com.mysql.jdbc.Driver not found in spark2 scala - mysql

I am using Jupyter Notebook with Scala kernel, below is my code to import mysql table to a dataframe:
val sql="""select * from customer"""
val df_customer = spark.read
.format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/ccfd")
.option("driver", "com.mysql.jdbc.Driver")
.option("dbtable", s"( $sql ) t")
.option("user", "root")
.option("password", "xxxxxxx")
.load()
Below is the error:
Name: java.lang.ClassNotFoundException
Message: com.mysql.jdbc.Driver
StackTrace: at scala.reflect.internal.util.AbstractFileClassLoader.findClass(AbstractFileClassLoader.scala:62)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
at org.apache.spark.sql.execution.datasources.jdbc.DriverRegistry$.register(DriverRegistry.scala:45)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions$$anonfun$6.apply(JDBCOptions.scala:79)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions$$anonfun$6.apply(JDBCOptions.scala:79)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:79)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:35)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:34)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:340)
at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:239)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:227)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:164)
Can anyone share a working code snippet here? I am using Spark2, session named spark is ready when I start the kernel in a new notebook.
Thank you in advance.

Related

Apache Spark SQL get_json_object java.lang.String cannot be cast to org.apache.spark.unsafe.types.UTF8String

I am trying to read a json stream from an MQTT broker in Apache Spark with structured streaming, read some properties of an incoming json and output them to the console. My code looks like that:
val spark = SparkSession
.builder()
.appName("BahirStructuredStreaming")
.master("local[*]")
.getOrCreate()
import spark.implicits._
val topic = "temp"
val brokerUrl = "tcp://localhost:1883"
val lines = spark.readStream
.format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
.option("topic", topic).option("persistence", "memory")
.load(brokerUrl)
.toDF().withColumn("payload", $"payload".cast(StringType))
val jsonDF = lines.select(get_json_object($"payload", "$.eventDate").alias("eventDate"))
val query = jsonDF.writeStream
.format("console")
.start()
query.awaitTermination()
However, when the json arrives I get the following errors:
Exception in thread "main" org.apache.spark.sql.streaming.StreamingQueryException: Writing job aborted.
=== Streaming Query ===
Identifier: [id = 14d28475-d435-49be-a303-8e47e2f907e3, runId = b5bd28bb-b247-48a9-8a58-cb990edaf139]
Current Committed Offsets: {MQTTStreamSource[brokerUrl: tcp://localhost:1883, topic: temp clientId: paho7247541031496]: -1}
Current Available Offsets: {MQTTStreamSource[brokerUrl: tcp://localhost:1883, topic: temp clientId: paho7247541031496]: 0}
Current State: ACTIVE
Thread State: RUNNABLE
Logical Plan:
Project [get_json_object(payload#22, $.id) AS eventDate#27]
+- Project [id#10, topic#11, cast(payload#12 as string) AS payload#22, timestamp#13]
+- StreamingExecutionRelation MQTTStreamSource[brokerUrl: tcp://localhost:1883, topic: temp clientId: paho7247541031496], [id#10, topic#11, payload#12, timestamp#13]
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:300)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
Caused by: org.apache.spark.SparkException: Writing job aborted.
at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.doExecute(WriteToDataSourceV2Exec.scala:92)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:131)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:155)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
at org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:247)
at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:296)
at org.apache.spark.sql.Dataset.collectFromPlan(Dataset.scala:3384)
at org.apache.spark.sql.Dataset.$anonfun$collect$1(Dataset.scala:2783)
at org.apache.spark.sql.Dataset.$anonfun$withAction$2(Dataset.scala:3365)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:78)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3365)
at org.apache.spark.sql.Dataset.collect(Dataset.scala:2783)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runBatch$15(MicroBatchExecution.scala:537)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:78)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runBatch$14(MicroBatchExecution.scala:533)
at org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken(ProgressReporter.scala:351)
at org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken$(ProgressReporter.scala:349)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runBatch(MicroBatchExecution.scala:532)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runActivatedStream$2(MicroBatchExecution.scala:198)
