Github user aniketadnaik commented on a diff in the pull request:
https://github.com/apache/carbondata/pull/1352#discussion_r138813289
--- Diff:
examples/spark2/src/main/scala/org/apache/carbondata/examples/streaming/CarbonStreamingIngestFileSourceExample.scala
---
@@ -0,0 +1,132 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.carbondata.examples
+
+import java.io.File
+
+import org.apache.commons.lang.RandomStringUtils
+import org.apache.spark.sql.{SaveMode, SparkSession}
+
+import org.apache.carbondata.core.constants.CarbonCommonConstants
+import org.apache.carbondata.core.util.CarbonProperties
+import org.apache.carbondata.examples.utils.StreamingCleanupUtil
+
+object CarbonStreamingIngestFileSourceExample {
+
+ def main(args: Array[String]) {
+
+ val rootPath = new File(this.getClass.getResource("/").getPath
+ + "../../../..").getCanonicalPath
+ val storeLocation = s"$rootPath/examples/spark2/target/store"
+ val warehouse = s"$rootPath/examples/spark2/target/warehouse"
+ val metastoredb = s"$rootPath/examples/spark2/target"
+ val csvDataDir = s"$rootPath/examples/spark2/resources/csvDataDir"
+ // val csvDataFile = s"$csvDataDir/sampleData.csv"
+ // val csvDataFile = s"$csvDataDir/sample.csv"
+ val streamTableName = s"_carbon_file_stream_table_"
+ val stremTablePath = s"$storeLocation/default/$streamTableName"
+ val ckptLocation = s"$rootPath/examples/spark2/resources/ckptDir"
+
+ CarbonProperties.getInstance()
+ .addProperty(CarbonCommonConstants.CARBON_TIMESTAMP_FORMAT,
"yyyy/MM/dd")
+
+ // cleanup any residual files
+ StreamingCleanupUtil.main(Array(csvDataDir, ckptLocation))
+
+ import org.apache.spark.sql.CarbonSession._
+ val spark = SparkSession
+ .builder()
+ .master("local")
+ .appName("CarbonFileStreamingExample")
+ .config("spark.sql.warehouse.dir", warehouse)
+ .getOrCreateCarbonSession(storeLocation, metastoredb)
+
+ spark.sparkContext.setLogLevel("ERROR")
+
+ // Writes Dataframe to CarbonData file:
+ import spark.implicits._
+ import org.apache.spark.sql.types._
+
+ // Generate random data
+ val dataDF = spark.sparkContext.parallelize(1 to 10)
+ .map(id => (id, "name_ABC", "city_XYZ", 10000.00*id)).
+ toDF("id", "name", "city", "salary")
+
+ // drop table if exists previously
+ spark.sql(s"DROP TABLE IF EXISTS ${streamTableName}")
+
+ // Create Carbon Table
+ // Saves dataframe to carbondata file
+ dataDF.write
+ .format("carbondata")
+ .option("tableName", streamTableName)
+ .option("compress", "true")
+ .option("tempCSV", "false")
+ .mode(SaveMode.Overwrite)
+ .save()
+
+ spark.sql(s""" SELECT * FROM ${streamTableName} """).show()
+
+ // Create csv data frame file
+ val csvDataDF = spark.sparkContext.parallelize(11 to 30)
+ .map(id => (id,
+ s"name_${RandomStringUtils.randomAlphabetic(4).toUpperCase}",
+ s"city_${RandomStringUtils.randomAlphabetic(2).toUpperCase}",
+ 10000.00*id)).toDF("id", "name", "city", "salary")
+
+ // write data into csv file ( It will be used as a stream source)
+ csvDataDF.write.
+ format("com.databricks.spark.csv").
+ option("header", "true").
+ save(csvDataDir)
+
+ // define custom schema
+ val inputSchema = new StructType().
+ add("id", "integer").
+ add("name", "string").
+ add("city", "string").
+ add("salary", "float")
+
+ // Read csv data file as a streaming source
+ val csvReadDF = spark.readStream.
--- End diff --
I referred to streaming examples from Spark and wanted to keep it similar
and simple. However, it should be doable. At this point we don't have read
support yet, may be I change it to have two threads, one for writing to csv
every few seconds and other for writing to carbondata table . This example can
be enhanced later to have read verification when read path supported is added.
---
