Slick can't connect to mssql database - sql-server

I would like to use Slick (3.2.3) to connect to a MSSQL database.
Currently, my project is the following.
In application.conf, I have
somedbname = {
driver = "slick.jdbc.SQLServerProfile$"
db {
host = "somehost"
port = "someport"
databaseName = "Recupel.Datawarehouse"
url = "jdbc:sqlserver://"${somedbname.db.host}":"${somedbname.db.port}";databaseName="${somedbname.db.databaseName}";"
user = "someuser"
password = "somepassword"
}
}
The "somehost" looks like XX.X.XX.XX where X's are numbers.
My build.sbt contains
name := "test-slick"
version := "0.1"
scalaVersion in ThisBuild := "2.12.7"
libraryDependencies ++= Seq(
"com.typesafe.slick" %% "slick" % "3.2.3",
"com.typesafe.slick" %% "slick-hikaricp" % "3.2.3",
"org.slf4j" % "slf4j-nop" % "1.6.4",
"com.microsoft.sqlserver" % "mssql-jdbc" % "7.0.0.jre10"
)
The file with the "main" object contains
import slick.basic.DatabaseConfig
import slick.jdbc.JdbcProfile
import slick.jdbc.SQLServerProfile.api._
import scala.concurrent.Await
import scala.concurrent.duration._
val dbConfig: DatabaseConfig[JdbcProfile] = DatabaseConfig.forConfig("somedbname")
val db: JdbcProfile#Backend#Database = dbConfig.db
def main(args: Array[String]): Unit = {
try {
val future = db.run(sql"SELECT * FROM somettable".as[(Int, String, String, String, String,
String, String, String, String, String, String, String)])
println(Await.result(future, 10.seconds))
} finally {
db.close()
}
}
}
This, according to all the documentation that I could find, should be enough to connect to the database. However, when I run this, I get
[error] (run-main-0) java.sql.SQLTransientConnectionException: somedbname.db - Connection is not available, request timed out after 1004ms.
[error] java.sql.SQLTransientConnectionException: somedbname.db - Connection is not available, request timed out after 1004ms.
[error] at com.zaxxer.hikari.pool.HikariPool.createTimeoutException(HikariPool.java:548)
[error] at com.zaxxer.hikari.pool.HikariPool.getConnection(HikariPool.java:186)
[error] at com.zaxxer.hikari.pool.HikariPool.getConnection(HikariPool.java:145)
[error] at com.zaxxer.hikari.HikariDataSource.getConnection(HikariDataSource.java:83)
[error] at slick.jdbc.hikaricp.HikariCPJdbcDataSource.createConnection(HikariCPJdbcDataSource.scala:14)
[error] at slick.jdbc.JdbcBackend$BaseSession.<init>(JdbcBackend.scala:453)
[error] at slick.jdbc.JdbcBackend$DatabaseDef.createSession(JdbcBackend.scala:46)
[error] at slick.jdbc.JdbcBackend$DatabaseDef.createSession(JdbcBackend.scala:37)
[error] at slick.basic.BasicBackend$DatabaseDef.acquireSession(BasicBackend.scala:249)
[error] at slick.basic.BasicBackend$DatabaseDef.acquireSession$(BasicBackend.scala:248)
[error] at slick.jdbc.JdbcBackend$DatabaseDef.acquireSession(JdbcBackend.scala:37)
[error] at slick.basic.BasicBackend$DatabaseDef$$anon$2.run(BasicBackend.scala:274)
[error] at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1135)
[error] at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:635)
[error] at java.base/java.lang.Thread.run(Thread.java:844)
[error] Nonzero exit code: 1
Perhaps related and also annoying, when I run this code for the second (and subsequent) times, I get the following error instead:
Failed to get driver instance for jdbcUrl=jdbc:sqlserver://[...]
which forces me to kill and reload sbt each time.
What am I doing wrong? Worth noting: I can connect to the database with the same credential from a software like valentina.

