __digest is StringType, then set aerospike.digestType to string.
  • Records retrieved with __digest batchget call will have null primary key (i.e.__key is null).
In [ ]:
#convert digests to a list of byte[]"__digest").rdd.flatMap(lambda x: x).collect()

#convert digest to hex string for querying. Only digests of type hex string and byte[] array are allowed.
string_digest=[ ''.join(format(x, '02x') for x in m) for m in digest_list]

#option("aerospike.digestType", "string") hints to assume that __digest type is string in schema inference.
#please note that __key retrieved in this case is null. So be careful to use retrieved keys in downstream query!
batchGetWithDigest= spark \
.read \
.format("aerospike") \
.option("aerospike.set", "salary_data") \
.option("aerospike.digestType", "string") \

#digests can be mixed with primary keys as well
batchGetWithDigestAndKey= spark \
.read \
.format("aerospike") \
.option("aerospike.set", "salary_data") \
.option("aerospike.digestType", "string") \
.option("aerospike.keyType", "int") \
.load().where(col("__digest").isin(string_digest[0:1]) | ( col("__key").isin(someMoreKeys)))
#please note to the null in key columns in both dataframe

Queries including non-primary key conditions

In [ ]:
# This query will run as a scan, which will be slower
somePrimaryKeys= list(range(1,10))
scanQuery1= spark \
.read \
.format("aerospike") \
.option("aerospike.set", "salary_data") \
.option("aerospike.keyType", "int") \
.load().where((col("__key").isin(somePrimaryKeys)) | ( col("age") >50 ))

Pushdown Aerospike Expressions from within a Spark API.

  • Make sure that you do not use no the WHERE clause or spark filters while querying
  • See Aerospike Expressions for more information on how to construct expressions.
  • Contstructed expressions must be converted to Base64 before using them in the Spark API
In [ ]:

#id % 5 == 0  => get rows where mod(col("id")) ==0
#Equvalent java Exp: Exp.eq(Exp.mod(Exp.intBin("a"), Exp.`val`(5)), Exp.`val`(0))
expIntBin=scala_predexp.intBin("id") # id is the name of column
expMODIntBinEqualToZero=scala_predexp.eq(scala_predexp.mod(expIntBin, scala_predexp.val(5)),scala_predexp.val(0))
#expMODIntBinToBase64= "kwGTGpNRAqJpZAUA"
pushdownset = "py_input_data"

pushDownDF =spark\
        .read \
        .format("aerospike") \
        .schema(schema) \
        .option("aerospike.set", pushdownset) \
        .option("aerospike.pushdown.expressions", expMODIntBinToBase64) \

pushDownDF.count() #should get 39 records, we have 199/5 records whose id bin is divisble by 5
In [ ]:

Parameters for tuning Aerospike / Spark performance

  • aerospike.partition.factor: number of logical aerospike partitions [0-15]
  • aerospike.maxthreadcount : maximum number of threads to use for writing data into Aerospike
  • aerospike.compression : compression of java client-server communication
  • aerospike.batchMax : maximum number of records per read request (default 5000)
  • aerospike.recordspersecond : same as java client

Other useful parameters

  • aerospike.keyType : Primary key type hint for schema inference. Always set it properly if primary key type is not string

See for detailed description of the above properties

Machine Learning using Aerospike / Spark

In this section we use the data we took from Aerospike and apply a clustering algorithm to it.

We assume the data is composed of multiple data sets having a Gaussian multi-variate distribution

We don't know how many clusters there are, so we try clustering based on the assumption there are 1 through 20.

We compare the quality of the results using the Bayesian Information Criterion - and pick the best.

