I have a glue job that reads directly from redshift, and to do that, one has to provide connection credentials. I have created an embedded glue connection and can extract the credentials with the following pyspark code. Is there a way to do this in Scala?
glue = boto3.client('glue', region_name='us-east-1')
response = glue.get_connection(
Name='name-of-embedded-connection',
HidePassword=False
)
table = spark.read.format(
'com.databricks.spark.redshift'
).option(
'url',
'jdbc:redshift://prod.us-east-1.redshift.amazonaws.com:5439/db'
).option(
'user',
response['Connection']['ConnectionProperties']['USERNAME']
).option(
'password',
response['Connection']['ConnectionProperties']['PASSWORD']
).option(
'dbtable',
'db.table'
).option(
'tempdir',
's3://config/glue/temp/redshift/'
).option(
'forward_spark_s3_credentials', 'true'
).load()
There is no scala equivalent from AWS to issue this API call.But you can use Java SDK code inside scala as mentioned in this answer.
This is the Java SDK call for getConnection and if you don't want to do this then you can follow below approach:
Create AWS Glue python shell job and retrieve the connection information.
Once you have the values then call the other scala Glue job with these as arguments inside your python shell job as shown below :
glue = boto3.client('glue', region_name='us-east-1')
response = glue.get_connection(
Name='name-of-embedded-connection',
HidePassword=False
)
response = client.start_job_run(
JobName = 'my_scala_Job',
Arguments = {
'--username': response['Connection']['ConnectionProperties']['USERNAME'],
'--password': response['Connection']['ConnectionProperties']['PASSWORD'] } )
Then access these parameters inside your scala job using getResolvedOptions as shown below:
import com.amazonaws.services.glue.util.GlueArgParser
val args = GlueArgParser.getResolvedOptions(
sysArgs, Array(
"username",
"password")
)
val user = args("username")
val pwd = args("password")
Related
I am trying to push records to AWS S3 with Aplakka S3 library. The issue is due to security issues, I have to "assume role", and my IAM user doesn't have access to PUT. with aws cli, I could have successfully pushed to s3 with --profile parameter in aws s3 --profile <profile>. I want to know HOW TO ASSUME ROLE IN ALPAKKA S3 LIBRARY?
my application.conf file has the credentials as in https://github.com/akka/alpakka/blob/v3.0.4/s3/src/main/resources/reference.conf
:
alpakka.s3 {
# default values for AWS configuration
aws {
# to use the same configuration as if credentials.provider = default
credentials {
# static credentials
provider = default //static
access-key-id = <> //valid access key exists in original code
secret-access-key = <>
}
and my PUTing code is:
object Experiments extends App{
//AWS S3 configs
val s3BucketName = config.getString("alpakka.s3.bucket_name")
val s3BucketRegion = config.getString("alpakka.s3.bucket_region")
val bucket_path = config.getString("alpakka.s3.path_inside_bucket")
Source.single(record)
.map { Record => println("Record value: " + Record)
val K_record = //some parsing for the record into JSON
//push to S3
val bucketKey = s"$bucket_path/${K_record.session_id}.json"
try{
Source.single(ByteString(K_record.featureSet))
.runWith(S3.multipartUpload(bucket = s3BucketName, key = bucketKey))
}
catch{
case e2:Exception => println(s"S3 pushing error: ${e2.getMessage}")
}
}
.runWith(Sink.ignore)
I use following test case to write data to a postgresql table, and it works fine.
test("SparkSQLTest") {
val session = SparkSession.builder().master("local").appName("SparkSQLTest").getOrCreate()
val url = "jdbc:postgresql://dbhost:12345/db1"
val table = "schema1.table1"
val props = new Properties()
props.put("user", "user123")
props.put("password", "pass#123")
props.put(JDBCOptions.JDBC_DRIVER_CLASS, "org.postgresql.Driver")
session.range(300, 400).write.mode(SaveMode.Append).jdbc(url, table, props)
}
Then, I use following spark-sql -f sql_script_file.sql to write an hive data into postgresql table.
