[
https://issues.apache.org/jira/browse/SPARK-20698?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
someshwar kale updated SPARK-20698:
-----------------------------------
Description:
I have written below spark program- its not working as expected
++++++++++++++++++++++++
{code}
package computedBatch;
import org.apache.log4j.Level;
import org.apache.log4j.Logger;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.sql.hive.HiveContext;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
public class ArithmeticIssueTest {
private transient JavaSparkContext javaSparkContext;
private transient SQLContext sqlContext;
public ArithmeticIssueTest() {
Logger.getLogger("org").setLevel(Level.OFF);
Logger.getLogger("akka").setLevel(Level.OFF);
SparkConf conf = new
SparkConf().setAppName("ArithmeticIssueTest").setMaster("local[4]");
javaSparkContext = new JavaSparkContext(conf);
sqlContext = new HiveContext(javaSparkContext);
}
public static void main(String[] args) {
ArithmeticIssueTest arithmeticIssueTest = new ArithmeticIssueTest();
arithmeticIssueTest.execute();
}
private void execute(){
List<String> data = Arrays.asList(
"a1,1494389759,99.8793003568,325.389705932",
"a1,1494389759,99.9472573803,325.27559502",
"a1,1494389759,99.7887233987,325.334374851",
"a1,1494389759,99.9547800925,325.371537062",
"a1,1494389759,99.8039111691,325.305285877",
"a1,1494389759,99.8342317379,325.24881354",
"a1,1494389759,99.9849449235,325.396678931",
"a1,1494389759,99.9396731311,325.336115345",
"a1,1494389759,99.9320915068,325.242622938",
"a1,1494389759,99.8943333669,325.320965146",
"a1,1494389759,99.7735359781,325.345168334",
"a1,1494389759,99.9698837734,325.352291407",
"a1,1494389759,99.8418330703,325.296539372",
"a1,1494389759,99.796315751,325.347570632",
"a1,1494389759,99.7811931613,325.351137315",
"a1,1494389759,99.9773765104,325.218131741",
"a1,1494389759,99.8189825201,325.288197381",
"a1,1494389759,99.8115005369,325.282327633",
"a1,1494389759,99.9924539722,325.24048614",
"a1,1494389759,99.9170191204,325.299431664");
JavaRDD<String> rawData = javaSparkContext.parallelize(data);
List<StructField> fields = new ArrayList<>();
fields.add(DataTypes.createStructField("ASSET_ID",
DataTypes.StringType, true));
fields.add(DataTypes.createStructField("TIMESTAMP", DataTypes.LongType,
true));
fields.add(DataTypes.createStructField("fuel", DataTypes.DoubleType,
true));
fields.add(DataTypes.createStructField("temperature",
DataTypes.DoubleType, true));
StructType schema = DataTypes.createStructType(fields);
JavaRDD<Row> rowRDD = rawData.map(
(Function<String, Row>) record -> {
String[] fields1 = record.split(",");
return RowFactory.create(
fields1[0].trim(),
Long.parseLong(fields1[1].trim()),
Double.parseDouble(fields1[2].trim()),
Double.parseDouble(fields1[3].trim()));
});
DataFrame df = sqlContext.createDataFrame(rowRDD, schema);
df.show(false);
df.registerTempTable("x_linkx1087571272_filtered");
sqlContext.sql("SELECT x_linkx1087571272_filtered.ASSET_ID, count(case
when x_linkx1087571272_filtered" +
".temperature=325.0 then 1 else 0 end) AS xsumptionx1582594572,
max(x_linkx1087571272_filtered" +
".TIMESTAMP) AS eventTime FROM x_linkx1087571272_filtered
GROUP BY x_linkx1087571272_filtered" +
".ASSET_ID").show(false);
sqlContext.sql("SELECT x_linkx1087571272_filtered.ASSET_ID, count(case
when x_linkx1087571272_filtered" +
".fuel>99.8 then 1 else 0 end) AS xnsumptionx352569416,
max(x_linkx1087571272_filtered.TIMESTAMP) AS " +
"eventTime FROM x_linkx1087571272_filtered GROUP BY
x_linkx1087571272_filtered.ASSET_ID").show(false);
// +++++++++
sqlContext.sql("SELECT x_linkx1087571272_filtered.ASSET_ID, count(case
