I have a table in big query with 1 GB size. I create a view from this table with partitioning on created_at(timestamp) column. The view is useful for me but I want to write a query using created_at column. When I use this column , does the query run for whole data of view or run for only partitioned values? I want to limit usage of table like 500 MB. Is it possible with views by using partitioning column in where clause?
You can create new partitioned tables (here is the documentation) and copy the data into them.
To query a partitioned table you can use _PARTITIONTIME, for example:
SELECT
[COLUMN]
FROM
[DATASET].[TABLE]
WHERE
_PARTITIONTIME BETWEEN TIMESTAMP('2017-01-01') AND TIMESTAMP('2017-03-01')
Unless you're using actual BigQuery partitioned tables (there is no such thing as partitioned views) you'll be charged for all the data in the columns you access.
Related
In SQL Server , we can create index like this. How do we create the index after the table already exists? What is the syntax of create clusted index in bigquery?
CREATE INDEX abcd ON `abcd.xxx.xxx`(columnname )
In big query, we can create table like below. But how to create partition and cluster on an existing table?
CREATE TABLE rep_sales.orders_tmp PARTITION BY DATE(created_at) CLUSTER BY created_at AS SELECT * FROM rep_sales.orders
As #Sergey Geron mentioned in the comments, BigQuery doesn’t support indexes. For more information, please refer to this doc.
An existing table cannot be partitioned but you can create a new partitioned table and then load the data into it from the unpartitioned table.
As for clustering of tables, BigQuery supports changing an existing non-clustered table to a clustered table and vice versa. You can also update the set of clustered columns of a clustered table. This method of updating the clustering column set is useful for tables that use continuous streaming inserts because those tables cannot be easily swapped by other methods.
You can change the clustering specification in the following ways:
Call the tables.update or tables.patch API method.
Call the bq command-line tool's bq update command with the --clustering_fields flag.
Note: When a table is converted from non-clustered to clustered or the clustered column set is changed, automatic re-clustering only works from that time onward. For example, a non-clustered 1 PB table that is converted to a clustered table using tables.update still has 1 PB of non-clustered data. Automatic re-clustering only applies to any new data committed to the table after the update.
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
BigQuery's documentation says the following about clustered tables:
When you create a clustered table in BigQuery, the table data is automatically organized based on the contents of one or more columns in the table’s schema. The columns you specify are used to colocate related data. When you cluster a table using multiple columns, the order of columns you specify is important. The order of the specified columns determines the sort order of the data.
Since the records in the table are already colocated and sorted, is it possible to retrieve all the records from an arbitrary cluster?
I have a table that is too large to use ORDER BY. However, it is already clustered in the manner I need, so I can save a lot of time and expense if I could retrieve all the data from each cluster separately.
How should I go about clustering an ingestion-time partitioned table?
The examples mentioned in https://cloud.google.com/bigquery/docs/creating-clustered-tables create the destination clustered table as partitioned on a column. What if I want the new clustered table to be ingestion-time partitioned as well (similar to the source)? A work around is to copy each partition separately, for example:
bq query --nouse_legacy_sql --time_partitioning_type DAY --clustering_fields customer_id --destination_table='mydataset.myclusteredtable$20181001' 'SELECT * FROM `mydataset.mytable` where _PARTITIONTIME = timestamp("2018-10-01")'
But that's too much of a hassle.
Does the "Create table as" function in SQL Data Warehouse create statistics in the background, or do they have to manually be created (as I would when I do a normal "Create table" statement?)
As of the current version, you always have to create column-level statistics on tables, irrespective of whether it was created with a normal CREATE TABLE or the CTAS CREATE TABLE AS... command. It's also good practice to create stats for columns used in JOINs, WHERE clauses, GROUP BY, ORDER BY and DISTINCT clauses.
Regarding tables created with CTAS, the database engine has a correct idea of how many rows are in the table as listed in sys.partitions, but not at the column-level statistics level. For tables created by CREATE TABLE this defaults to 1,000 rows. For the example below, the first table was created with a CTAS and has 208 rows, the second table with an ordinary CREATE TABLE and INSERT from the first table and also has 208 rows, but sys.partitions believes it to have 1,000 eg
Creating any column-level statistics manually will correct this number.
In summary, always manually create statistics against important columns irrespective of how the table was created.