Here's the case:
Our client daily uploads CSVs (overwritten) to a bucket in Google Cloud Storage (each table in a different file).
We use BigQuery as DataSource in DataStudio
We want to automatically transfer the CSVs to BigQuery.
The thing is, even though we've:
Declared the tables in BigQuery with "Overwrite table" write preference option
Configured the daily Transfers vía UI (BigQuery > Transfers) to automatically upload the CSVs from Google Cloud one hour after the files are uploaded to Google Cloud, as stated by the limitations.
The automated transfer/load is by default in "WRITE_APPEND", thus the tables are appended instead of overwritten in BigQuery.
Hence the question: How/where can we change the
configuration.load.writeDisposition = WRITE_TRUNCATE
as stated here in order to overwrite the tables when the CSVs are automatically loaded?
I think that's what we're missing.
Cheers.
None of the above worked for us, so I'm posting this in case anyone has the same issue.
We scheduled a query to erase the table content just before the automatic importation process starts:
DELETE FROM project.tableName WHERE true
And then, new data will be imported to a void table, therefore default "WRITE_APPEND" doesn't affect us.
1) One way to do this is to use DDL to CREATE and REPLACE your table before running the query which imports the data.
This is an example of how to create a table
#standardSQL
CREATE TABLE mydataset.top_words
OPTIONS(
description="Top ten words per Shakespeare corpus"
) AS
SELECT
corpus,
ARRAY_AGG(STRUCT(word, word_count) ORDER BY word_count DESC LIMIT 10) AS top_words
FROM bigquery-public-data.samples.shakespeare
GROUP BY corpus;
Now that it's created you can import your data.
2) Another way is to use BigQuery schedule Queries
3) If you write Python you can find an even better solution here
Related
I have CSV files added to a GCS bucket daily or weekly each file name contains (date + specific parameter)
The files contain the schema (id + name) columns and we need to auto load/ingest these files into a bigquery table so that the final table have 4 columns (id,name,date,specific parameter)
We have tried dataflow templates but we couldn't get the date and specific parameter from the file name to the dataflow
And we tried cloud function (we can get the date and specific parameter value from file name) but couldn't add it in columns while ingestion
Any suggestions?
Disclaimer: I have authored an article for this kind of problem using Cloud Workflows. When you want to extract parts of filename, to use as table definition later.
We will create a Cloud Workflow to load data from Google Storage into BigQuery. This linked article is a complete guide on how to work with workflows, connecting any Google Cloud APIs, working with subworkflows, arrays, extracting segments, and calling BigQuery load jobs.
Let’s assume we have all our source files in Google Storage. Files are organized in buckets, folders, and could be versioned.
Our workflow definition will have multiple steps.
(1) We will start by using the GCS API to list files in a bucket, by using a folder as a filter.
(2) For each file then, we will further use parts from the filename to use in BigQuery’s generated table name.
(3) The workflow’s last step will be to load the GCS file into the indicated BigQuery table.
We are going to use BigQuery query syntax to parse and extract the segments from the URL and return them as a single row result. This way we will have an intermediate lesson on how to query from BigQuery and process the results.
Full article with lots of Code Samples is here: Using Cloud Workflows to load Cloud Storage files into BigQuery
I am porting a java application from Hadoop/Hive to Google Cloud/BigQuery. The application writes avro files to hdfs and then creates Hive external tables with one/multiple partitions on top of the files.
I understand Big Query only supports date/timestamp partitions for now, and no nested partitions.
The way we now handle hive is that we generate the ddl and then execute it with a rest call.
I could not find support for CREATE EXTERNAL TABLE in the BigQuery DDL docs, so I've switched to using the java library.
I managed to create an external table, but I cannot find any reference to partitions in the parameters passed to the call.
Here's a snippet of the code I use:
....
ExternalTableDefinition extTableDef =
ExternalTableDefinition.newBuilder(schemaName, null, FormatOptions.avro()).build();
TableId tableID = TableId.of(dbName, tableName);
TableInfo tableInfo = TableInfo.newBuilder(tableID, extTableDef).build();
Table table = bigQuery.create(tableInfo);
....
There is however support for partitions for non external tables.
I have a few questions questions:
is there support for creating external tables with partition(s)? Can you please point me in the right direction
is loading the data into BigQuery preferred to having it stored in GS avro files?
if yes, how would we deal with schema evolution?
thank you very much in advance
You cannot create partitioned tables over files on GCS, although you can use the special _FILE_NAME pseudo-column to filter out the files that you don't want to read.
If you can, prefer just to load data into BigQuery rather than leaving it on GCS. Loading data is free, and queries will be way faster than if you run them over Avro files on GCS. BigQuery uses a columnar format called Capacitor internally, which is heavily optimized for BigQuery, whereas Avro is a row-based format and doesn't perform as well.
In terms of schema evolution, if you need to change a column type, drop a column, etc., you should recreate your table (CREATE OR REPLACE TABLE ...). If you are only ever adding columns, you can add the new columns using the API or UI.
See also a relevant blog post about lazy data loading.
This is a question about importing data files from Google Cloud Storage to BigQuery.
I have a number of JSON files that follow a strict naming convention to include some key data not included in the JSON data itself.
For example:
xxx_US_20170101.json.gz
xxx_GB_20170101.json.gz
xxx_DE_20170101.json.gz
Which is client_country_date.json.gz At the moment, I have some convoluted processes in a Ruby app that reads the files, appends the additional data and then writes it back to a file that is then imported into a single daily table for the client in BigQuery.