CREATE OR REPLACE TEMPORARY VIEW tmp_v1
USING org.apache.spark.sql.jdbc
OPTIONS (
driver 'org.postgresql.Driver',
url 'jdbc:postgresql://dbhost:12345/db1',
dbtable 'schema1.table2',
user 'user123',
password 'pass#123',
batchsize '2000'
);
insert into tmp_v1 select
name,
age
from test.person; ---test.person is the Hive db.table
But when I run the above script using spark-sql -f sql_script.sql, it complains that the postgresql user/passord is invalid, the exception is as follows, I think the above two methods are basically the same, so I would ask where the problem is, thanks.
org.postgresql.util.PSQLException: FATAL: Invalid username/password,login denied.
at org.postgresql.core.v3.ConnectionFactoryImpl.doAuthentication(ConnectionFactoryImpl.java:375)
at org.postgresql.core.v3.ConnectionFactoryImpl.openConnectionImpl(ConnectionFactoryImpl.java:189)
at org.postgresql.core.ConnectionFactory.openConnection(ConnectionFactory.java:64)
at org.postgresql.jdbc2.AbstractJdbc2Connection.<init>(AbstractJdbc2Connection.java:124)
at org.postgresql.jdbc3.AbstractJdbc3Connection.<init>(AbstractJdbc3Connection.java:28)
at org.postgresql.jdbc3g.AbstractJdbc3gConnection.<init>(AbstractJdbc3gConnection.java:20)
at org.postgresql.jdbc4.AbstractJdbc4Connection.<init>(AbstractJdbc4Connection.java:30)
at org.postgresql.jdbc4.Jdbc4Connection.<init>(Jdbc4Connection.java:22)
at org.postgresql.Driver.makeConnection(Driver.java:392)
at org.postgresql.Driver.connect(Driver.java:266)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$createConnectionFactory$1.apply(JdbcUtils.scala:59)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils$$anonfun$createConnectionFactory$1.apply(JdbcUtils.scala:50)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.resolveTable(JDBCRDD.scala:58)
at org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation.<init>(JDBCRelation.scala:114)
at org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:45)
at org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:330)
at org.apache.spark.sql.execution.datasources.CreateTempViewUsing.run(ddl.scala:76)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:59)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:57)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:75)
I'm trying to connect to IBM Cloud Object Storage from IBM Data Science Experience:
access_key = 'XXX'
secret_key = 'XXX'
bucket = 'mybucket'
host = 'lon.ibmselect.objstor.com'
service = 'mycos'
sqlCxt = SQLContext(sc)
hconf = sc._jsc.hadoopConfiguration()
hconf.set('fs.cos.myCos.access.key', access_key)
hconf.set('fs.cos.myCos.endpoint', 'http://' + host)
hconf.set('fs.cose.myCos.secret.key', secret_key)
hconf.set('fs.cos.service.v2.signer.type', 'false')
obj = 'mydata.tsv.gz'
rdd = sc.textFile('cos://{0}.{1}/{2}'.format(bucket, service, obj))
print(rdd.count())
This returns:
Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: java.io.IOException: No FileSystem for scheme: cos
I'm guessing I need to use the 'cos' scheme based on the stocator docs. However, the error suggests stocator isn't available or is an old version?
Any ideas?
Update 1:
I have also tried the following:
sqlCxt = SQLContext(sc)
hconf = sc._jsc.hadoopConfiguration()
hconf.set('fs.cos.impl', 'com.ibm.stocator.fs.ObjectStoreFileSystem')
hconf.set('fs.stocator.scheme.list', 'cos')
hconf.set('fs.stocator.cos.impl', 'com.ibm.stocator.fs.cos.COSAPIClient')
hconf.set('fs.stocator.cos.scheme', 'cos')
hconf.set('fs.cos.mycos.access.key', access_key)
hconf.set('fs.cos.mycos.endpoint', 'http://' + host)
hconf.set('fs.cos.mycos.secret.key', secret_key)
hconf.set('fs.cos.service.v2.signer.type', 'false')
service = 'mycos'
obj = 'mydata.tsv.gz'
rdd = sc.textFile('cos://{0}.{1}/{2}'.format(bucket, service, obj))
print(rdd.count())
However, this time the response was:
Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.collectAndServe.
: java.io.IOException: No object store for: cos
at com.ibm.stocator.fs.ObjectStoreVisitor.getStoreClient(ObjectStoreVisitor.java:121)
...
Caused by: java.lang.ClassNotFoundException: com.ibm.stocator.fs.cos.COSAPIClient
The latest version of Stocator (v1.0.9) that supports fs.cos scheme is not yet deployed on Spark aaService (It will be soon). Please use the stocator scheme "fs.s3d" to connect to your COS.