Find Optimal Cluster Count

In [ ]:
from sklearn.mixture import GaussianMixture

# We take the data we previously 

# Find the optimal number of clusters
optimal_cluster_count = 1
best_bic_score = GaussianMixture(1).fit(age_salary_matrix).bic(age_salary_matrix)

for count in range(1,20):
    if gm.bic(age_salary_matrix) < best_bic_score:
        best_bic_score = gm.bic(age_salary_matrix)
        optimal_cluster_count = count

print("Optimal cluster count found to be "+str(optimal_cluster_count))

Estimate cluster distribution parameters

Next we fit our cluster using the optimal cluster count, and print out the discovered means and covariance matrix

In [ ]:
gm = GaussianMixture(optimal_cluster_count)

estimates = []
# Index
for index in range(0,optimal_cluster_count):
    estimated_mean_age = round(gm.means_[index][0],2)
    estimated_mean_salary = round(gm.means_[index][1],0)
    estimated_age_std_dev = round(math.sqrt(gm.covariances_[index][0][0]),2)
    estimated_salary_std_dev = round(math.sqrt(gm.covariances_[index][1][1]),0)
    estimated_correlation = round(gm.covariances_[index][0][1] / ( estimated_age_std_dev * estimated_salary_std_dev ),3)
    row = [estimated_mean_age,estimated_mean_salary,estimated_age_std_dev,estimated_salary_std_dev,estimated_correlation]
pd.DataFrame(estimates,columns = ["Est Mean Age","Est Mean Salary","Est Age Std Dev","Est Salary Std Dev","Est Correlation"])    

Original Distribution Parameters

In [ ]:
distribution_data_as_rows = []
for distribution in distribution_data:
    row = [distribution['age_mean'],distribution['salary_mean'],distribution['age_std_dev'],

pd.DataFrame(distribution_data_as_rows,columns = ["Mean Age","Mean Salary","Age Std Dev","Salary Std Dev","Correlation"])

You can see that the algorithm provides good estimates of the original parameters


We generate new age/salary pairs for each of the distributions and look at how accurate the prediction is

In [ ]:
def prediction_accuracy(model,age_salary_distribution,sample_size):
    # Generate new values
    new_ages,new_salaries = age_salary_sample(age_salary_distribution,sample_size)
    # Find which cluster the mean would be classified into
    #mean = np.matrix([age_salary_distribution['age_mean'],age_salary_distribution['salary_mean']])
    #mean = np.asarray([age_salary_distribution['age_mean'],age_salary_distribution['salary_mean']])
    mean = np.asarray(np.matrix([age_salary_distribution['age_mean'],age_salary_distribution['salary_mean']]))
    mean_cluster_index = model.predict(mean)[0]
    # How would new samples be classified
    classification = model.predict(new_age_salary_matrix)
    # How many were classified correctly
    correctly_classified = len([ 1 for x in classification if x  == mean_cluster_index])
    return correctly_classified / sample_size

prediction_accuracy_results = [None for x in range(3)]
for index, age_salary_distribution in enumerate(distribution_data):
    prediction_accuracy_results[index] = prediction_accuracy(gm,age_salary_distribution,1000)

overall_accuracy = sum(prediction_accuracy_results)/ len(prediction_accuracy_results)
print("Accuracies for each distribution : "," ,".join(map('{:.2%}'.format,prediction_accuracy_results)))
print("Overall accuracy : ",'{:.2%}'.format(overall_accuracy))


aerolookup allows you to look up records corresponding to a set of keys stored in a Spark DF, streaming or otherwise. It supports:

  • Aerospike CDT
  • Quota and retry (these configurations are extracted from sparkconf)
  • Flexible schema. To enable, set aerospike.schema.flexible to true in the SparkConf object.
  • Aerospike Expressions Pushdown (Note: This must be specified through SparkConf object.)
In [ ]:
alias = StructType([StructField("first_name", StringType(), False),
                    StructField("last_name", StringType(), False)])
name = StructType([StructField("first_name", StringType(), False),
                   StructField("aliases", ArrayType(alias), False)])
street_adress = StructType([StructField("street_name", StringType(), False),
                            StructField("apt_number", IntegerType(), False)])
address = StructType([StructField("zip", LongType(), False),
                      StructField("street", street_adress, False),
                      StructField("city", StringType(), False)])
work_history = StructType([StructField("company_name", StringType(), False),
                          StructField("company_address", address, False),
                          StructField("worked_from", StringType(), False)])

output_schema = StructType([StructField("name", name, False),
                          StructField("SSN", StringType(), False),
                         StructField("home_address", ArrayType(address), False)])

ssns = [["825-55-3247"], ["289-18-1554"], ["756-46-4088"], 
        ["525-31-0299"], ["456-45-2200"], ["200-71-7765"]]

