I have data coming from multiple hotels. These hotels are not using the same naming convention for storing the order information. I have a predefined dataset created in the bigquery(called hotel_order). I
want to map the data coming from different hotels to the single dataset in GCP, so it is easier for me to do comparisons in the bigquery.
If the column name(from hotel1) matches the bigquery dataset columnname, then the bigquery should load the data in the column, if the columnnames (from hotel orders data and dataset in bigquery) don't match, then column in the bigquery should have the null value. How do I do implement this in GCP? Problem of mapping in the GCP?
If you want to join tables together, and show a null value when a match doesn't exist, then you can do so using 'left join'.
Rough example
from hotel.orders as main left join hotel_number_one as Hotel_One on main.order_information = Hotel_One.order_information
Difficult to give a more detailed answer without more details or a working example using dbfiddle.
Related
I want to find out what my table sizes are (in BigQuery).
However I want to sum up the size of of all tables that belong to a specific set of sharded tables.
So I need to find metadata that shows that a table is part of a set of sharded tables.
So I can do: How to get BigQuery storage size for a single table
select
sum(size_bytes)/pow(2, 30) as size_gb
from
<your_dataset>.__TABLES__
But here I can't see if the table is part of a set of sharded set of tables.
This is what my Google Analytics sharded tables look like in BQ:
So somewhere must be metadata that indicates that tables with for example name ga_sessions_20220504 belong to a sharded set ga_sesssions_
Where/how can I find that metadata?
I think you are exploring the right query, most of the time, I use the following query to drill down on shards & it's sizes
SELECT
project_id,
dataset_id,
table_id,
array_reverse(SPLIT(table_id, '_'))[OFFSET(0)] AS shard_pt,
DATE(TIMESTAMP_MILLIS(creation_time)) creation_dt,
ROUND(size_bytes/POW(1024, 3), 2) size_in_gb
FROM
`<project>.<dataset>.__TABLES__`
WHERE
table_id LIKE 'ga_sessions_%'
ORDER BY
4 DESC
Result (on some random GA dataset I have access to FYI)
There is no metadata on Sharded tables via SQL.
Tables being displayed as Sharded in BigQuery UI happens when you do the following ->
Create 2 or more tables that have the following characteristics:
exist in the same dataset
have the exact same table schema
the same prefix
have a suffix of the form _YYYYMMDD (eg. 20210130)
These are something of a legacy feature, they were more commonly used with bigquery’s legacy SQL.
This blog was very insightful on this:
https://mark-mccracken.medium.com/bigquery-date-sharding-vs-date-partitioning-cee3754f7900
I am trying to automatically generate a data documentation in the Redshift cluster for all the maintained data products, but I am having trouble to do so.
Is there a way to fetch/store metadata about tables/columns in redshift directly?
Is there also some automatic way to determine what are the unique keys in a Redshift table?
For example an ideal solution would be to have:
Table location (cluster, schema, etc.)
Table description (what is the table for)
Each column's description (what is each column for, data type, is it a key column, if so what type, etc.)
Column's distribution (min, max, median, mode, etc.)
Columns which together form a unique entry in the table
I fully understand that getting the descriptions automatically is pretty much impossible, but I couldn't find a way to store the descriptions in redshift directly, instead I'd have to use 3rd party solutions or generally a documentation outside of the SQL scripts, which I'm not a big fan of, due to the way the data products are built right now. Thus having a way to store each table's/column's description in redshift would be greatly appreciated.
Amazon Redshift has the ability to store a COMMENT on:
TABLE
COLUMN
CONSTRAINT
DATABASE
VIEW
You can use these comments to store descriptions. It might need a bit of table joining to access.
See: COMMENT - Amazon Redshift
I’m building an model in Azure Analysis Services. The model should contain only data for the last 3 months and is processed every day.
I have a separate dimension for date that has a relation with a fact table using a datekey. I’m using a power query to only load the last 3 months in the date dimension. In the power query to load the fact table I used Table.nestedjoin to only load the rows that have a value in the date table.
When I do this, the processing of the model takes forever. After some troubleshooting I saw that the query Analysis Services is using to retrieve data from the SQL database retrieves all rows. So, Am I correct saying AS load all data before it merge the rows? Is there a way to change this? Or is there a better way to a chief my solution?
Kind regards,
Joins are super slow in Power Query. You should avoid them if you can do it in the datasource or use normal relationships in the data model.
Also, you can setup the date dimension in DAX and dynamically populate it to contain only dates present in the FACT table.
As for the load of all the data, it could be because the data is fetched as is, and only then power query applies the transformations (the join).