at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
at org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken(ProgressReporter.scala:351)
at org.apache.spark.sql.execution.streaming.ProgressReporter.reportTimeTaken$(ProgressReporter.scala:349)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.$anonfun$runActivatedStream$1(MicroBatchExecution.scala:166)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:160)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279)
... 1 more
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 8, localhost, executor driver): java.lang.ClassCastException: java.lang.String cannot be cast to org.apache.spark.unsafe.types.UTF8String
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.getUTF8String(rows.scala:46)
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.getUTF8String$(rows.scala:46)
at org.apache.spark.sql.catalyst.expressions.GenericInternalRow.getUTF8String(rows.scala:195)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.$anonfun$run$2(WriteToDataSourceV2Exec.scala:117)
at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1394)
at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.run(WriteToDataSourceV2Exec.scala:116)
at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.$anonfun$doExecute$2(WriteToDataSourceV2Exec.scala:67)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:405)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:1887)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:1875)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:1874)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1874)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:926)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2108)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2057)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2046)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.doExecute(WriteToDataSourceV2Exec.scala:64)
... 34 more
Caused by: java.lang.ClassCastException: java.lang.String cannot be cast to org.apache.spark.unsafe.types.UTF8String
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.getUTF8String(rows.scala:46)
at org.apache.spark.sql.catalyst.expressions.BaseGenericInternalRow.getUTF8String$(rows.scala:46)
at org.apache.spark.sql.catalyst.expressions.GenericInternalRow.getUTF8String(rows.scala:195)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:619)
at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:460)
at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.$anonfun$run$2(WriteToDataSourceV2Exec.scala:117)
at org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1394)
at org.apache.spark.sql.execution.datasources.v2.DataWritingSparkTask$.run(WriteToDataSourceV2Exec.scala:116)
at org.apache.spark.sql.execution.datasources.v2.WriteToDataSourceV2Exec.$anonfun$doExecute$2(WriteToDataSourceV2Exec.scala:67)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:121)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:405)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:408)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
I am sending the JSON records using mosquitto broker and they look like this:
mosquitto_pub -m '{"eventDate": "2020-11-11T15:17:00.000+0200"}' -t "temp"
It seems that every strings coming from Bahir stream source provider raise this error. For instance the following code also raises this error :
spark.readStream
.format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
.option("topic", topic).option("persistence", "memory")
.load(brokerUrl)
.select("topic")
.writeStream
.format("console")
.start()
It looks like Spark does not recognize strings coming from Bahir, maybe some kind of weird string class version issue. I've tried the following actions to make the code work:
setup java version to 8
upgrade spark version from 2.4.0 to 2.4.7
setup scala version to 2.11.12
use decode function with all possible encoding combinations instead of .cast(StringType) to transform column "payload" to String
use substring function on column "payload" to recreate a compatible String.
Finally, I got working code by recreating the string using constructor and dataset:
val lines = spark.readStream
.format("org.apache.bahir.sql.streaming.mqtt.MQTTStreamSourceProvider")
.option("topic", topic).option("persistence", "memory")
.load(brokerUrl)
.select("payload")
.as[Array[Byte]]
.map(payload => new String(payload))
.toDF("payload")
This solution is rather ugly but at least it works.
I believe that there is nothing wrong with the code provided in the question and I suspect a bug on Bahir or Spark side preventing Spark to handle String from Bahir source.