#Create a set of PKs whose records you'd like to look up in the Aerospike database

from pyspark.sql import SQLContext

scala_py_util= #Import the scala object
    customerIdsDF._jdf,  #Please note ._jdf
    'complex_input_data', #complex_input_data is the set in Aerospike database that you are using to look up the keys stored in SSN DF
    {} # may use this map to pass any aerospike configuration
#Note the wrapping of java object into python.sql.DataFrame 
In [ ]:

Secondary index

  • Secondary index query can be disabled by setting aerospike.sindex.enable to false (by default it is set to true).
  • User can specify secondary index by setting aerospike.sindex. If it is not set, connector appropriately selects secondary index for query execution.
  • User can also specify filter to use by setting aerospike.sindex.filter. This feature may be user to filter out CDT at the database site itself, which is not immediately possible to acheive using standard spark filters.
  • Refer to the documentation for detailed discussion.

create data for secondary index query demo

In [ ]:
si_schema = StructType([StructField("id", IntegerType(), True),
                        StructField("name", StringType(), True),
                       StructField("array", ArrayType(IntegerType()), True)])
si_set= "py_si_set"
siBuf = []
for i in range(1, si_records) :
    id_ = i 
    name = "name"  + str(i)
    arr= [x for x in range(i, i+3) ]
    siBuf.append((id_, name, arr))    

siRDD = spark.sparkContext.parallelize(siBuf)

#Write the secondary index Data to Aerospike
siInputDF.write.mode('overwrite').format("aerospike")  \
.option("aerospike.writeset", si_set).option("aerospike.updateByKey", "id").save()

create and list secondary indices

  • create secondary index py_id_idx, py_name_idx and py_arr_idx over respective bins.
  • list the reated indices using connector sindexList(namespace) API. This API assumes that sparksession is alread created and contains informations such as hostname, namespace in spark runtime configuration.
In [ ]:
!pip install aerospike

#index names to be created
num_idx= "py_id_idx"
str_idx= "py_name_idx"
arr_idx= "py_arr_idx"

import aerospike 
pyClient = aerospike.client({"hosts": [AS_HOST]}).connect()
    pyClient.index_integer_create('test', si_set, "id", num_idx)
    pyClient.index_string_create('test', si_set, "name", str_idx)
    pyClient.index_list_create('test', si_set, 'array', aerospike.INDEX_NUMERIC, arr_idx)
except ex.IndexFoundError as e:

#Print all "test" namsespace indices
Index selection
  • will be done automatically, if index is present in DB and aerospike.sindex is not set.
  • user may set aerospike.sindex to indicate to use the set index for query
In [ ]:
# automatically an appropriate secindary index is selected
siIdDF ="aerospike").schema(si_schema)\
.option("aerospike.set", si_set)\
siIdDF.where( >= 40).show() #should get 10 records
#search for `using secondary index: py_id_idx` in info logs 

user may set aerospike.sindex to use it for query

In [ ]:
#user specified index "aerospike.sindex"
siNameDF ="aerospike").schema(si_schema)\
.option("aerospike.set", si_set)\
siNameDF.where( == "name1").show() #should get 1 records
#search for `using secondary index: py_name_idx` in info logs 

Secondary index query over CDT using user specified filter

In [ ]:
#user specified filter in JSON format
arrayQuery =r'''{ "name": "array", "type": "NUMERIC", "colType": 1, "value": 10 }''' # "name" is bin name, colType =1 indicates sindex over array datatype.
siArrayDF ="aerospike").schema(si_schema)\
.option("aerospike.set", si_set).option("aerospike.partition.factor",1)\
.option("aerospike.sindex","py_arr_idx").load() #should print 3 records,