I've been asked to implement a way to load data to my datasets once a month. As Power BI Service doesn't have this option, I had to find a solution using Power Query and bellow I describe the step-by-step of my solution.
If it helps you at some way, please, let me know by posting a comment bellow. If you have a better and/or more elegant solution I'm glad to hear from you.
So, as my first solution didn't work, here I'll post the definity solution that we (me and my colleges) found.
I have to say that this solution is not so simple as it uses a Linux server, Gitlab and Jenkins, so it require a relative complex environment and I'll not describe how to build it.
At the end, I'll suggest a simpler solution.
THE ENVIRONMENT
On my company we use Jenkins to schedule jobs, Gitlab to store source code and we have a Linux Server to execute small tasks using Shell Script. For this problem I used all three services besides Power BI API.
JENKINS
I use Jenkins to schedule a job that run montlhy. This job was created using the following configs:
Parameters: I created 2 parameters (workspace_id and dataset_id) so I can test the script at any environment (Power BI Workspace) by just changing the value of those parameters;
Schedule Job: this job was schedule to run every day 1 at 02:00 a.m. As Jenkins uses the same sintax as CRON (I thing it is just a intermediate between you and CRON) the value of this field is 0 2 1 * *.
Build: as here we have a remote linux server to execute the scripts, I used a Execute shell script on remote host using ssh. I don't know why on Jenkins you can not execute the curl command direct on the job, it just didn't work, so I had to split the solution into both Jenkins and Linux server. At SSH site you have to select the credentials (previously created by my team) and at command are the commands bellow:
#Navigate to the script shell directory
cd "script-shell-script/"
# pulls the last version of the script. If you aren't using Gitlab,
# remove this command
git pull
# every time git pulls a new file version, it has read access.
# This command allows the execution of the filechmod +x powerbi_refresh_dataset.sh
# make a call to the file passing as parameter the workspace id and dataset id
./powerbi_refresh_dataset.sh $ID_WORKSPACE $ID_DATASET
SHELL SCIPT
As you already imagine, the core solution is the content of powerbi_refresh_dataset.sh. But, before going, there, you must understand how Power BI API works and you have to configure your Power BI environment to make API calls work. So, please, make sure that you already have your Principal Service properly configured by following this tutorial: https://learn.microsoft.com/en-us/power-bi/developer/embedded/embed-service-principal
Once you got your object_id, client_id and client_secret you can create your shell script file. Bellow is the code of my .sh file.
# load OBJECT_ID, CLIENT_ID and CLIENT_SECRET as environment variables
source credential_file.sh
# This command retrieves a new token from Microsoft Credentials Manager
token_msg=$(curl -X POST "https://login.windows.net/$OBJECT_ID/oauth2/token" \
-H 'Content-Type: application/x-www-form-urlencoded' \
-H 'Accept: application/json' \
-d 'grant_type=client_credentials&resource=https://analysis.windows.net/powerbi/api&client_id='$CLIENT_ID'&client_secret='$CLIENT_SECRET
)
# Extract the token from the response message
token=$(echo "$token_msg" | jq -r '.access_token')
# Ask Power BI to refresh dataset
refresh_msg=$(curl -X POST 'https://api.powerbi.com/v1.0/myorg/groups/'$1'/datasets/'$2'/refreshes' \
-H 'Authorization: Bearer '$token \
-H 'Content-Type: application/json' \
-d '{"notifyOption": "NoNotification"}')
And here goes some explanation. The first command is source credential_file.sh which loads 3 variables (OBJECT_ID, CLIENT_ID and CLIENT_SECRET). The intention here is to separate confidential info from the script so I can store the main script file on a version control (Git) and not disclosure any sensitivy information. So, besides powerbi_refresh_dataset.sh file you must have credential_file.sh at the same directory and with the following content:
OBJECT_ID=OBJECT_ID_VALUE
CLIENT_ID=CLIENT_ID_VALUE
CLIENT_SECRET=CLIENT_SECRET_VALUE
It's important to say that if you are using Git or any other version control, only powerbi_refresh_dataset.sh file goes to version control and credential_file.sh file must remain only at your Linux Server. I suggest you to save it's content into a password store application like keepass, as CLIENT_SECRET is not possible to retrieve.