As suggested by #MarkRotteveel, and following this link, I found a solution.
First, I explicitly set the driver, adding the line
driver = "com.microsoft.sqlserver.jdbc.SQLServerDriver"
in the db dictionary, after password = "somepassword".
Secondly, the default timeout (after one second) appears to be too short for my purposes, and therefore I added the line
connectionTimeout = "30 seconds"
after the previous driver line, still in the db dictionary.
Now it works.

Related

postgresSQL terminating abnormally

I am trying to update postgresSQL table with psycopg2 (python package) sometimes it is failing with below error.
server closed the connection unexpectedly
This probably means the server terminated abnormally
before or while processing the request.
Here is the code
from psycopg2 import pool
now = datetime.now()
logoff_time = datetime(now.year, now.month, now.day, 15, 0, 0)
while True:
time.sleep(1)
try:
status = 'EXECUTED'
exec_type1 = 'CANCELLED'
exec_type2 = 'COMPLETED'
try:
postgreSQL_pool = pool.SimpleConnectionPool(1, 20, host = db_host,
database = db_name,
port = db_port,
user = db_user,
password = db_pwd)
if postgreSQL_pool:
print("Connection pool created successfully")
conn = postgreSQL_pool.getconn()
except (Exception, psycopg2.DatabaseError) as error:
print(error)
sql = """ UPDATE orders SET status = %s, executed_type = %s WHERE order_id = %s"""
updated_rows = 0
try:
cur = conn.cursor()
cur.execute(sql, (status, exec_type1, order_id,))
conn.commit()
updated_rows = cur.rowcount
cur.close()
break
except (Exception, psycopg2.DatabaseError) as error:
print(error)
print(updated_rows)
except Exception as e:
print(e)
psycopg2 version: '2.8.6 (dt dec pq3 ext lo64)'
Postgres: PostgreSQL 12.7 on x86_64-pc-linux-gnu, compiled by gcc (GCC) 4.8.3 20140911 (Red Hat 4.8.3-9), 64-bit
it is pretty much simple task but facing challenges. suggestions please
The server is crashing for some reason that you might be able to read in the server's logs.

PyFlink - Issue using Scala UDF in JAR

I'm trying to register a Scala UDF in Pyflink using an external JAR as follows, but get below error.
Scala UDF:
package com.dummy
import org.apache.flink.table.functions.ScalarFunction
class dummyTransform(factor: Int) extends ScalarFunction {
def eval(s: String): Int = {
s.hashCode()
}
}
build.sbt:
name := "hello_scala_for_flink"
version := "0.1"
scalaVersion := "2.12.11"
libraryDependencies += "org.apache.flink" % "flink-table-common" % "1.11.2" % "provided"
assembly.sbt:
addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.14.5")
Scala project structure:
hello_scala_for_flink/
project/
src/
main/
resources/
scala/
com.dummy/
dummyTransform
hello_scala
test
target/
build.sbt
shell:
cd hello_scala_for_flink
sbt assembly
cp ./target/scala-2.12/hello_scala_for_flink-assembly-0.1.jar /Users/py-r/opt/anaconda3/envs/venv_pyflink_37/lib/python3.7/site-packages/pyflink/lib/
Python:
from pyflink.dataset import ExecutionEnvironment
exec_env = ExecutionEnvironment.get_execution_environment()
#exec_env = StreamExecutionEnvironment.get_execution_environment()
exec_env.set_parallelism(1)
t_config = TableConfig()
table_env = BatchTableEnvironment.create(exec_env, t_config)
table_env.register_java_function("hash_code","com.dummy.dummyTransform")
Error (incl. after restarting Anaconda):
Py4JJavaError: An error occurred while calling o12.newInstance.
: java.lang.InstantiationException: com.dummy.dummyTransform
at java.base/java.lang.Class.newInstance(Class.java:598)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at java.base/jdk.internal.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at java.base/jdk.internal.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.base/java.lang.reflect.Method.invoke(Method.java:564)
at org.apache.flink.api.python.shaded.py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at org.apache.flink.api.python.shaded.py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at org.apache.flink.api.python.shaded.py4j.Gateway.invoke(Gateway.java:282)
at org.apache.flink.api.python.shaded.py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at org.apache.flink.api.python.shaded.py4j.commands.CallCommand.execute(CallCommand.java:79)
at org.apache.flink.api.python.shaded.py4j.GatewayConnection.run(GatewayConnection.java:238)
at java.base/java.lang.Thread.run(Thread.java:832)
Caused by: java.lang.NoSuchMethodException: com.dummy.dummyTransform.<init>()
at java.base/java.lang.Class.getConstructor0(Class.java:3427)
at java.base/java.lang.Class.newInstance(Class.java:585)
11 more
Versions:
jdk = 1.8.0_151.jdk
scala = 2.12.11
python = 3.7
apache-beam = 2.19.0
apache-flink = 1.11.2
Any idea what the issue might be ?
Thanks for your support
Looks like I found the issue myself. Apparently only a class instantiation was required in above code:
class dummyTransform(factor: Int) extends ScalarFunction {
def eval(s: String): Int = {
s.hashCode() * factor
}
def this() = this(1)
}
Also, I changed to Scala 2.11.12 due to another error. Now everything seems to work: awesome !

