Tables
All tables created within Base are gathered on the Projects > your_project > Base > Tables page. New tables can be created and existing tables can be updated or deleted here.
Create a new Table
To create a new table, click Projects > your_project > Base > Tables > +Create. Tables can be created from scratch or from a template that was previously saved. Views on data from Illumina hardware and processes are selected with the option Import from catalogue.
Once a table is saved it is no longer possible to edit the schema, only new fields can be added. The workaround is switching to text mode, copying the schema of the table to which you want to make modifications and paste it into a new empty table where the necessary changes can be made before saving.
Once created, do not try to modify your table column layout via the Query module as even though you can execute ALTER TABLE commands, the definitions and syntax of the table will go out of sync resulting in processing issues.
Be careful when naming tables when you want to use them in bundles. Table names have to be unique per bundle, so no two tables with the same name can be part of the same bundle.
Empty Table
To create a table from scratch, complete the fields listed below and click the Save button. Once saved, a job will be created to create the table. To view table creation progress, navigate to the Activity page.
Table information
The table name is a required field and must be unique. The first character of the table must be a letter followed by letters, numbers or underscores. The description is optional.
References
Including or excluding references can be done by checking or un-checking the Include reference checkbox. These reference fields are not shown on the table creation page, but are added to the schema definition, which is visible after creating the table (Projects > your_project > Base > Tables > your_table > Schema definition). By including references, additional columns will be added to the schema which can contain references to the data on the platform:
data_reference: reference to the data element in the Illumina platform from which the record originates
data_name: original name of the data element in the Illumina platform from which the record originates
sample_reference: reference to the sample in the Illumina platform from which the record originates
sample_name: name of the sample in the Illumina platform from which the record originates
pipeline_reference: reference to the pipeline in the Illumina platform from which the record originates
pipeline_name: name of the pipeline in the Illumina platform from which the record originates
execution_reference: reference to the pipeline execution in the Illumina platform from which the record originates
account_reference: reference to the account in the Illumina platform from which the record originates
account_name: name of the account in the Illumina platform from which the record originates
Schema
In an empty table, you can create a schema by adding a field with the +Add button for each column of the table and defining it. At any time during the creation process, it is possible to switch to the edit definition mode and back. The definition mode shows the JSON code, whereas the original view shows the fields in a table.
Each field requires:
a unique name (*1) with optional description.
a type
String – collection of characters
Bytes – raw binary data
Integer – whole numbers
Float – fractional numbers (*2)
Numeric – any number (*3)
Boolean – only options are “true” or “false”
Timestamp - Stores number of (milli)seconds passed since the Unix epoch
Date - Stores date in the format YYYY-MM-DD
Time - Stores time in the format HH:MI:SS
Datetime - Stores date and time information in the format YYYY-MM-DD HH:MI:SS
Record – has a child field
Variant - can store a value of any other type, including OBJECT and ARRAY
a mode
Required - Mandatory field
Nullable - Field is allowed to have no value
Repeated - Multiple values are allowed in this field (will be recognized as array in Snowflake)
(*1) Do not use reserved Snowflake keywords such as left, right, sample, select, table,... (https://docs.snowflake.com/en/sql-reference/reserved-keywords) for your schema name as this will lead to SQL compilation errors.
From template
Users can create their own template by making a table which is turned into a template at Projects > your_project > Base > Tables > your_table > Manage (top right) > Save as template.
If a template is created and available/active, it is possible to create a new table based on this template. The table information and references follow the rules of the empty table but in this case the schema will be pre-filled. It is possible to still edit the schema that is based on the template.
Table information
Table status
The status of a table can be found at Projects > your_project > Base > Tables. The possible statuses are:
Available: Ready to be used, both with or without data
Pending: The system is still processing the table, there is probably a process running to fill the table with data
Deleted: The table is deleted functionally; it still exists and can be shown in the list again by clicking the Show deleted tables/views button
Additional Considerations
Tables created from empty data or from a template are available faster.
When copying a table with data, it can remain in a Pending for longer periods of time.
Clicking on the page's refresh button will update the list.
Table details
For any available table, the following details are shown:
Table information: Name, description, status, number of records and data size.