FINAL CONSIDERATIONS
So above is the most relevant info of my solution. As you can see I'm ommiting (intentionally) how to build the environment and make them talk (jekins with linux, jenkins with Git and so on).
If all you have is a Linux or Windows host, I suggest you this:
Linux Host
On this simpler environment, just create the powerbi_refresh_dataset.sh and credential_file.sh, place it at any directory and create a CRON task to call powerbi_refresh_dataset as many time as you wish.
Windows Host
On windows you can do almost the same as on Linux, but you'll have to replace the content of shell script file by Power Shell command (google it) and use the Scheduled Task to regularly execute you Power Shell file.
Well, I think this would help you. I know it's not a complete answer as it will only works if you have a similar environment, but I hope that the final tips might help you.
Best regards
The Solution
First let me resume the solution. I just putted a condition execution at the end of each query that checks if today is the day where new data must be uploaded or not. If yes, it returns the step to be executed, if not, it raises a error.
There is many ways to implement that and I'll go from the simplest form to the more complex one.
Simplest Version: checking if it's the day to load new data directly at the query
This is the simplest way to implement the solution, but, depending on your dataset it may not be the smartest one.
Lets say you have this foo query:
let
step1 = ...,
...,
...,
step10 = SomeFunction(Somevariable, someparameter)
in
setp10
Now lets pretend you want that query to upload new data just on 1st day of the month. To do that, you just insert a condicional struction at in clause.
let
step1 = ...,
...,
...,
step10 = SomeFunction(Somevariable, someparameter)
in
if Date.Day(DateTime.LocalNow()) = 1 then setp10 else error "Today is not the day to load data"
At this example I just replaced the setp10 at the return of the query by this piece of code:if Date.Day(DateTime.LocalNow()) = 1 then setp10 else error "Today is not the day to load data". By doing that, setp10 will be the result of this query only if this query is been executed at day 1st of the month, otherwise, it will return a error.
And here it's worthy some explanation. Power Query is not a script language that runs at the same order that it's declared. So the fact the condicional statement was placed at the end of the query doesn't mean that all code above will be executed before the error is launched. As Power Query just executes what's necessary, the if... statement it will probably be the first one to be executed. For more info about how Power Query works behind the scene, I stronlgy recomend you this reading: https://bengribaudo.com/blog/2018/02/28/4391/power-query-m-primer-part5-paradigm
Using function
Now lets move foward. Lets say that your Dataset set has not only one, but many queries and all of them needs to be executed only once a month. In this case, a smart way to do that is by using what all other programming languages have to reuse block of code: create a function!
For this, create a new Blank Query and paste this code on its body:
(step) =>
let
result = if Date.Day(DateTime.LocalNow()) = 1 then step else error "Today is not the day to load data"
in
result
Now, at each query you'll call this function, sending the last setp as parameter. The function will check which day is today and return the same step passed as parameter if it's the day to load the data. Otherwise, it will return the error.
Bellow is the code of our query using our function called check_if_upload
let
step1 = ...,
...,
...,
step10 = SomeFunction(Somevariable, someparameter)
step11 = check_if_upload(step10)
in
step11
Using parameters
One final tip. As your query raises a error if today is not the day to upload day, it means that you can only test your ETL once a month, right? The error message also limite you to save you Power Query, which means that if you don't apply the modifications you can't upload the new Power Query version (having this implementations) to Power BI Service.
Well, you could change the value of the day verification into the function, but it's let's say, a little dummy.