Pyspark : Unable to import csv file in Zeppilin instance

I'm unable to run following line of code.
from pyspark.sql import SQLContext
sqlContext = SQLContext(sc)
df_t = sqlContext.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('s3a://Bucket_name/Train - Copy.csv')
it throws below error:
AnalysisException: u'java.lang.RuntimeException: java.lang.RuntimeException: Unable to instantiate org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient;'
I tried restarting the interpreter but no help.
Can someone please help with this issue?
Thanks,
Naseer
It seems, hive metastore is not running, you can try starting the service
hive --service metastore
you can use following code, to read csv which doesn't use SQLContext
from pyspark.sql import SparkSession
spark = SparkSession \
.builder \
.appName("Reading CSV") \
.getOrCreate()
df_t = spark.read.csv('s3a://Bucket_name/Train - Copy.csv',header=True, inferSchema=True)
df_t.show()

Use SparkSession.sql() with JDBC

Problem :
I would like to use JDBC connection to make a custom request using spark.
The goal of this query is to optimized memory allocation on workers, because of that I can't use :
ss.read
.format("jdbc")
.option("url", "jdbc:postgresql:dbserver")
.option("dbtable", "schema.tablename")
.option("user", "username")
.option("password", "password")
.load()
Currently :
I am currently trying to run :
ss = SparkSession
.builder()
.appName(appName)
.master("local")
.config(conf)
.getOrCreate()
ss.sql("some custom query")
Configuration :
url=jdbc:mysql://127.0.0.1/database_name
driver=com.mysql.jdbc.Driver
user=user_name
password=xxxxxxxxxx
Error :
[info] Exception encountered when attempting to run a suite with class name: db.TestUserProvider *** ABORTED ***
[info] org.apache.spark.sql.AnalysisException: Table or view not found: users; line 1 pos 14
[info] at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
[info] at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveRelations$$lookupTableFromCatalog(Analyzer.scala:459)
[info] at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$8.applyOrElse(Analyzer.scala:478)
[info] at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$8.applyOrElse(Analyzer.scala:463)
[info] at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:61)
[info] at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$resolveOperators$1.apply(LogicalPlan.scala:61)
[info] at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
[info] at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:60)
[info] at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:58)
[info] at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan$$anonfun$1.apply(LogicalPlan.scala:58)
Assumption :
I guess there is a configuration error, but I can't find out where.
Spark can read and write data to/from relational databases using the JDBC data source (like you did in your first code example).
In addition (and completely separately), spark allows using SQL to query views that were created over data that was already loaded into a DataFrame from some source. For example:
val df = Seq(1,2,3).toDF("a") // could be any DF, loaded from file/JDBC/memory...
df.createOrReplaceTempView("my_spark_table")
spark.sql("select a from my_spark_table").show()
Only "tables" (called views, as of Spark 2.0.0) created this way can be queried using SparkSession.sql.
If your data is stored in a relational database, Spark will have to read it from there first, and only then would it be able to execute any distributed computation on the loaded copy. Bottom line - we can load the data from the table using read, create a temp view, and then query it:
ss.read
.format("jdbc")
.option("url", "jdbc:mysql://127.0.0.1/database_name")
.option("dbtable", "schema.tablename")
.option("user", "username")
.option("password", "password")
.load()
.createOrReplaceTempView("my_spark_table")
// and then you can query the view:
val df = ss.sql("select * from my_spark_table where ... ")

Using JBDC to read sql file in spark scala collecting Warehouse error

I am trying to read MySQL file using Spark Scala. Following is the code I tried
val dataframe_mysql = sqlContext.read.format("jdbc")
.option("url","jdbc:mysql://xx.xx.xx.xx:xx")
.option("driver", "com.mysql.jdbc.Driver")
.option("dbtable", "schema.xxxx")
.option("user", "xxxx").option("password", "xxxxx").load()
but I am collecting Warehouse path error as following:
Warehouse path is 'file:/C:/Users/Owner/eclipse-workspace/stProject/spark-ware‌​house/'. Exception in thread "main" java.lang.NullPointerException at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.res‌​olveTable(JDBCRDD.sc‌​ala:72) at org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation‌​.(JDBCRelation‌​.scala:113) at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelation‌​Provider.createRelat‌​ion(JdbcRelationProv‌​ider.scala:45) at

Spark SQL error read JSON file : java.lang.ClassNotFoundException: scala.collection.GenTraversableOnce$class

i am trying to read JSON file using Spark SQL in Java.
this is my code
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.SQLContext;
...
JavaSparkContext jsc = new JavaSparkContext(sparkConf);
SQLContext sqlContext = new SQLContext(jsc);
DataFrame df = sqlContext.jsonFile("~/test.json");
df.printSchema();
df.registerTempTable("test");
...
i made simple JSON "test.json", to make it simple:
{
"name": "myname"
}
and when i tried to run the code, it comes error message:
efg
17/03/30 10:02:26 INFO BlockManagerMasterEndpoint: Registering block manager 10.6.86.82:36824 with 1948.2 MB RAM, BlockManagerId(driver, 10.6.86.82, 36824)
17/03/30 10:02:26 INFO BlockManagerMaster: Registered BlockManager BlockManagerId(driver, 10.6.86.82, 36824)
17/03/30 10:02:26 INFO StandaloneSchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.0
Exception in thread "main" java.lang.NoClassDefFoundError: scala/collection/GenTraversableOnce$class
at org.apache.spark.sql.sources.CaseInsensitiveMap.<init>(ddl.scala:344)
at org.apache.spark.sql.sources.ResolvedDataSource$.apply(ddl.scala:219)
at org.apache.spark.sql.SQLContext.load(SQLContext.scala:697)
at org.apache.spark.sql.SQLContext.jsonFile(SQLContext.scala:572)
at org.apache.spark.sql.SQLContext.jsonFile(SQLContext.scala:553)
at sugi.kau.sparkonjava.SparkSQL.main(SparkSQL.java:32)
Caused by: java.lang.ClassNotFoundException: scala.collection.GenTraversableOnce$class
at java.net.URLClassLoader.findClass(URLClassLoader.java:381)
at java.lang.ClassLoader.loadClass(ClassLoader.java:424)
at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:331)
at java.lang.ClassLoader.loadClass(ClassLoader.java:357)
... 6 more
17/03/30 10:02:26 INFO SparkContext: Invoking stop() from shutdown hook
...
thanks
in the docs spark for the function jsonFile(String path):
Loads a JSON file (one object per line), returning the result as a DataFrame. (Note tha jsonFile is replaced by read().json())
so you should have an object per line and your source file should be like this :
{"name": "myname"}
{"name": "myname2"}
.....