SparkSQL MS SQL Server , Get message "No suitable driver" After compiled

build.sbt
name := "BigData"
version := "0.1"
scalaVersion := "2.12.7"
libraryDependencies += "com.github.tototoshi" %% "scala-csv" % "1.3.5"
// https://mvnrepository.com/artifact/org.apache.spark/spark-core
libraryDependencies += "org.apache.spark" %% "spark-core" % "2.4.0"
// https://mvnrepository.com/artifact/org.apache.spark/spark-sql
libraryDependencies += "org.apache.spark" %% "spark-sql" % "2.4.0"
// https://mvnrepository.com/artifact/com.microsoft.sqlserver/mssql-jdbc
libraryDependencies += "com.microsoft.sqlserver" % "mssql-jdbc" % "6.1.0.jre8" % Test
SparkMSSQL.scala
import org.apache.spark.sql.SparkSession
object SparkMSSQL {
def main(args: Array[String]): Unit = {
val spark = SparkSession.
builder.master("local[*]")
.appName("Simple Application")
.getOrCreate()
val url = "jdbc:sqlserver://localhost;databaseName=scalatest;integratedSecurity=true";
// Define database table to load into DataFrame
val jdbcDbTable = "dbo.user_profiles"
val df = spark
.read
.format("jdbc")
.option("url", url)
.option("dbtable", "dbo.user_profiles")
.load()
df.printSchema()
}
}
Error after compile
Exception in thread "main" java.sql.SQLException: No suitable driver
at java.sql.DriverManager.getDriver(DriverManager.java:315)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.$anonfun$driverClass$2(JDBCOptions.scala:105)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions.<init>(JDBCOptions.scala:105)
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:32)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:318)
at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:223)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:211)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:167)
Please advice , what is wrong with my code?
First, you have your jdbc driver in the test scope, so the jar is probably not loaded at runtime. But also, spark needs driver class information to create JDBC connection, so try adding the following option to the DF initializer:
.option("driver", "com.microsoft.sqlserver.jdbc.SQLServerDriver")

How to connect to SQL Server with R?