A more ellegante way to change this parameter is by using parameters. So, lets do it. Create a parameter (I'll call it Upload Day) as a number type. Now, all you have to do is use this parameter at your function. It will look like this:
(step) =>
let
result = if Date.Day(DateTime.LocalNow()) = #"Upload Day" then step else error "Today is not the day to load data"
in
result
That's it. Now you can change the upload day directly at Power BI Service, just changing this parameter at the dataset (click on dataset name and goes to Settings >> Parameters).
Hope you neiled it and that its helpful for you.
Best regards.
I am trying to run an Informatica workflow to check database table and write fetched data to a flat file on the server.
source: database table
target: flat file
transformations: non
this wf runs fine when "run on demand" but I need to run this continuously so I tried with INFA scheduler to run it every 5 minutes. When the scheduler is enabled the workflow continuously fails. Kindly help with any ideas to run this on scheduler.
oops... sorted, this was my mistake. I have not checked in the Target I created for flat file. my bad. Thanks all
I am in the process of migrating a database from an external server to cloud sql 2nd gen. Have been following the recommended steps and the 2TB mysqlsump process was complete and replication started. However, got an error:
'Error ''Access denied for user ''skip-grants user''#''skip-grants host'' (using password: NO)'' on query. Default database: ''mondovo_db''. Query: ''LOAD DATA INFILE ''/mysql/tmp/SQL_LOAD-0a868f6d-8681-11e9-b5d3-42010a8000a8-6498057-322806.data'' IGNORE INTO TABLE seoi_volume_update_tracker FIELDS TERMINATED BY ''^#^'' ENCLOSED BY '''' ESCAPED BY ''\'' LINES TERMINATED BY ''^|^'' (keyword_search_volume_id)'''
2 questions,
1) I'm guessing the error has come about because cloud sql requires LOAD DATA LOCAL INFILE instead of LOAD DATA INFILE? However am quite sure on the master we run only LOAD DATA LOCAL INFILE so not sure how it changes to remove LOCAL while in replication, is that possible?
2) I can't stop the slave to skip the error and restart since SUPER privileges aren't available and so am not sure how to skip this error and also avoid it for the future while the the final sync happens. Suggestions?
There was no way to work around the slave replication error in Google Cloud SQL, so had to come up with another way.
Since replication wasn't going to work, I had to do a copy of all the databases. However, because of the aggregate size of all my DBs being at 2TB, it was going to take a long time.
The final strategy that took the least amount of time:
1) Pre-requisite: You need to have at least 1.5X the amount of current database size in terms of disk space remaining on your SQL drive. So my 2TB DB was on a 2.7TB SSD, I needed to eventually move everything temporarily to a 6TB SSD before I could proceed with the steps below. DO NOT proceed without sufficient disk space, you'll waste a lot of your time as I did.
2) Install cloudsql-import on your server. Without this, you can't proceed and this took a while for me to discover. This will facilitate in the quick transfer of your SQL dumps to Google.
3) I had multiple databases to migrate. So if in a similar situation, pick one at a time and for the sites that access that DB, prevent any further insertions/updates. I needed to put a "Website under Maintenance" on each site, while I executed the operations outlined below.
4) Run the commands in the steps below in a separate screen. I launched a few processes in parallel on different screens.
screen -S DB_NAME_import_process
5) Run a mysqldump using the following command and note, the output is an SQL file and not a compressed file:
mysqldump {DB_NAME} --hex-blob --default-character-set=utf8mb4 --skip-set-charset --skip-triggers --no-autocommit --single-transaction --set-gtid-purged=off > {DB_NAME}.sql
6) (Optional) For my largest DB of around 1.2TB, I also split the DB backup into individual table SQL files using the script mentioned here: https://stackoverflow.com/a/9949414/1396252
7) For each of the files dumped, I converted the INSERT commands into INSERT IGNORE because didn't want any further duplicate errors during the import process.
cat {DB_OR_TABLE_NAME}.sql | sed s/"^INSERT"/"INSERT IGNORE"/g > new_{DB_OR_TABLE_NAME}_ignore.sql
8) Create a database by the same name on Google Cloud SQL that you want to import. Also create a global user that has permission to access all the databases.
