Is it possible to write to 2nd BigQuery table after writing to 1st has finished in a batch pipeline using Wait.on() method(new feature in Apache Beam 2.4)? The example given in the Apache Beam documentation is:
PCollection<Void> firstWriteResults = data.apply(ParDo.of(...write to first database...));
data.apply(Wait.on(firstWriteResults))
// Windows of this intermediate PCollection will be processed no earlier than when
// the respective window of firstWriteResults closes.
.apply(ParDo.of(...write to second database...));
But why would I write to database from within ParDo? Can we not do the same by using the I/O transforms given in Dataflow?
Thanks.
Yes this is possible, although there are some known limitations and there is currently some work being done to further support this.
In order to make this work you can do something like the following:
WriteResult writeResult = data.apply(BigQueryIO.write()
...
.withMethod(BigQueryIO.Write.Method.STREAMING_INSERTS)
);
data.apply(Wait.on(writeResults.getFailedInserts()))
.apply(...some transform which writes to second database...);
It should be noted that this only works with streaming inserts and wont work with file loads. At the same time there is some work being done currently to better support this use case that you can follow here
Helpful references:
http://moi.vonos.net/cloud/beam-send-pubsub/
http://osdir.com/apache-beam-users/msg02120.html
Related
Is there a way to make the logged user (on superset) to make the queries on impala?
I tried to enable the "Impersonate the logged on user" option on Databases but with no success because all the queries run on impala with superset user.
I'm trying to achieve the same! This will not completely answer this question since it does not still work but I want to share my research in order to maybe help another soul that is trying to use this instrument outside very basic use cases.
I went deep in the code and I found out that impersonation is not implemented for Impala. So you cannot achieve this from the UI. I found out this PR https://github.com/apache/superset/pull/4699 that for whatever reason was never merged into the codebase and tried to copy&paste code in my Superset version (1.1.0) but it didn't work. Adding some logs I can see that the configuration with the impersonation is updated, but then the actual Impala query is with the user I used to start the process.
As you can imagine, I am a complete noob at this. However I found out that the impersonation thing happens when you create a cursor and there is a constructor parameter in which you can pass the impersonation configuration.
I managed to correctly (at least to my understanding) implement impersonation for the SQL lab part.
In the sql_lab.py class you have to add in the execute_sql_statements method the following lines
with closing(engine.raw_connection()) as conn:
# closing the connection closes the cursor as well
cursor = conn.cursor(**database.cursor_kwargs)
where cursor_kwargs is defined in db_engine_specs/impala.py as the following
#classmethod
def get_configuration_for_impersonation(cls, uri, impersonate_user, username):
logger.info(
'Passing Impala execution_options.cursor_configuration for impersonation')
return {'execution_options': {
'cursor_configuration': {'impala.doas.user': username}}}
#classmethod
def get_cursor_configuration_for_impersonation(cls, uri, impersonate_user,
username):
logger.debug('Passing Impala cursor configuration for impersonation')
return {'configuration': {'impala.doas.user': username}}
Finally, in models/core.py you have to add the following bit in the get_sqla_engine def
params = extra.get("engine_params", {}) # that was already there just for you to find out the line
self.cursor_kwargs = self.db_engine_spec.get_cursor_configuration_for_impersonation(
str(url), self.impersonate_user, effective_username) # this is the line I added
...
params.update(self.get_encrypted_extra()) # already there
#new stuff
configuration = {}
configuration.update(
self.db_engine_spec.get_configuration_for_impersonation(
str(url),
self.impersonate_user,
effective_username))
if configuration:
params.update(configuration)
As you can see I just shamelessy pasted the code from the PR. However this kind of works only for the SQL lab as I already said. For the dashboards there is an entirely different way of querying Impala that I did not still find out.
This means that queries for the dashboards are handled in a different way and there isn't something like this
with closing(engine.raw_connection()) as conn:
# closing the connection closes the cursor as well
cursor = conn.cursor(**database.cursor_kwargs)
My gut (and debugging) feeling is that you need to first understand the sqlalchemy part and extend a new ImpalaEngine class that uses a custom cursor with the impersonation conf. Or something like that, however it is not simple (if we want to call this simple) as the sql_lab part. So, the trick is to find out where the query is executed and create a cursor with the impersonation configuration. Easy, isnt'it ?
I hope that this could shed some light to you and the others that have this issue. Let me know if you did find out another way to solve this issue, or if this comment was useful.
Update: something really useful
A colleague of mine succesfully implemented impersonation with impala without touching any superset related, but instead working directly with the impyla lib. A PR was open with the code to change. You can apply the patch directly in the impyla src used by superset. You have to edit both dbapi.py and hiveserver2.py.
As a reminder: we are still testing this and we do not know if it works with different accounts using the same superset instance.
I really liked BigQuery's Data Transfer Service. I have flat files in the exact schema sitting to be loaded into BQ. It would have been awesome to just setup DTS schedule that picked up GCS files that match a pattern and load the into BQ. I like the built in option to delete source files after copy and email in case of trouble. But the biggest bummer is that the minimum interval is 60 minutes. That is crazy. I could have lived with a 10 min delay perhaps.