Definition: An overview of the table schema, also available in text. Fields can be added to the schema but not deleted. Tip for deleting fields: copy the schema as text and paste in a new empty table where the schema is still editable.
Preview: A preview of the table for the 50 first rows (when data is uploaded into the table). Select show details to see record details.
Source Data: the files that are currently uploaded into the table. You can see the Load Status of the files which can be Prepare Started, Prepare Succeeded or Prepare Failed and finally Load Succeeded or Load Failed.
Table actions
From within the details of a table it is possible to perform the following actions from the Manage menu (top right) of the table:
Edit: Add fields to the table and change the table description.
Copy: Create a copy from this table in the same or a different project. In order to copy to another project, data sharing of the original project should be enabled in the details of this project. The user also has to have access to both original and target project.
Export as file: Export this table as a CSV or JSON file. The exported file can be found in a project where the user has the access to download it.
Save as template: Save the schema or an edited form of it as a template.
Add data: Load additional data into the table manually. This can be done by selecting data files previously uploaded to the project, or by dragging and dropping files directly into the popup window for adding data to the table. It’s also possible to load data into a table manually or automatically via a pre-configured job. This can be done on the Schedule page.
Delete: Delete the table.
Manually importing data to your Table
To manually add data to your table, go to Projects > your_project > Base > Tables > your_table > Manage (top right) > Add Data
Data selection
The data selection screen will show options to select the structure as CSV (comma-separated), TSV (tab-separated) or JSON (JavaScript Object Notation) and the location of your source data. In the first step, you select the data format and the files containing the data.
Data format (required): Select the format of the data which you want to import.
Write preference: Define if data can be written to the table only when the table is empty, if the data should be appended to the table or if the table should be overwrtitten.
Delimiter: Which delimiter is used in the delimiter separated file. If the required delimiter is not comma, tab or pipe, select custom and define the custom delimiter.
Custom delimiter: If a custom delimiter is used in the source data, it must be defined here.
Header rows to skip: The number of consecutive header rows (at the top of the table) to skip.
References: Choose which references must be added to the table.
Most of the advanced options are legacy functions and should not be used. The only exceptions are
Encoding: Select if the encoding is UTF-8 (any Unicode character) or ISO-8859-1 (first 256 Unicode characters).
Ignore unknown values: This applies to CSV-formatted files. You can use this function to handle optional fields without separators, provided that the missing fields are located at the end of the row. Otherwise, the parser can not detect the missing separator and will shift fields to the left, resulting in errors.
If headers are used: The columns that have matching fields are loaded, those that have no matching fields are loaded with NULL and remaining fields are discarded.
If no headers are used: The fields are loaded in order of occurrence and trailing missing fields are loaded with NULL, trailing additional fields are discarded.
Data import progress
To see the status of your data import, go to Projects > your_project > Activity > Base Jobs where you will see a job of type Prepare Data which will have succeeded or failed. If it has failed, you can see the error message and details by double-clicking the base job. You can then take corrective actions if the input mismatched with the table design and try to run the import again (with a new copy of the file as each input file can only be used once)
If you need to cancel the import, you can do so while it is scheduled by navigating to the Base Jobs inventory and selecting the job followed by Abort.
List of table data sources
To see which data has been used to populate your table go to Projects > your_project > Base > Tables > your_table > Source Data. This will list all the source data files, even those that failed to be imported. You can not use these files anymore to import again to prevent double entries.
How to load array data in Base
Base Table schema definitions do not include an array type, but arrays can be ingested using either the Repeated mode for arrays containing a single type (ie, String), or the Variant type.
Parsing nested JSON data
If you have a nested JSON structure, you can import it into individual fields of your table.
{
"one": {
"a": "1",
"b": "1"
},
"three": {
"a": "3",
"b": "3",
"c": "3"
}
}For example, if your JSON nested structure looks like the above and you want to get it imported into a table with a, b and c having integers as values, you need to create a matching table. This can be done either manually or via the sql command CREATE OR REPLACE TABLE json_data ( a INTEGER, b INTEGER, c INTEGER);
Format your JSON data to have single lines per structure.
{"A":1,"B":1}
{"A":3,"B":3,"C":3}Finally, create a schedule to import your data or perform a manual import.
The resulting table will look like this:
1
1
1
2
3
3
3
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