9) Now, we import the SQL files using the cloudsql-import plugin. If you split the larger DB into individual table files in Step 6, use the cat command to combine a batch of them into a single file and make as many batch files as you see appropriate.
Run the following command:
cloudsql-import --dump={DB_OR_TABLE_NAME}.sql --dsn='{DB_USER_ON_GLCOUD}:{DB_PASSWORD}#tcp({GCLOUD_SQL_PUBLIC_IP}:3306)/{DB_NAME_CREATED_ON_GOOGLE}'
10) While the process is running, you can step out of the screen session using Ctrl+a
+ Ctrl+d (or refer here) and then reconnect to the screen later to check on progress. You can create another screen session and repeat the same steps for each of the DBs/batches of tables that you need to import.
Because of the large sizes that I had to import, I believe it did take me a day or two, don't remember now since it's been a few months but I know that it's much faster than any other way. I had tried using Google's copy utility to copy the SQL files to Cloud Storage and then use Cloud SQL's built-in visual import tool but that was slow and not as fast as cloudsql-import. I would recommend this method up until Google fixes the ability to skip slave errors.
I have a Hive script I'm running in EMR that is creating a partitioned Parquet table in S3 from a ~40GB gzipped CSV file also stored in S3.
The script runs fine for about 4 hours but reaches a point (pretty sure when it is just about done creating the Parquet table) where it errors out. The logs show that the error is:
HiveException: Hive Runtime Error while processing row
caused by:
AmazonS3Exception: Bad Request
There really isn't any more useful information in the logs that I can see. It is reading the CSV file fine from S3 and it creates a couple metadata files in S3 fine as well, so I've confirmed the instance has read/write permissions to the Bucket.
I really can't think of anything else that's going on and I wish there was more info in the logs about what "Bad Request" to S3 that Hive is making. Anyone have any ideas?
BadRequest is a fairly meaningless response from AWS which it sends if there is any reason why it doesn't like the caller. Nobody really knows what's happening.
The troubleshooting docs for the ASF S3A connector list some causes, but they aren't complete, and based on guesswork from what made the message go away.
If you have the request ID which failed, you can submit a support request for amazon to see what they saw on their side.
If it makes you feel any better, I'm seeing it when I try to list exactly one directory in an object store, and I'm co-author of the s3a connector. Like I said "guesswork". Once you find out, add a comment here or, if it's not in the troubleshooting doc, submit a patch to hadoop on the topic.
I'm executing a query in AWS Athena and writing the results to s3. It seems like it's taking a long time (way too long in fact) for the file to be available when I execute the query from a lambda script.
I'm scanning 70MB of data, and the file returned is 12MB. I execute this from a lambda script like so:
athena_client = boto3.client('athena')
athena_client.start_query_execution(
QueryString=query_string,
ResultConfiguration={
'OutputLocation': 'location_on_s3',
'EncryptionConfiguration': 'SSE_S3',
}
)
If I run the query directly in Athena it takes 2.97 seconds to run. However it looks like the file is available after 2 minutes if I run this query from the lambda script.
Does anyone know the write performance of AWS Athena to AWS S3? I would like to know if this is normal. The docs don't state how quickly the write occurs.
Every query in Athena writes to S3.
If you check the History tab on the Athena page in the console you'll see a history of all queries you've run (not just through the console, but generally). Each of those has a link to a download path.
If you click the Settings button a dialog will open asking you to specify an output location. Check that location and you'll find all your query results there.
Why is this taking so much longer from your Lambda script? I'm guessing, but the only possible suggestion I have is that you're querying across regions - if your data is in your region and your result location is in another location you might experience slowness due to transfer cost. Even so, 12MB should be fast.