So if I set up the DTS to be on demand, how can I invoke it from an API? I am thinking create a cronjob that calls it on demand every 10 mins. But I can’t figure out through the docs how to call it.
Also, what is my second best most reliable and cheapest way of moving GCS files (no ETL needed) into bq tables that match the exact schema. Should I use Cloud Scheduler, Cloud Functions, DataFlow, Cloud Run etc.
If I use Cloud Function, how can I submit all files in my GCS at time of invocation as one bq load job?
Lastly, anyone know if DTS will lower the limit to 10 mins in future?
So if I set up the DTS to be on demand, how can I invoke it from an API? I am thinking create a cronjob that calls it on demand every 10 mins. But I can’t figure out through the docs how to call it.
StartManualTransferRuns is part of the RPC library but does not have a REST API equivalent as of now. How to use that will depend on your environment. For instance, you can use the Python Client Library (docs).
As an example, I used the following code (you'll need to run pip install google-cloud-bigquery-datatransfer for the depencencies):
import time
from google.cloud import bigquery_datatransfer_v1
from google.protobuf.timestamp_pb2 import Timestamp
client = bigquery_datatransfer_v1.DataTransferServiceClient()
PROJECT_ID = 'PROJECT_ID'
TRANSFER_CONFIG_ID = '5e6...7bc' # alphanumeric ID you'll find in the UI
parent = client.project_transfer_config_path(PROJECT_ID, TRANSFER_CONFIG_ID)
start_time = bigquery_datatransfer_v1.types.Timestamp(seconds=int(time.time() + 10))
response = client.start_manual_transfer_runs(parent, requested_run_time=start_time)
print(response)
Note that you'll need to use the right Transfer Config ID and the requested_run_time has to be of type bigquery_datatransfer_v1.types.Timestamp (for which there was no example in the docs). I set a start time 10 seconds ahead of the current execution time.
You should get a response such as:
runs {
name: "projects/PROJECT_NUMBER/locations/us/transferConfigs/5e6...7bc/runs/5e5...c04"
destination_dataset_id: "DATASET_NAME"
schedule_time {
seconds: 1579358571
nanos: 922599371
}
...
data_source_id: "google_cloud_storage"
state: PENDING
params {
...
}
run_time {
seconds: 1579358581
}
user_id: 28...65
}
and the transfer is triggered as expected (nevermind the error):
Also, what is my second best most reliable and cheapest way of moving GCS files (no ETL needed) into bq tables that match the exact schema. Should I use Cloud Scheduler, Cloud Functions, DataFlow, Cloud Run etc.
With this you can set a cron job to execute your function every ten minutes. As discussed in the comments, the minimum interval is 60 minutes so it won't pick up files less than one hour old (docs).
Apart from that, this is not a very robust solution and here come into play your follow-up questions. I think these might be too broad to address in a single StackOverflow question but I would say that, for on-demand refresh, Cloud Scheduler + Cloud Functions/Cloud Run can work very well.
Dataflow would be best if you needed ETL but it has a GCS connector that can watch a file pattern (example). With this you would skip the transfer, set the watch interval and the load job triggering frequency to write the files into BigQuery. VM(s) would be running constantly in a streaming pipeline as opposed to the previous approach but a 10-minute watch period is possible.
If you have complex workflows/dependencies, Airflow has recently introduced operators to start manual runs.
If I use Cloud Function, how can I submit all files in my GCS at time of invocation as one bq load job?
You can use wildcards to match a file pattern when you create the transfer:
Also, this can be done on a file-by-file basis using Pub/Sub notifications for Cloud Storage to trigger a Cloud Function.
Lastly, anyone know if DTS will lower the limit to 10 mins in future?
There is already a Feature Request here. Feel free to star it to show your interest and receive updates
Now your can easy manual run transfer Bigquery data use RESTApi:
HTTP request
POST https://bigquerydatatransfer.googleapis.com/v1/{parent=projects/*/locations/*/transferConfigs/*}:startManualRuns
About this part > {parent=projects//locations//transferConfigs/*}, check on CONFIGURATION of your Transfer then notice part like image bellow.
Here
More here:
https://cloud.google.com/bigquery-transfer/docs/reference/datatransfer/rest/v1/projects.locations.transferConfigs/startManualRuns
following the Guillem's answer and the API updates, this is my new code:
import time
from google.cloud.bigquery import datatransfer_v1
from google.protobuf.timestamp_pb2 import Timestamp
client = datatransfer_v1.DataTransferServiceClient()
config = '34y....654'
PROJECT_ID = 'PROJECT_ID'
TRANSFER_CONFIG_ID = config
parent = client.transfer_config_path(PROJECT_ID, TRANSFER_CONFIG_ID)
start_time = Timestamp(seconds=int(time.time()))
request = datatransfer_v1.types.StartManualTransferRunsRequest(
{ "parent": parent, "requested_run_time": start_time }
)
response = client.start_manual_transfer_runs(request, timeout=360)
print(response)
For this to work, you need to know the correct TRANSFER_CONFIG_ID.
