Write SQL queries
Transform data from Cognite RAW into the CDF data model using built-in and customer Spark SQL queries. This article describes the queries and explains how you can load data incrementally.
Read data from CDF
From a RAW table
To select data from a RAW table, use the syntax mydb.mytable
.
select * from mydb.mytable
If your database or table name contains special characters, enclose the name in backticks, for example `my-db`.`my table`
.
From other CDF resource types
To select other CDF resource types, use the syntax _cdf.resource_type
.
select * from _cdf.events
The supported resource types are:
_cdf.events
_cdf.assets
_cdf.files
_cdf.timeseries
_cdf.sequences
_cdf_sequences.<sequence_externalId>
_cdf.datapoints
_cdf.stringdatapoints
_cdf.labels
_cdf.relationships
Load data incrementally
When reading from RAW tables, you probably want to transform only the data that has changed since the last transformation job ran. To achieve this, you can filter on the lastUpdatedTime
column to query for the rows that have changed after a specific timestamp.
When filtering on lastUpdatedTime
, the filter is pushed down to the RAW service itself, so this query can be performed efficiently.
For example: select * from mydb.mytable where lastUpdatedTime > to_timestamp(123456)
.
Instead of encoding the timestamp directly in the query and manually keeping it up to date every time new data has been processed, you can use the is_new
function. This function returns true
when a row has changed since the last time the transformation was run and false
otherwise.
The first time you run a transformation using the query below, all the rows of mytable
will be processed:
select * from mydb.mytable where is_new("mydb_mytable", lastUpdatedTime)
If the transformation completes successfully, the second run will only process rows that have changed since the first run.
If the transformation fails, is_new
filters the same rows the next time the transformation is run. This ensures that there is no data loss in the transformation from source to destination.
Incremental load is disabled when previewing query results. That is, is_new
will always return true
for all rows.
Each is_new
filter is identified by a name (for example,"mydb_mytable"
) and can be set to any constant string. This allows you to differentiate between multiple calls to is_new
in the same query and use is_new
to filter on multiple tables. To easily identify the different filters, we recommend that you use the name of the table as the name of the is_new
filter.
Backfill
To process all the data even if it hasn't changed since the last transformation, change the name of the is_new
filter, for example, by adding a postfix with an incrementing number (e.g. "mydb_mytable_1"
).
This is especially useful when the logic of the query changes and data that has already been imported needs to be updated accordingly.
Custom SQL functions
In addition to the built-in Spark SQL functions, we also provide a set of custom SQL functions to help you write efficient transformations.
When a function expects var_args
, it allows a variable number of arguments of any type, including star *
.
get_names
get_names(var_args): Array[String]
Returns an array of the field names of a struct or row.
Example
select get_names(*) from mydb.mytable
-- Returns the column names of 'mydb.mytable'
select get_names(some_struct.*) from mydb.mytable
-- Returns the field names of 'some_struct'
cast_to_strings
cast_to_strings(var_args): Array[String]
Casts the arguments to an array of strings. It handles array, struct and map types by casting it to JSON strings.
Example
select cast_to_strings(*) from mydb.mytable
-- Returns the values of all columns in 'mydb.mytable' as strings
to_metadata
to_metadata(var_args): Map[String, String]
Creates metadata compatible type from the arguments. In practice it does map_from_arrays(get_names(var_args), cast_to_strings(var_args))
. Use this function when you want to transform your columns or structures into a format that fits the metadata field in CDF.
Example
select to_metadata(*) from mydb.mytable
-- Creates a metadata structure from all the columns found in 'mydb.mytable'
to_metadata_except
to_metadata_except(excludeFilter: Array[String], var_args)
Returns a metadata structure (Map[String, String]
) where strings found in excludeFilter
will exclude keys from var_args
.
Use this function when you want to put most, but not all, columns into metadata, for example to_metadata_except(array("someColumnToExclude"), *)
Example
select to_metadata_except(array("myCol"), myCol, testCol) from mydb.mytable
-- Creates a map where myCol is filtered out.
-- The result in this case will be Map("testCol" -> testCol.value.toString)
asset_ids
asset_ids(assetNames: Array[String], rootAssetName: String): Array[BigInt]
Attempts to find Asset names in the asset hierarchy which have rootAssetName
as their root Asset. The function returns the IDs of the assets matched.
See Assets for more information about assets in CDF.
The entire job will be aborted if asset_ids()
did not find any matching assets.
Example
select asset_ids(array("PV10"), "MyBoat")
is_new
is_new(name: String, version: Timestamp)
Returns true
if the version provided is higher than the version found with the specified name, based on the last time the transformation was run.
If the transformation completes successfully, the next transformation job only processes rows that have changed since the start of the last successfully completed transformation job.
If the transformation fails,
is_new
processes all rows that have changed since the start of the last successful run. This ensures no data loss in the transformation from source to destination. See also Load data incrementally.
If you're using more than one occurrence of is_new()
in one transformation, we recommend that you use different variable names. This guarantees that subqueries within one transformation don't override the lastUpdatedTime
record before the transformation is completed.
Example
select * from mydb.mytable where is_new("mydb_mytable_version", lastUpdatedTime)
-- Returns only rows that have changed since the last successful run
dataset_id
dataset_id(externalId: String): BigInt
Attempts to find the id
of the given data set by externalId
and returns the id
if the externalId
exists.
Example
select dataset_id("EXAMPLE_DATASET") as dataSetId