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.
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
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 am going to use a simple scenario to simplify my question.
I have table A (1000 records). This table has 5 years worth of data
table B (1,000,000 records). This table has 20 years worth of data.
Table A also has a column containing the key to join to table B. The key is to the earliest created record from Table B.
I am using import mode to load this data. When i load both tables, it imports all the records from both tables. I am looking to only bring in the records from table B that join to table A. similar to INNER JOIN.
I tried using the merge funcionality and selecting INNER as join type. In theory, this should only retrieve 1000 records back but when the data is loaded in PowerBI, all records from both tables are loaded into PowerBI desktop.
I am trying to reduce the dataset size by only retrieving the relevant records from table B but not having any luck.
Does anyone have any suggestions?
Import Table A and Table B into the query editor, do the inner join to create a new Table C that only has the matching rows.
Then right-click the Table A and Table B and uncheck "Enable Load" so that those tables are only used as connections rather than being loaded into the data model and saved in the PBIX.
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.
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.