In my case, I wanted to list all the BigQuery Scheduled queries, to get a specific ID. You can do it like that :
# Put your projetID here
PROJECT_ID = 'PROJECT_ID'
from google.cloud import bigquery_datatransfer_v1
bq_transfer_client = bigquery_datatransfer_v1.DataTransferServiceClient()
parent = bq_transfer_client.project_path(PROJECT_ID)
# Iterate over all results
for element in bq_transfer_client.list_transfer_configs(parent):
# Print Display Name for each Scheduled Query
print(f'[Schedule Query Name]:\t{element.display_name}')
# Print name of all elements (it contains the ID)
print(f'[Name]:\t\t{element.name}')
# Extract the IDs:
TRANSFER_CONFIG_ID= element.name.split('/')[-1]
print(f'[TRANSFER_CONFIG_ID]:\t\t{TRANSFER_CONFIG_ID}')
# You can print the entire element for debug purposes
print(element)
I need to set variables in root scope in one job to be used in a different job. The first job has a Javascript job entry, with the statements:
parent_job.setVariable("customers_full_path", "C:\\customers22.csv", "r");
true;
But the compilation fails with:
Couldn't compile javascript:
org.mozilla.javascript.EvaluatorException: Can't find method
org.pentaho.di.job.Job.setVariable(string,string,string). (#2)
How to set a variable at root level in a Javascript job entry?
Sorry for the passive agressive but:
I don't know if you are new to Pentaho but, the most common mistake for new users, with previous knowledge of programming, is to be sort of 'addicted' to know methods, as such you are using JavaScript for a functionality that is built in the tool. Both Transformations(KTR) and JOBs(KJB) have a similar step, you can better manipulate this in a KTR.
JavaScript steps slow down the flow considerably, so try to stay away from those as much as possible.
EDIT:
Reading This article, seems the only thing you're doing wrong is the actual syntax of the command..
Correct usage :
parent_job.setVariable("Desired Value", [name_of_variable]);
The command you described has 3 parameters, when it should be 2. If you have more than 1 variable you need to set, use 3 times the command. Try it out see if it works.
I need to write to a non-VSAM dataset in the mainframe. I know that we need to use the ZFile library to do it and I found how to do it here
I am running my Java batch job in the WebSphere Liberty on zOS. How do I specify the dataset? Can I directly give the DataSet a name like this?
dsnFile = new ZFile("X.Y.Z", "wb,type=record,noseek");
I am able to write it to a text file on the server itself using Java's File Writers but I don't know how to access a mvs dataset.
I am relatively new to the world of zOS and mainframe.
It sounds like you might be asking more generally how to use the ZFile API on WebSphere Liberty on z/OS.
Have you tried something like:
String pdsName = ZFile.getSlashSlashQuotedDSN("X.Y.Z");
ZFile zfile = new ZFile(pdsName , ...options...)
As far as batch-specific use cases, you might obviously have to differentiate between writing to a new file that's created for the first time on an original execution, as opposed to appending to an already-existing one on a restart.
You also might find some useful snipopets in this doctorbatch.io repo, along with the original link you posted.
For reference, I'll copy/paste from the ZFile Javadoc:
ZFile dd = new ZFile("//DD:MYDD", "r");
Opens the DD namee MYDD for reading
ZFile dsn = new ZFile("//'SYS1.HELP(ACCOUNT)'", "rt");
Opens the member ACCOUNT from the PDS SYS1.HELP for reading text records
ZFile dsn = new ZFile("//SEQ", "wb,type=record,recfm=fb,lrecl=80,noseek");
Opens the data set {MVS_USER}.SEQ for sequential binary writing. Note that ",noseek" should be specified with "type=record" if access is sequential, since performance is greatly improved.
One final note, another couple useful ZFile helper methods are: bpxwdyn() and getFullyQualifiedDSN().
I use the following command to load data.
/home/bigquery/bq load --max_bad_record=30000 -F '^' company.junelog entry.gz country:STRING,telco_name:STRING,datetime:STRING, ...
It has happened that when I got non-zero return code the data was still loaded. How do I make sure that the command is successful or not? Checking return code does not seem to help. There are times when I loaded the same file again because I got an error but the data was already available in bigquery.
You can use bq show -j of the load job and check job status.
If you are writing code to do the load, so you don't know the job id, you can pass the job id into the load operation (as long as it is unique) so you will know which job to check.
For instance you can run
/home/bigquery/bq load --job_id=some_unique_job_id --max_bad_record=30000 -F '^' company.junelog entry.gz country:STRING,telco_name:STRING,datetime:STRING, ...'
then
/home/bigquery/bq show --j some_unique_job_id
Note if you are creating new tables for every load (as opposed to appending), you could use the write disposition WRITE_EMPTY to make sure you only did the load if the table was empty, thus preventing adding the same data twice. This isn't directly supported in bq.py, but you could use the underlying bigquery_client.py to make this call, or use the REST api directly.