Example:
endpoint = 'endpointXXX'
access_key = 'XXX'
secret_key = 'XXX'
prefix = "fs.s3d.service"
hconf = sc._jsc.hadoopConfiguration()
hconf.set(prefix + ".endpoint", endpoint)
hconf.set(prefix + ".access.key", access_key)
hconf.set(prefix + ".secret.key", secret_key)
bucket = 'mybucket'
obj = 'mydata.tsv.gz'
rdd = sc.textFile('s3d://{0}.service/{1}'.format(bucket, obj))
rdd.count()
Alternatively, you can use ibmos2spark. The lib is already installed on our service. Example:
import ibmos2spark
credentials = {
'endpoint': 'endpointXXXX',
'access_key': 'XXXX',
'secret_key': 'XXXX'
}
configuration_name = 'os_configs' # any string you want
cos = ibmos2spark.CloudObjectStorage(sc, credentials, configuration_name)
bucket = 'mybucket'
obj = 'mydata.tsv.gz'
rdd = sc.textFile(cos.url(obj, bucket))
rdd.count()
Stocator is on the classpath for Spark 2.0 and 2.1 kernels, but the cos scheme is not configured. You can access the config by executing the following in a Python notebook:
!cat $SPARK_CONF_DIR/core-site.xml
Look for the property fs.stocator.scheme.list. What I currently see is:
<property>
<name>fs.stocator.scheme.list</name>
<value>swift2d,swift,s3d</value>
</property>
I recommend that you raise a feature request against DSX to support the cos scheme.
It looks like cos driver is not properly initialized. Try this configuration:
hconf.set('fs.cos.impl', 'com.ibm.stocator.fs.ObjectStoreFileSystem')
hconf.set('fs.stocator.scheme.list', 'cos')
hconf.set('fs.stocator.cos.impl', 'com.ibm.stocator.fs.cos.COSAPIClient')
hconf.set('fs.stocator.cos.scheme', 'cos')
hconf.set('fs.cos.mycos.access.key', access_key)
hconf.set('fs.cos.mycos.endpoint', 'http://' + host)
hconf.set('fs.cos.mycos.secret.key', secret_key)
hconf.set('fs.cos.service.v2.signer.type', 'false')
UPDATE 1:
You also need to ensure stocator classes are on the classpath. You can use packages system by exceuting pyspark in the following way:
./bin/pyspark --packages com.ibm.stocator:stocator:1.0.24
This works with swift2d and cos scheme.
UPDATE 2:
Just follow Stocator documentation (https://github.com/CODAIT/stocator). It contains all details how to install it, what branch to use, etc.
I found the same issue, and to solve it I just changed environment:
Within IBM Watson Studio, if you start a a Jupyter notebook in an environment without a pre-configured spark cluster, than you get that error. Installing PySpark is not enough.
Instead, if you start a notebook with the Spark cluster available, you will be just fine.
You have to set .config("spark.hadoop.fs.stocator.scheme.list", "cos") along with some others fs.cos... configurations.
Here's an end-to-end snippet code example that works (tested with pyspark==2.3.2 and Python 3.7.3):
from pyspark.sql import SparkSession
stocator_jar = '/path/to/stocator-1.1.2-SNAPSHOT-IBM-SDK.jar'
cos_instance_name = '<myCosIntanceName>'
bucket_name = '<bucketName>'
s3_region = '<region>'
cos_iam_api_key = '*******'
iam_servicce_id = 'crn:v1:bluemix:public:iam-identity::<****************>'
spark_builder = (
SparkSession
.builder
.appName('test_app'))
spark_builder.config('spark.driver.extraClassPath', stocator_jar)
spark_builder.config('spark.executor.extraClassPath', stocator_jar)
spark_builder.config(f"fs.cos.{cos_instance_name}.iam.api.key", cos_iam_api_key)
spark_builder.config(f"fs.cos.{cos_instance_name}.endpoint", f"s3.{s3_region}.cloud-object-storage.appdomain.cloud")
spark_builder.config(f"fs.cos.{cos_instance_name}.iam.service.id", iam_servicce_id)
spark_builder.config("spark.hadoop.fs.stocator.scheme.list", "cos")
spark_builder.config("spark.hadoop.fs.cos.impl", "com.ibm.stocator.fs.ObjectStoreFileSystem")
spark_builder.config("fs.stocator.cos.impl", "com.ibm.stocator.fs.cos.COSAPIClient")
spark_builder.config("fs.stocator.cos.scheme", "cos")
spark_sess = spark_builder.getOrCreate()
dataset = spark_sess.range(1, 10)
dataset = dataset.withColumnRenamed('id', 'user_idx')
dataset.repartition(1).write.csv(
f'cos://{bucket_name}.{cos_instance_name}/test.csv',
mode='overwrite',
header=True)
spark_sess.stop()
print('done!')