when x_linkx1087571272_filtered" +
".temperature==325.0 then 1 else 0 end) AS
xsumptionx1582594572, max(x_linkx1087571272_filtered" +
".TIMESTAMP) AS eventTime FROM x_linkx1087571272_filtered
GROUP BY x_linkx1087571272_filtered" +
".ASSET_ID").show(false);
}
}
{code}
++++++++++++++++++++++++++++++
Logs-
+--------+----------+-------------+-------------+
|ASSET_ID|TIMESTAMP |fuel |temperature |
+--------+----------+-------------+-------------+
|a1 |1494389759|99.8793003568|325.389705932|
|a1 |1494389759|99.9472573803|325.27559502 |
|a1 |1494389759|99.7887233987|325.334374851|
|a1 |1494389759|99.9547800925|325.371537062|
|a1 |1494389759|99.8039111691|325.305285877|
|a1 |1494389759|99.8342317379|325.24881354 |
|a1 |1494389759|99.9849449235|325.396678931|
|a1 |1494389759|99.9396731311|325.336115345|
|a1 |1494389759|99.9320915068|325.242622938|
|a1 |1494389759|99.8943333669|325.320965146|
|a1 |1494389759|99.7735359781|325.345168334|
|a1 |1494389759|99.9698837734|325.352291407|
|a1 |1494389759|99.8418330703|325.296539372|
|a1 |1494389759|99.796315751 |325.347570632|
|a1 |1494389759|99.7811931613|325.351137315|
|a1 |1494389759|99.9773765104|325.218131741|
|a1 |1494389759|99.8189825201|325.288197381|
|a1 |1494389759|99.8115005369|325.282327633|
|a1 |1494389759|99.9924539722|325.24048614 |
|a1 |1494389759|99.9170191204|325.299431664|
+--------+----------+-------------+-------------+
17/05/11 00:22:08 INFO ParseDriver: Parsing command: SELECT
x_linkx1087571272_filtered.ASSET_ID, count(case when
x_linkx1087571272_filtered.temperature=325.0 then 1 else 0 end) AS
xsumptionx1582594572, max(x_linkx1087571272_filtered.TIMESTAMP) AS eventTime
FROM x_linkx1087571272_filtered GROUP BY x_linkx1087571272_filtered.ASSET_ID
17/05/11 00:22:09 INFO ParseDriver: Parse Completed
[Stage 5:======================================================>(198 + 1) /
199]+--------+--------------------+----------+
|ASSET_ID|xsumptionx1582594572|eventTime |
+--------+--------------------+----------+
|a1 |20 |1494389759|
+--------+--------------------+----------+
17/05/11 00:22:16 INFO ParseDriver: Parsing command: SELECT
x_linkx1087571272_filtered.ASSET_ID, count(case when
x_linkx1087571272_filtered.fuel>99.8 then 1 else 0 end) AS
xnsumptionx352569416, max(x_linkx1087571272_filtered.TIMESTAMP) AS eventTime
FROM x_linkx1087571272_filtered GROUP BY x_linkx1087571272_filtered.ASSET_ID
17/05/11 00:22:16 INFO ParseDriver: Parse Completed
+--------+--------------------+----------+
|ASSET_ID|xnsumptionx352569416|eventTime |
+--------+--------------------+----------+
|a1 |20 |1494389759|
+--------+--------------------+----------+
17/05/11 00:22:24 INFO ParseDriver: Parsing command: SELECT
x_linkx1087571272_filtered.ASSET_ID, count(case when
x_linkx1087571272_filtered.temperature==325.0 then 1 else 0 end) AS
xsumptionx1582594572, max(x_linkx1087571272_filtered.TIMESTAMP) AS eventTime
FROM x_linkx1087571272_filtered GROUP BY x_linkx1087571272_filtered.ASSET_ID
17/05/11 00:22:24 INFO ParseDriver: Parse Completed
[Stage 13:==========================================> (158 + 4) /
199]+--------+--------------------+----------+
|ASSET_ID|xsumptionx1582594572|eventTime |
+--------+--------------------+----------+
|a1 |20 |1494389759|
+--------+--------------------+----------+
both the queries are resulting to wrong values
was:
I have written below spark program- its not working as expected
++++++++++++++++++++++++
package computedBatch;
import org.apache.log4j.Level;
import org.apache.log4j.Logger;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.sql.hive.HiveContext;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