You can modify the query in the Power Query Editor / Advenced Editor to add a where clause direclty in the query
I have a huge amount of log data exported from StackDriver to Google Cloud Storage. I am trying to run queries using BigQuery.
However, while creating the table in BigQuery Dataset I am getting
Invalid field name "k8s-app".
Fields must contain only letters, numbers, and underscores, start with a letter or underscore, and be at most 128 characters long.
Table: bq_table
A huge amount of log data is exported from StackDriver sinks which contains a large number of unique column names. Some of these names aren't valid as per BigQuery tables.
What is the solution for this? Is there a way to query the log data without cleaning it? Using temporary tables or something else?
Note: I do not want to load(put) my data into BigQuery Storage, just to query data which is present in Google Cloud Storage.
* EDIT *
Please refer to this documentation for clear understanding
I think you can go any of these two routes based on your application:
A. Ignore Header
If the problematic field is in the header row of your logs, you can choose to ignore the header row by adding the --skip_leading_rows=1 parameter in your import command. Something like:
bq location=US load --source_format=YOURFORMAT --skip_leading_rows=1 mydataset.rawlogstable gs://mybucket/path/* 'colA:STRING,colB:STRING,..'
B. Load Raw Data
If the above is not applicable, then just simply load the data in its un-structured raw format into BigQuery. Once your data is in there, you can go about doing all sorts of stuff.
So, first create a table with a single column:
bq mk --table mydataset.rawlogstable 'data:STRING'
Now load your dataset in the table providing appropriate location:
bq --location=US load --replace --source_format=YOURFORMAT mydataset.rawlogstable gs://mybucket/path/* 'data:STRING'
Once your data is loaded, now you can process it using SQL queries, and split it based on your delimiter and skip the stuff you don't like.
C. Create External Table
If you do not want to load data into BigQuery but still want to query it, you can choose to create an external table in BigQuery:
bq --location=US mk --external_table_definition=data:STRING#CSV=gs://mybucket/path/* mydataset.rawlogstable
Querying Data
If you pick option A and it works for you, you can simply choose to query your data the way you were already doing.
In the case you pick B or C, your table now has rows from your dataset as singular column rows. You can now choose to split these singular column rows into multiple column rows, based on your delimiter requirements.
Let's say your rows should have 3 columns named a,b and c:
a1,b1,c1
a2,b2,c2
Right now its all in the form of a singular column named data, which you can separate by the delimiter ,:
select
splitted[safe_offset(0)] as a,
splitted[safe_offset(1)] as b,
splitted[safe_offset(2)] as c
from (select split(data, ',') as splitted from `mydataset.rawlogstable`)
Hope it helps.
To expand on #khan's answer:
If the files are JSON, then you won't be able to use the first method (skip headers).
But you can load each JSON row raw to BigQuery - as if it was a CSV - and then parse in BigQuery
Find a full example for loading rows raw at:
https://medium.com/google-cloud/bigquery-lazy-data-loading-ddl-dml-partitions-and-half-a-trillion-wikipedia-pageviews-cd3eacd657b6
And then you can use JSON_EXTRACT_SCALAR to parse JSON in BigQuery - and transform the existing field names into BigQuery compatible ones.
Unfortunately no!
As part of log analytics, it is common to reshape the log data and run few ETL's before the files are committed to a persistent sink such as BigQuery.
If performance monitoring is all you need for log analytics, and there is no rationale to create additional code for ETL, all metrics can be derived from REST API endpoints of stackdriver monitoring.
If you do not need fields containing - you can set up to ignore ignore_unknown_values. You have to provide the schema you want and using ignore_unknown_values any field not matching the schema will be ignored.
Google BigQuery (BQ) allows you to create a partition using timestamp or date types only.
99% of my data has a very clear selector, idClient. I've created to my customer's views with a predicate like idClient = code so the privacy is guaranteed.
The problem with this strategy is that there are customers with 5M rows and others with 200K and as BQ does not have indexes, they are always processing data from each other (and the costs are rising).
I am intending to create a timestamp field where each customer will have a different timestamp that will be repeated for every Insert in every customer sensitive table and thus I can query by timestamp by fixing it as it would be with a standard ID.
Does this make any sense? If BQ was an indexed database I'd be concerned about skewed data but as it is always full table scan, I think I'd have only benefits and no downsides.
The solution for your problem is to add Cluster field to your table which is equivalent to an Index in other databases
This link provides the basic on how to use cluster field
Clustering can improve the performance of certain types of queries such as queries that use filter clauses and queries that aggregate data. When data is written to a clustered table by a query job or a load job, BigQuery sorts the data using the values in the clustering columns
Note: When using cluster field BigQuert dryRun doesn't show the cost improvement which can only be seen post-execution