I want to create a model in R using a connection to data stored in SQL Server datawarehouse.
I tried to use RevoScaleR library which returned
package RevoScaleR is not available (for R version 3.4.1)
so, I edited the connection string (given on the code below) for ODBC library:
install.packages("RevoScaleR")
#require("RevoScaleR")
if (!require("RODBC"))
install.packages("RODBC")
conn <- odbcDriverConnect(connection="Driver={SQL Server Native Client 11.0}; Server=CZPHADDWH01/DEV; Database=DWH_Staging; trusted_connection=true")
sqlWait <- TRUE;
sqlConsoleOutput <- FALSE;
cc <- RxInSqlServer(connectionString = conn, wait = sqlWait)
rxSetComputeContext(cc)
train_query <- "SELECT TOP(10000) * FROM dim.Contract"
formula <- as.formula("Cosi ~ ContractID + ApprovedLoanAmount + ApprovedLoadDuration")
forest_model <- rxDForest(formula = formula,
data = train_query,
nTree = 20,
maxDepth = 32,
mTry = 3,
seed = 5,
verbose = 1,
reportProgress = 1)
rxDForest_model <- as.raw(serialize(forest_model, connection = conn))
lenght(rxDForest_model)
However:
package 'RODBC' successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\sjirak\AppData\Local\Temp\Rtmpqa9iKN\downloaded_packages
Error in odbcDriverConnect(connection = "Driver={SQL Server Native
Client 11.0}; Server=CZPHADDWH01/DEV; Database=DWH_Staging;
trusted_connection=true") : could not find function
"odbcDriverConnect" In library(package, lib.loc = lib.loc,
character.only = TRUE, logical.return = TRUE, : there is no
package called 'RODBC'
Any help would be appreciated.
Looking at the documentation of the ODBC, I see the following functions
odbc-package
dbConnect,OdbcDriver-method
dbUnQuoteIdentifier
odbc
odbc-tables
OdbcConnection
odbcConnectionActions
odbcConnectionIcon
odbcDataType
OdbcDriver
odbcListColumns
odbcListDataSources
odbcListDrivers
odbcListObjects
odbcListObjectTypes
odbcPreviewObject
OdbcResult
odbcSetTransactionIsolationLevel
test_roundtrip
hence I dont see your function in this list. This could be the reason why...
Hence, check the documentation for the proper function.