public class ArithmeticIssueTest {
private transient JavaSparkContext javaSparkContext;
private transient SQLContext sqlContext;
public ArithmeticIssueTest() {
Logger.getLogger("org").setLevel(Level.OFF);
Logger.getLogger("akka").setLevel(Level.OFF);
SparkConf conf = new
SparkConf().setAppName("ArithmeticIssueTest").setMaster("local[4]");
javaSparkContext = new JavaSparkContext(conf);
sqlContext = new HiveContext(javaSparkContext);
}
public static void main(String[] args) {
ArithmeticIssueTest arithmeticIssueTest = new ArithmeticIssueTest();
arithmeticIssueTest.execute();
}
private void execute(){
List<String> data = Arrays.asList(
"a1,1494389759,99.8793003568,325.389705932",
"a1,1494389759,99.9472573803,325.27559502",
"a1,1494389759,99.7887233987,325.334374851",
"a1,1494389759,99.9547800925,325.371537062",
"a1,1494389759,99.8039111691,325.305285877",
"a1,1494389759,99.8342317379,325.24881354",
"a1,1494389759,99.9849449235,325.396678931",
"a1,1494389759,99.9396731311,325.336115345",
"a1,1494389759,99.9320915068,325.242622938",
"a1,1494389759,99.8943333669,325.320965146",
"a1,1494389759,99.7735359781,325.345168334",
"a1,1494389759,99.9698837734,325.352291407",
"a1,1494389759,99.8418330703,325.296539372",
"a1,1494389759,99.796315751,325.347570632",
"a1,1494389759,99.7811931613,325.351137315",
"a1,1494389759,99.9773765104,325.218131741",
"a1,1494389759,99.8189825201,325.288197381",
"a1,1494389759,99.8115005369,325.282327633",
"a1,1494389759,99.9924539722,325.24048614",
"a1,1494389759,99.9170191204,325.299431664");
JavaRDD<String> rawData = javaSparkContext.parallelize(data);
List<StructField> fields = new ArrayList<>();
fields.add(DataTypes.createStructField("ASSET_ID",
DataTypes.StringType, true));
fields.add(DataTypes.createStructField("TIMESTAMP", DataTypes.LongType,
true));
fields.add(DataTypes.createStructField("fuel", DataTypes.DoubleType,
true));
fields.add(DataTypes.createStructField("temperature",
DataTypes.DoubleType, true));
StructType schema = DataTypes.createStructType(fields);
JavaRDD<Row> rowRDD = rawData.map(
(Function<String, Row>) record -> {
String[] fields1 = record.split(",");
return RowFactory.create(
fields1[0].trim(),
Long.parseLong(fields1[1].trim()),
Double.parseDouble(fields1[2].trim()),
Double.parseDouble(fields1[3].trim()));
});
DataFrame df = sqlContext.createDataFrame(rowRDD, schema);
df.show(false);
df.registerTempTable("x_linkx1087571272_filtered");
sqlContext.sql("SELECT x_linkx1087571272_filtered.ASSET_ID, count(case
when x_linkx1087571272_filtered" +
".temperature=325.0 then 1 else 0 end) AS xsumptionx1582594572,
max(x_linkx1087571272_filtered" +
".TIMESTAMP) AS eventTime FROM x_linkx1087571272_filtered
GROUP BY x_linkx1087571272_filtered" +
".ASSET_ID").show(false);
sqlContext.sql("SELECT x_linkx1087571272_filtered.ASSET_ID, count(case
when x_linkx1087571272_filtered" +
".fuel>99.8 then 1 else 0 end) AS xnsumptionx352569416,
max(x_linkx1087571272_filtered.TIMESTAMP) AS " +
"eventTime FROM x_linkx1087571272_filtered GROUP BY
x_linkx1087571272_filtered.ASSET_ID").show(false);
// +++++++++
sqlContext.sql("SELECT x_linkx1087571272_filtered.ASSET_ID, count(case
when x_linkx1087571272_filtered" +
".temperature==325.0 then 1 else 0 end) AS
xsumptionx1582594572, max(x_linkx1087571272_filtered" +
".TIMESTAMP) AS eventTime FROM x_linkx1087571272_filtered
GROUP BY x_linkx1087571272_filtered" +
".ASSET_ID").show(false);
}
}
++++++++++++++++++++++++++++++
Logs-
+--------+----------+-------------+-------------+
|ASSET_ID|TIMESTAMP |fuel |temperature |
+--------+----------+-------------+-------------+
