Development Description

The CloudTable OpenTSDB and MRS OpenTSDB can be connected to DLI as data sources.

Prerequisites
A datasource connection has been created on the DLI management console.

Hard-coded or plaintext passwords pose significant security risks. To ensure security, encrypt your passwords, store them in configuration files or environment variables, and decrypt them when needed.

Code implementation

Import dependency packages.

from __future__ import print_function
from pyspark.sql.types import StructType, StructField, StringType, LongType, DoubleType
from pyspark.sql import SparkSession

Create a session.

sparkSession = SparkSession.builder.appName("datasource-opentsdb").getOrCreate()

Create a table to connect to an OpenTSDB data source.

sparkSession.sql("create table opentsdb_test using opentsdb options(
  'Host'='opentsdb-3xcl8dir15m58z3.cloudtable.com:4242',
  'metric'='ct_opentsdb',
  'tags'='city,location')")

For details about the Host, metric, and tags parameters, see Table 1.

Connecting to data sources through SQL APIs

Insert data.

sparkSession.sql("insert into opentsdb_test values('aaa', 'abc', '2021-06-30 18:00:00', 30.0)")

Query data.

result = sparkSession.sql("SELECT * FROM opentsdb_test")

Connecting to data sources through DataFrame APIs

Construct a schema.

schema = StructType([StructField("location", StringType()),\                     
                     StructField("name", StringType()), \                   
                     StructField("timestamp", LongType()),\                  
                     StructField("value", DoubleType())])

Set data.

dataList = sparkSession.sparkContext.parallelize([("aaa", "abc", 123456L, 30.0)])

Create a DataFrame.

dataFrame = sparkSession.createDataFrame(dataList, schema)

Import data to OpenTSDB.

dataFrame.write.insertInto("opentsdb_test")

Read data from OpenTSDB.

jdbdDF = sparkSession.read
    .format("opentsdb")\
    .option("Host","opentsdb-3xcl8dir15m58z3.cloudtable.com:4242")\
    .option("metric","ctopentsdb")\
    .option("tags","city,location")\
    .load()
jdbdDF.show()

Submitting a Spark job
1. Upload the Python code file to DLI.
2. In the Spark job editor, select the corresponding dependency module and execute the Spark job.
  If the Spark version is 2.3.2 (will be offline soon) or 2.4.5, specify the Module to sys.datasource.opentsdb when you submit a job.
  If the Spark version is 3.1.1, you do not need to select a module. Configure Spark parameters (--conf).
  spark.driver.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/opentsdb/*
  
  spark.executor.extraClassPath=/usr/share/extension/dli/spark-jar/datasource/opentsdb/*

Complete Example Code

Connecting to MRS OpenTSDB through SQL APIs

# _*_ coding: utf-8 _*_
from __future__ import print_function
from pyspark.sql.types import StructType, StructField, StringType, LongType, DoubleType
from pyspark.sql import SparkSession

if __name__ == "__main__":
  # Create a SparkSession session.    
  sparkSession = SparkSession.builder.appName("datasource-opentsdb").getOrCreate()


  # Create a DLI cross-source association opentsdb data table    
  sparkSession.sql(\
    "create table opentsdb_test using opentsdb options(\
    'Host'='10.0.0.171:4242',\
    'metric'='cts_opentsdb',\
    'tags'='city,location')")

  sparkSession.sql("insert into opentsdb_test values('aaa', 'abc', '2021-06-30 18:00:00', 30.0)")

  result = sparkSession.sql("SELECT * FROM opentsdb_test")
  result.show()

  # close session 
  sparkSession.stop()

Connecting to OpenTSDB through DataFrame APIs

# _*_ coding: utf-8 _*_
from __future__ import print_function
from pyspark.sql.types import StructType, StructField, StringType, LongType, DoubleType
from pyspark.sql import SparkSession

if __name__ == "__main__":
  # Create a SparkSession session.    
  sparkSession = SparkSession.builder.appName("datasource-opentsdb").getOrCreate()

  # Create a DLI cross-source association opentsdb data table    
  sparkSession.sql(
    "create table opentsdb_test using opentsdb options(\
    'Host'='opentsdb-3xcl8dir15m58z3.cloudtable.com:4242',\
    'metric'='ct_opentsdb',\
    'tags'='city,location')")

  # Create a DataFrame and initialize the DataFrame data.    
  dataList = sparkSession.sparkContext.parallelize([("aaa", "abc", 123456L, 30.0)])

  # Setting schema   
  schema = StructType([StructField("location", StringType()),\                 
                       StructField("name", StringType()),\                
                       StructField("timestamp", LongType()),\               
                       StructField("value", DoubleType())])

  # Create a DataFrame from RDD and schema   
  dataFrame = sparkSession.createDataFrame(dataList, schema)

  # Set cross-source connection parameters   
  metric = "ctopentsdb"   
  tags = "city,location"  
  Host = "opentsdb-3xcl8dir15m58z3.cloudtable.com:4242"

  # Write data to the cloudtable-opentsdb   
  dataFrame.write.insertInto("opentsdb_test")
  # ******* Opentsdb does not currently implement the ctas method to save data, so the save() method cannot be used.*******   
  # dataFrame.write.format("opentsdb").option("Host", Host).option("metric", metric).option("tags", tags).mode("Overwrite").save()   

  # Read data on CloudTable-OpenTSDB  
  jdbdDF = sparkSession.read\       
      .format("opentsdb")\      
      .option("Host",Host)\     
      .option("metric",metric)\  
      .option("tags",tags)\    
      .load()   
  jdbdDF.show()

  # close session 
  sparkSession.stop()

PySpark Example Code

Development Description

Complete Example Code