For test purpose, I would like to use BigQuery Connector to write Parquet Avro logs in BigQuery. As I'm writing there is no way to read directly Parquet from the UI to ingest it so I'm writing a Spark job to do so.
In Scala, for the time being, job body is the following:
val events: RDD[RichTrackEvent] =
readParquetRDD[RichTrackEvent, RichTrackEvent](sc, googleCloudStorageUrl)
val conf = sc.hadoopConfiguration
conf.set("mapred.bq.project.id", "myproject")
// Output parameters
val projectId = conf.get("fs.gs.project.id")
val outputDatasetId = "logs"
val outputTableId = "test"
val outputTableSchema = LogSchema.schema
// Output configuration
BigQueryConfiguration.configureBigQueryOutput(
conf, projectId, outputDatasetId, outputTableId, outputTableSchema
)
conf.set(
"mapreduce.job.outputformat.class",
classOf[BigQueryOutputFormat[_, _]].getName
)
events
.mapPartitions {
items =>
val gson = new Gson()
items.map(e => gson.fromJson(e.toString, classOf[JsonObject]))
}
.map(x => (null, x))
.saveAsNewAPIHadoopDataset(conf)
As the BigQueryOutputFormat isn't finding the Google Credentials, it fallbacks on metadata host to try to discover them with the following stacktrace:
016-06-13 11:40:53 WARN HttpTransport:993 - exception thrown while executing request
java.net.UnknownHostException: metadata
at java.net.AbstractPlainSocketImpl.connect(AbstractPlainSocketImpl.java:184)
at java.net.SocksSocketImpl.connect(SocksSocketImpl.java:392)
at java.net.Socket.connect(Socket.java:589 at sun.net.NetworkClient.doConnect(NetworkClient.java:175)
at sun.net.www.http.HttpClient.openServer(HttpClient.java:432)
at sun.net.www.http.HttpClient.openServer(HttpClient.java:527)
at sun.net.www.http.HttpClient.<init>(HttpClient.java:211)
at sun.net.www.http.HttpClient.New(HttpClient.java:308)
at sun.net.www.http.HttpClient.New(HttpClient.java:326)
at sun.net.www.protocol.http.HttpURLConnection.getNewHttpClient(HttpURLConnection.java:1169)
at sun.net.www.protocol.http.HttpURLConnection.plainConnect0(HttpURLConnection.java:1105)
at sun.net.www.protocol.http.HttpURLConnection.plainConnect(HttpURLConnection.java:999)
at sun.net.www.protocol.http.HttpURLConnection.connect(HttpURLConnection.java:933)
at com.google.api.client.http.javanet.NetHttpRequest.execute(NetHttpRequest.java:93)
at com.google.api.client.http.HttpRequest.execute(HttpRequest.java:972)
at com.google.cloud.hadoop.util.CredentialFactory$ComputeCredentialWithRetry.executeRefreshToken(CredentialFactory.java:160)
at com.google.api.client.auth.oauth2.Credential.refreshToken(Credential.java:489)
at com.google.cloud.hadoop.util.CredentialFactory.getCredentialFromMetadataServiceAccount(CredentialFactory.java:207)
at com.google.cloud.hadoop.util.CredentialConfiguration.getCredential(CredentialConfiguration.java:72)
at com.google.cloud.hadoop.io.bigquery.BigQueryFactory.createBigQueryCredential(BigQueryFactory.java:81)
at com.google.cloud.hadoop.io.bigquery.BigQueryFactory.getBigQuery(BigQueryFactory.java:101)
at com.google.cloud.hadoop.io.bigquery.BigQueryFactory.getBigQueryHelper(BigQueryFactory.java:89)
at com.google.cloud.hadoop.io.bigquery.BigQueryOutputCommitter.<init>(BigQueryOutputCommitter.java:70)
at com.google.cloud.hadoop.io.bigquery.BigQueryOutputFormat.getOutputCommitter(BigQueryOutputFormat.java:102)
at com.google.cloud.hadoop.io.bigquery.BigQueryOutputFormat.getOutputCommitter(BigQueryOutputFormat.java:84)
at com.google.cloud.hadoop.io.bigquery.BigQueryOutputFormat.getOutputCommitter(BigQueryOutputFormat.java:30)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1.apply$mcV$sp(PairRDDFunctions.scala:1135)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1.apply(PairRDDFunctions.scala:1078)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$saveAsNewAPIHadoopDataset$1.apply(PairRDDFunctions.scala:1078)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:357)
at org.apache.spark.rdd.PairRDDFunctions.saveAsNewAPIHadoopDataset(PairRDDFunctions.scala:1078)
It is of course expected but it should be able to use my service account and its key as GoogleCredential.getApplicationDefault() returns appropriate credentials fetched from GOOGLE_APPLICATION_CREDENTIALS environment variable.