|a1 |1494389759|99.8793003568|325.389705932|
|a1 |1494389759|99.9472573803|325.27559502 |
|a1 |1494389759|99.7887233987|325.334374851|
|a1 |1494389759|99.9547800925|325.371537062|
|a1 |1494389759|99.8039111691|325.305285877|
|a1 |1494389759|99.8342317379|325.24881354 |
|a1 |1494389759|99.9849449235|325.396678931|
|a1 |1494389759|99.9396731311|325.336115345|
|a1 |1494389759|99.9320915068|325.242622938|
|a1 |1494389759|99.8943333669|325.320965146|
|a1 |1494389759|99.7735359781|325.345168334|
|a1 |1494389759|99.9698837734|325.352291407|
|a1 |1494389759|99.8418330703|325.296539372|
|a1 |1494389759|99.796315751 |325.347570632|
|a1 |1494389759|99.7811931613|325.351137315|
|a1 |1494389759|99.9773765104|325.218131741|
|a1 |1494389759|99.8189825201|325.288197381|
|a1 |1494389759|99.8115005369|325.282327633|
|a1 |1494389759|99.9924539722|325.24048614 |
|a1 |1494389759|99.9170191204|325.299431664|
+--------+----------+-------------+-------------+
17/05/11 00:22:08 INFO ParseDriver: Parsing command: SELECT
x_linkx1087571272_filtered.ASSET_ID, count(case when
x_linkx1087571272_filtered.temperature=325.0 then 1 else 0 end) AS
xsumptionx1582594572, max(x_linkx1087571272_filtered.TIMESTAMP) AS eventTime
FROM x_linkx1087571272_filtered GROUP BY x_linkx1087571272_filtered.ASSET_ID
17/05/11 00:22:09 INFO ParseDriver: Parse Completed
[Stage 5:======================================================>(198 + 1) /
199]+--------+--------------------+----------+
|ASSET_ID|xsumptionx1582594572|eventTime |
+--------+--------------------+----------+
|a1 |20 |1494389759|
+--------+--------------------+----------+
17/05/11 00:22:16 INFO ParseDriver: Parsing command: SELECT
x_linkx1087571272_filtered.ASSET_ID, count(case when
x_linkx1087571272_filtered.fuel>99.8 then 1 else 0 end) AS
xnsumptionx352569416, max(x_linkx1087571272_filtered.TIMESTAMP) AS eventTime
FROM x_linkx1087571272_filtered GROUP BY x_linkx1087571272_filtered.ASSET_ID
17/05/11 00:22:16 INFO ParseDriver: Parse Completed
+--------+--------------------+----------+
|ASSET_ID|xnsumptionx352569416|eventTime |
+--------+--------------------+----------+
|a1 |20 |1494389759|
+--------+--------------------+----------+
17/05/11 00:22:24 INFO ParseDriver: Parsing command: SELECT
x_linkx1087571272_filtered.ASSET_ID, count(case when
x_linkx1087571272_filtered.temperature==325.0 then 1 else 0 end) AS
xsumptionx1582594572, max(x_linkx1087571272_filtered.TIMESTAMP) AS eventTime
FROM x_linkx1087571272_filtered GROUP BY x_linkx1087571272_filtered.ASSET_ID
17/05/11 00:22:24 INFO ParseDriver: Parse Completed
[Stage 13:==========================================> (158 + 4) /
199]+--------+--------------------+----------+
|ASSET_ID|xsumptionx1582594572|eventTime |
+--------+--------------------+----------+
|a1 |20 |1494389759|
+--------+--------------------+----------+
both the queries are resulting to wrong values
> =, ==, > is not working as expected when used in sql query
> ----------------------------------------------------------
>
> Key: SPARK-20698
> URL: https://issues.apache.org/jira/browse/SPARK-20698
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.2
> Environment: windows
> Reporter: someshwar kale
> Priority: Critical
> Fix For: 1.6.2
>
>
> I have written below spark program- its not working as expected
> ++++++++++++++++++++++++
> {code}
> package computedBatch;
> import org.apache.log4j.Level;
> import org.apache.log4j.Logger;
> import org.apache.spark.SparkConf;
> import org.apache.spark.api.java.JavaRDD;
> import org.apache.spark.api.java.JavaSparkContext;
> import org.apache.spark.api.java.function.Function;