I am wondering if it is possible to grab and parse the filename as part of the import to BigQuery? I could then drop the convoluted Ruby processes which occasionally fail on larger files.
You could define an external table pointing to your files:
Note that the table type is "external table", and that it points to multiple files with the * glob.
Now you can query for all data in these files, and query for the meta-column _FILE_NAME:
#standardSQL
SELECT *, _FILE_NAME filename
FROM `project.dataset.table`
You can now save these results to a new native table.
I'm not a developer so please bear with me on this. I wasn't able to follow the PHP-based answer at Google BigQuery - Automating a Cron Job, so I don't know if that's even the same thing as what I'm looking for.
Anyway, I use Google Cloud to store data, and several times throughout the day data is uploaded into CSVs there. I use BigQuery to run jobs to populate BigQuery tables with the data there.
Because of reasons beyond my control, the CSVs have duplicate data. So what I want to do is basically create a daily ETL to append all new data to the existing tables, perhaps running at 1 am every day:
Identify new files that have not been added (something like date = today - 1)
Run a job on all the CSVs from step 1 to convert them to a temporary BigQuery table
De-dupe the BigQuery table via SQL (I can do this in a variety of ways)
Insert the de-duped temp table into the BigQuery table.
Delete the temp table
So basically I'm stuck at square 1 - I don't know how to do any of this in an automated fashion. I know BigQuery has an API, and there's some documentation on cron jobs, and there's something called Cloud Dataflow, but before going down those rabbit holes I was hoping someone else may have had experience with this and could give me some hints. Like I said, I'm not a developer so if there's a more simplistic way to accomplish this that would be easier for me to run with.
Thanks for any help anyone can provide!
There are a few ways to solve this, but I'd recommend something like this:
Create a templated Dataflow pipeline to read from GCS (source) and write append to BigQuery (sink).
Your pipeline can remove duplicates directly itself. See here and here.
Create a cloud function to monitor your GCS bucket.
When a new file arrives, your cloud function is triggered automatically, which calls your Dataflow pipeline to start reading the new file, deduping it and writing the results to BigQuery.
So no offense to Graham Polley but I ended up using a different approach. Thanks to these pages (and a TON of random Batch file Google searching and trial and error):
how to get yesterday's date in a batch file
https://cloud.google.com/bigquery/bq-command-line-tool
cscript //nologo C:\Desktop\yester.vbs > C:\Desktop\tempvar.txt &&
set /p zvar =< C:\Desktop\tempvar.txt &&
del C:\Desktop\tempvar.txt &&
bq load
--skip_leading_rows=1
data.data_%%zvar:~0,4%%%%zvar:~4,2%%%%zvar:~6,2%%_1
gs://mybucket/data/%%zvar:~0,4%%-%%zvar:~4,2%%-%%zvar:~6,2%%*.csv.gz
Timestamp:TIMESTAMP,TransactionID:STRING &&
bq query --destination_table=data.data_%%zvar:~0,4%%%%zvar:~4,2%%%%zvar:~6,2%%2 "SELECT * FROM data.data%%zvar:~0,4%%%%zvar:~4,2%%%%zvar:~6,2%%_1 group by 1,2" &&
bq cp -a data.data_%%zvar:~0,4%%%%zvar:~4,2%%%%zvar:~6,2%%_2 data.data &&
bq rm -f data.data_%%zvar:~0,4%%%%zvar:~4,2%%%%zvar:~6,2%%_1 &&
bq rm -f data.data_%%zvar:~0,4%%%%zvar:~4,2%%%%zvar:~6,2%%_2
A VB script called yester.vbs prints out yesterday's date in YYYYMMDD format. This is saved as a variable which is used to search for yesterday's data files in GCS and output to a table, from which a de-duped (via grouping by all columns) table is created. This is then appended to the main table, and the two intermediate tables are deleted.
The double percent signs are shown because it's saved as .CMD file and run through Windows Task Scheduler.
I've got jobs/queries that return a few hundred thousand rows. I'd like to get the results of the query and write them as json in a storage bucket.
Is there any straightforward way of doing this? Right now the only method I can think of is:
set allowLargeResults to true
set a randomly named destination table to hold the query output
create a 2nd job to extract the data in the "temporary" destination table to a file in a storage bucket
delete the random "temporary" table.
This just seems a bit messy and roundabout. I'm going to be wrapping all this in a service hooked up to a UI that would have lots of users hitting it and would rather not be in the business of managing all these temporary tables.
1) As you mention the steps are good. You need to use Google Cloud Storage for your export job. Exporting data from BigQuery is explained here, check also the variants for different path syntax.
Then you can download the files from GCS to your local storage.
Gsutil tool can help you further to download the file from GCS to local machine.
With this approach you first need to export to GCS, then to transfer to local machine. If you have a message queue system (like Beanstalkd) in place to drive all these it's easy to do a chain of operation: submit jobs, monitor state of the job, when done initiate export to GCS, then delete the temp table.
Please also know that you can update a table via the API and set the expirationTime property, with this aproach you don't need to delete it.
2) If you use the BQ Cli tool, then you can set output format to JSON, and you can redirect to a file. This way you can achieve some export locally, but it has certain other limits.
this exports the first 1000 line as JSON
bq --format=prettyjson query --n=1000 "SELECT * from publicdata:samples.shakespeare" > export.json