As the connector seems to read credentials, from hadoop configuration, what's the keys to set so that it reads GOOGLE_APPLICATION_CREDENTIALS ? Is there a way to configure the output format to use a provided GoogleCredential object ?
If I understand your question correctly - you might want to set:
<name>mapred.bq.auth.service.account.enable</name>
<name>mapred.bq.auth.service.account.email</name>
<name>mapred.bq.auth.service.account.keyfile</name>
<name>mapred.bq.project.id</name>
<name>mapred.bq.gcs.bucket</name>
Here, the mapred.bq.auth.service.account.keyfile should point to the full file path to the older-style "P12" keyfile; alternatively, if you're using the newer "JSON" keyfiles, you should replace the "email" and "keyfile" entries with the single mapred.bq.auth.service.account.json.keyfile key:
<name>mapred.bq.auth.service.account.enable</name>
<name>mapred.bq.auth.service.account.json.keyfile</name>
<name>mapred.bq.project.id</name>
<name>mapred.bq.gcs.bucket</name>
Also you might want to take a look at https://github.com/spotify/spark-bigquery - which is much more civilised way of working with BQ and Spark. The setGcpJsonKeyFile method used in this case is the same JSON file you'd set for mapred.bq.auth.service.account.json.keyfile if using the BQ connector for Hadoop.
I'm using the following Scala code (as a custom spark-submit wrapper) to submit a Spark application to a YARN cluster:
val result = Seq(spark_submit_script_here).!!
All I have at the time of submission is spark-submit and the Spark application's jar (no SparkContext). I'd like to capture applicationId from result, but it's empty.
I can see in my command line output the applicationId and rest of the Yarn messages:
INFO yarn.Client: Application report for application_1450268755662_0110
How can I read it within code and get the applicationId ?
As stated in the Spark issue 5439, you could either use SparkContext.applicationId or parse the stderr output. Now, as you are wrapping the spark-submit command with your own script/object, I would say you need to read the stderr and get the application id.
If you are submitting the job via Python, then this is how you can get the yarn application id:
cmd_list = [{
'cmd': '/usr/bin/spark-submit --name %s --master yarn --deploy-mode cluster '
'--executor-memory %s --executor-cores %s --num-executors %s '
'--class %s %s %s'
% (
app_name,
config.SJ_EXECUTOR_MEMORY,
config.SJ_EXECUTOR_CORES,
config.SJ_NUM_OF_EXECUTORS,
config.PRODUCT_SNAPSHOT_SKU_PRESTO_CLASS,
config.SPARK_JAR_LOCATION,
config.SPARK_LOGGING_ENABLED
),
'cwd': config.WORK_DIR
}]
cmd_output = subprocess.run(cmd_obj['cmd'], shell=True, check=True, cwd=cwd, stderr=subprocess.PIPE)
cmd_output = cmd_output.stderr.decode("utf-8")
yarn_application_ids = re.findall(r"application_\d{13}_\d{4}", cmd_output)
if len(yarn_application_ids):
yarn_application_id = yarn_application_ids[0]
yarn_command = "yarn logs -applicationId " + yarn_application_id
Use the spark context to get application info.
sc.getConf.getAppId
res7: String = application_1532296406128_16555
as Rajiv's answer , the regex 'application_\d{13}_\d{4}' is not correct
actualy, the job id will increase greater than 9999,
so the regex of application_\d{13}_\d{4,} will just working
and the java code
public static final String APPLICATION_REGEX="application_\\d+_\\d{4,}+";
/**
* get yarn application id list
* #param log log content
* #return app id list
*/
public static List<String> getAppIds(String log) {
List<String> appIds = new ArrayList<>();
Matcher matcher = APPLICATION_REGEX.matcher(log);
while (matcher.find()) {
String appId = matcher.group();
if(!appIds.contains(appId)){
appIds.add(appId);
}
}
return appIds;
}