> import org.apache.spark.sql.DataFrame;
> import org.apache.spark.sql.Row;
> import org.apache.spark.sql.RowFactory;
> import org.apache.spark.sql.SQLContext;
> import org.apache.spark.sql.hive.HiveContext;
> import org.apache.spark.sql.types.DataTypes;
> import org.apache.spark.sql.types.StructField;
> import org.apache.spark.sql.types.StructType;
> import java.util.ArrayList;
> import java.util.Arrays;
> import java.util.List;
> public class ArithmeticIssueTest {
> private transient JavaSparkContext javaSparkContext;
> private transient SQLContext sqlContext;
> public ArithmeticIssueTest() {
> Logger.getLogger("org").setLevel(Level.OFF);
> Logger.getLogger("akka").setLevel(Level.OFF);
> SparkConf conf = new
> SparkConf().setAppName("ArithmeticIssueTest").setMaster("local[4]");
> javaSparkContext = new JavaSparkContext(conf);
> sqlContext = new HiveContext(javaSparkContext);
> }
> public static void main(String[] args) {
> ArithmeticIssueTest arithmeticIssueTest = new ArithmeticIssueTest();
> arithmeticIssueTest.execute();
> }
> private void execute(){
> List<String> data = Arrays.asList(
> "a1,1494389759,99.8793003568,325.389705932",
> "a1,1494389759,99.9472573803,325.27559502",
> "a1,1494389759,99.7887233987,325.334374851",
> "a1,1494389759,99.9547800925,325.371537062",
> "a1,1494389759,99.8039111691,325.305285877",
> "a1,1494389759,99.8342317379,325.24881354",
> "a1,1494389759,99.9849449235,325.396678931",
> "a1,1494389759,99.9396731311,325.336115345",
> "a1,1494389759,99.9320915068,325.242622938",
> "a1,1494389759,99.8943333669,325.320965146",
> "a1,1494389759,99.7735359781,325.345168334",
> "a1,1494389759,99.9698837734,325.352291407",
> "a1,1494389759,99.8418330703,325.296539372",
> "a1,1494389759,99.796315751,325.347570632",
> "a1,1494389759,99.7811931613,325.351137315",
> "a1,1494389759,99.9773765104,325.218131741",
> "a1,1494389759,99.8189825201,325.288197381",
> "a1,1494389759,99.8115005369,325.282327633",
> "a1,1494389759,99.9924539722,325.24048614",
> "a1,1494389759,99.9170191204,325.299431664");
> JavaRDD<String> rawData = javaSparkContext.parallelize(data);
> List<StructField> fields = new ArrayList<>();
> fields.add(DataTypes.createStructField("ASSET_ID",
> DataTypes.StringType, true));
> fields.add(DataTypes.createStructField("TIMESTAMP",
> DataTypes.LongType, true));
> fields.add(DataTypes.createStructField("fuel", DataTypes.DoubleType,
> true));
> fields.add(DataTypes.createStructField("temperature",
> DataTypes.DoubleType, true));
> StructType schema = DataTypes.createStructType(fields);
> JavaRDD<Row> rowRDD = rawData.map(
> (Function<String, Row>) record -> {
> String[] fields1 = record.split(",");
> return RowFactory.create(
> fields1[0].trim(),
> Long.parseLong(fields1[1].trim()),
> Double.parseDouble(fields1[2].trim()),
> Double.parseDouble(fields1[3].trim()));
> });
> DataFrame df = sqlContext.createDataFrame(rowRDD, schema);
> df.show(false);
> df.registerTempTable("x_linkx1087571272_filtered");
> sqlContext.sql("SELECT x_linkx1087571272_filtered.ASSET_ID,
> count(case when x_linkx1087571272_filtered" +
> ".temperature=325.0 then 1 else 0 end) AS
> xsumptionx1582594572, max(x_linkx1087571272_filtered" +
> ".TIMESTAMP) AS eventTime FROM x_linkx1087571272_filtered
> GROUP BY x_linkx1087571272_filtered" +
> ".ASSET_ID").show(false);
> sqlContext.sql("SELECT x_linkx1087571272_filtered.ASSET_ID,
> count(case when x_linkx1087571272_filtered" +
> ".fuel>99.8 then 1 else 0 end) AS xnsumptionx352569416,
> max(x_linkx1087571272_filtered.TIMESTAMP) AS " +
> "eventTime FROM x_linkx1087571272_filtered GROUP BY
> x_linkx1087571272_filtered.ASSET_ID").show(false);