Flink Streaming: From one window, lookup state in another window

I have two streams:
Measurements
WhoMeasured (metadata about who took the measurement)
These are the case classes for them:
case class Measurement(var value: Int, var who_measured_id: Int)
case class WhoMeasured(var who_measured_id: Int, var name: String)
The Measurement stream has a lot of data. The WhoMeasured stream has little. In fact, for each who_measured_id in the WhoMeasured stream, only 1 name is relevant, so old elements can be discarded if one with the same who_measured_id arrives. This is essentially a HashTable that gets filled by the WhoMeasured stream.
In my custom window function
class WFunc extends WindowFunction[Measurement, Long, Int, TimeWindow] {
override def apply(key: Int, window: TimeWindow, input: Iterable[Measurement], out: Collector[Long]): Unit = {
// Here I need access to the WhoMeasured stream to get the name of the person who took a measurement
// The following two are equivalent since I keyed by who_measured_id
val name_who_measured = magic(key)
val name_who_measured = magic(input.head.who_measured_id)
}
}
This is my job. Now as you might see, there is something missing: The combination of the two streams.
val who_measured_stream = who_measured_source
.keyBy(w => w.who_measured_id)
.countWindow(1)
val measurement_stream = measurements_source
.keyBy(m => m.who_measured_id)
.timeWindow(Time.seconds(60), Time.seconds(5))
.apply(new WFunc)
So in essence this is sort of a lookup table that gets updated when new elements in the WhoMeasured stream arrive.
So the question is: How to achieve such a lookup from one WindowedStream into another?
Follow Up:
After implementing in the way Fabian suggested, the job always fails with some sort of serialization issue:
[info] Loading project definition from /home/jgroeger/Code/MeasurementJob/project
[info] Set current project to MeasurementJob (in build file:/home/jgroeger/Code/MeasurementJob/)
[info] Compiling 8 Scala sources to /home/jgroeger/Code/MeasurementJob/target/scala-2.11/classes...
[info] Running de.company.project.Main dev MeasurementJob
[error] Exception in thread "main" org.apache.flink.api.common.InvalidProgramException: The implementation of the RichCoFlatMapFunction is not serializable. The object probably contains or references non serializable fields.
[error] at org.apache.flink.api.java.ClosureCleaner.clean(ClosureCleaner.java:100)
[error] at org.apache.flink.streaming.api.environment.StreamExecutionEnvironment.clean(StreamExecutionEnvironment.java:1478)
[error] at org.apache.flink.streaming.api.datastream.DataStream.clean(DataStream.java:161)
[error] at org.apache.flink.streaming.api.datastream.ConnectedStreams.flatMap(ConnectedStreams.java:230)
[error] at org.apache.flink.streaming.api.scala.ConnectedStreams.flatMap(ConnectedStreams.scala:127)
[error] at de.company.project.jobs.MeasurementJob.run(MeasurementJob.scala:139)
[error] at de.company.project.Main$.main(Main.scala:55)
[error] at de.company.project.Main.main(Main.scala)
[error] Caused by: java.io.NotSerializableException: de.company.project.jobs.MeasurementJob
[error] at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1184)
[error] at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548)
[error] at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509)
[error] at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432)
[error] at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178)
[error] at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348)
[error] at org.apache.flink.util.InstantiationUtil.serializeObject(InstantiationUtil.java:301)
[error] at org.apache.flink.api.java.ClosureCleaner.clean(ClosureCleaner.java:81)
[error] ... 7 more
java.lang.RuntimeException: Nonzero exit code returned from runner: 1
at scala.sys.package$.error(package.scala:27)
[trace] Stack trace suppressed: run last MeasurementJob/compile:run for the full output.
[error] (MeasurementJob/compile:run) Nonzero exit code returned from runner: 1
[error] Total time: 9 s, completed Nov 15, 2016 2:28:46 PM
Process finished with exit code 1
The error message:
The implementation of the RichCoFlatMapFunction is not serializable. The object probably contains or references non serializable fields.
However, the only field my JoiningCoFlatMap has is the suggested ValueState.
The signature looks like this:
class JoiningCoFlatMap extends RichCoFlatMapFunction[Measurement, WhoMeasured, (Measurement, String)] {
I think what you want to do is a window operation followed by a join.
You can implement the a join of a high-volume stream and a low-value update-by-key stream using a stateful CoFlatMapFunction as in the example below:
val measures: DataStream[Measurement] = ???
val who: DataStream[WhoMeasured] = ???
val agg: DataStream[(Int, Long)] = measures
.keyBy(_._2) // measured_by_id
.timeWindow(Time.seconds(60), Time.seconds(5))
.apply( (id: Int, w: TimeWindow, v: Iterable[(Int, Int, String)], out: Collector[(Int, Long)]) => {
// do your aggregation
})
val joined: DataStream[(Int, Long, String)] = agg
.keyBy(_._1) // measured_by_id
.connect(who.keyBy(_.who_measured_id))
.flatMap(new JoiningCoFlatMap)
// CoFlatMapFunction
class JoiningCoFlatMap extends RichCoFlatMapFunction[(Int, Long), WhoMeasured, (Int, Long, String)] {
var names: ValueState[String] = null
override def open(conf: Configuration): Unit = {
val stateDescrptr = new ValueStateDescriptor[String](
"whoMeasuredName",
classOf[String],
"" // default value
)
names = getRuntimeContext.getState(stateDescrptr)
}
override def flatMap1(a: (Int, Long), out: Collector[(Int, Long, String)]): Unit = {
// join with state
out.collect( (a._1, a._2, names.value()) )
}
override def flatMap2(w: WhoMeasured, out: Collector[(Int, Long, String)]): Unit = {
// update state
names.update(w.name)
}
}
A note on the implementation: A CoFlatMapFunction cannot decide which input to process, i.e., the flatmap1 and flatmap2 functions are called depending on what data arrives at the operator. It cannot be controlled by the function. This is a problem when initializing the state. In the beginning, the state might not have the correct name for an arriving Measurement object but return the default value. You can avoid that by buffering the measurements and joining them once, the first update for the key from the who stream arrives. You'll need another state for that.

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