> // +++++++++
> sqlContext.sql("SELECT x_linkx1087571272_filtered.ASSET_ID,
> count(case when x_linkx1087571272_filtered" +
> ".temperature==325.0 then 1 else 0 end) AS
> xsumptionx1582594572, max(x_linkx1087571272_filtered" +
> ".TIMESTAMP) AS eventTime FROM x_linkx1087571272_filtered
> GROUP BY x_linkx1087571272_filtered" +
> ".ASSET_ID").show(false);
> }
> }
> {code}
> ++++++++++++++++++++++++++++++
> Logs-
> +--------+----------+-------------+-------------+
> |ASSET_ID|TIMESTAMP |fuel |temperature |
> +--------+----------+-------------+-------------+
> |a1 |1494389759|99.8793003568|325.389705932|
> |a1 |1494389759|99.9472573803|325.27559502 |
> |a1 |1494389759|99.7887233987|325.334374851|
> |a1 |1494389759|99.9547800925|325.371537062|
> |a1 |1494389759|99.8039111691|325.305285877|
> |a1 |1494389759|99.8342317379|325.24881354 |
> |a1 |1494389759|99.9849449235|325.396678931|
> |a1 |1494389759|99.9396731311|325.336115345|
> |a1 |1494389759|99.9320915068|325.242622938|
> |a1 |1494389759|99.8943333669|325.320965146|
> |a1 |1494389759|99.7735359781|325.345168334|
> |a1 |1494389759|99.9698837734|325.352291407|
> |a1 |1494389759|99.8418330703|325.296539372|
> |a1 |1494389759|99.796315751 |325.347570632|
> |a1 |1494389759|99.7811931613|325.351137315|
> |a1 |1494389759|99.9773765104|325.218131741|
> |a1 |1494389759|99.8189825201|325.288197381|
> |a1 |1494389759|99.8115005369|325.282327633|
> |a1 |1494389759|99.9924539722|325.24048614 |
> |a1 |1494389759|99.9170191204|325.299431664|
> +--------+----------+-------------+-------------+
> 17/05/11 00:22:08 INFO ParseDriver: Parsing command: SELECT
> x_linkx1087571272_filtered.ASSET_ID, count(case when
> x_linkx1087571272_filtered.temperature=325.0 then 1 else 0 end) AS
> xsumptionx1582594572, max(x_linkx1087571272_filtered.TIMESTAMP) AS eventTime
> FROM x_linkx1087571272_filtered GROUP BY x_linkx1087571272_filtered.ASSET_ID
> 17/05/11 00:22:09 INFO ParseDriver: Parse Completed
> [Stage 5:======================================================>(198 + 1) /
> 199]+--------+--------------------+----------+
> |ASSET_ID|xsumptionx1582594572|eventTime |
> +--------+--------------------+----------+
> |a1 |20 |1494389759|
> +--------+--------------------+----------+
> 17/05/11 00:22:16 INFO ParseDriver: Parsing command: SELECT
> x_linkx1087571272_filtered.ASSET_ID, count(case when
> x_linkx1087571272_filtered.fuel>99.8 then 1 else 0 end) AS
> xnsumptionx352569416, max(x_linkx1087571272_filtered.TIMESTAMP) AS eventTime
> FROM x_linkx1087571272_filtered GROUP BY x_linkx1087571272_filtered.ASSET_ID
> 17/05/11 00:22:16 INFO ParseDriver: Parse Completed
> +--------+--------------------+----------+
> |ASSET_ID|xnsumptionx352569416|eventTime |
> +--------+--------------------+----------+
> |a1 |20 |1494389759|
> +--------+--------------------+----------+
> 17/05/11 00:22:24 INFO ParseDriver: Parsing command: SELECT
> x_linkx1087571272_filtered.ASSET_ID, count(case when
> x_linkx1087571272_filtered.temperature==325.0 then 1 else 0 end) AS
> xsumptionx1582594572, max(x_linkx1087571272_filtered.TIMESTAMP) AS eventTime
> FROM x_linkx1087571272_filtered GROUP BY x_linkx1087571272_filtered.ASSET_ID
> 17/05/11 00:22:24 INFO ParseDriver: Parse Completed
> [Stage 13:==========================================> (158 + 4) /
> 199]+--------+--------------------+----------+
> |ASSET_ID|xsumptionx1582594572|eventTime |
> +--------+--------------------+----------+
> |a1 |20 |1494389759|
> +--------+--------------------+----------+
> both the queries are resulting to wrong values
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