Data Types

Data models and type definitions used throughout the DASL client. These classes represent the structure of datasources, rules, configurations, and other DASL objects.

Core Data Models

class dasl_client.types.datasource.DataSource(**data)

Bases: BaseModel

A DataSource resource.

metadata

Standard object metadata.

Type:

Optional[Metadata]

source

The name of the originator of the data.

Type:

Optional[str]

source_type

The type of data being imported.

Type:

Optional[str]

schedule

The schedule for data ingestion.

Type:

Optional[Schedule]

custom

A custom notebook for the datasource.

Type:

Optional[DataSource.CustomNotebook]

primary_key

Primary key configuration of the datasource.

Type:

Optional[PrimaryKey]

use_preset

The name of the preset to use for this data source.

Type:

Optional[str]

autoloader

Autoloader configuration.

Type:

Optional[DataSource.Autoloader]

bronze

Bronze table configuration.

Type:

Optional[BronzeSpec]

compute_mode

The compute mode to use for this datasource’s job.

Type:

Optional[str]

silver

Silver transformation configuration.

Type:

Optional[SilverSpec]

gold

Gold transformation configuration.

Type:

Optional[GoldSpec]

status

The current status of the datasource.

Type:

Optional[ResourceStatus]

Parameters:

data (Any)

class CustomNotebook(**data)

Bases: BaseModel

A custom notebook for generating data.

notebook

Path to the notebook in the Databricks workspace.

Type:

Optional[str]

Parameters:

data (Any)

notebook: Optional[str]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecCustom])

Return type:

CustomNotebook

to_api_obj()

Return type:

CoreV1DataSourceSpecCustom

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class PrimaryKey(**data)

Bases: BaseModel

PrimaryKey configuration for DataSource

time_column

column name used as timestamp portion of the sortable synthetic key

Type:

str

additionalColumns

list of columns to compute hashkey over

Type:

List[str]

Parameters:

data (Any)

time_column: str

additional_columns: List[str]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourcePrimaryKeySpec])

Return type:

PrimaryKey

to_api_obj()

Return type:

CoreV1DataSourcePrimaryKeySpec

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class Autoloader(**data)

Bases: BaseModel

Autoloader configuration for the DataSource.

format

The format of the data (e.g., json, parquet, csv, etc.).

Type:

Optional[str]

location

External location for the volume in Unity Catalog.

Type:

str

schema_file

An optional file containing the schema of the data source.

Type:

Optional[str]

cloud_files

CloudFiles configuration.

Type:

Optional[DataSource.Autoloader.CloudFiles]

Parameters:

data (Any)

class CloudFiles(**data)

Bases: BaseModel

CloudFiles configuration for the Autoloader.

schema_hints_file

Type:

Optional[str]

schema_hints

Type:

Optional[str]

Parameters:

data (Any)

schema_hints_file: Optional[str]

schema_hints: Optional[str]

static from_api_obj(obj)

Parameters:

obj (Optional[ContentV1DatasourcePresetAutoloaderCloudFiles])

Return type:

CloudFiles

to_api_obj()

Return type:

ContentV1DatasourcePresetAutoloaderCloudFiles

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

format: Optional[str]

location: str

schema_file: Optional[str]

cloud_files: Optional[DataSource.Autoloader.CloudFiles]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceAutoloaderSpec])

Return type:

Autoloader

to_api_obj()

Return type:

CoreV1DataSourceAutoloaderSpec

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

metadata: Optional[Metadata]

source: Optional[str]

source_type: Optional[str]

schedule: Optional[Schedule]

custom: Optional[DataSource.CustomNotebook]

primary_key: Optional[DataSource.PrimaryKey]

use_preset: Optional[str]

use_preset_version: Optional[int]

autoloader: Optional[DataSource.Autoloader]

compute_mode: Optional[str]

bronze: Optional[BronzeSpec]

silver: Optional[SilverSpec]

gold: Optional[GoldSpec]

status: Optional[ResourceStatus]

static from_api_obj(obj)

Parameters:

obj (CoreV1DataSource)

Return type:

DataSource

to_api_obj()

Return type:

CoreV1DataSource

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class dasl_client.types.rule.Rule(**data)

Bases: BaseModel

Rules define how to generate notables from input data.

metadata

Standard object metadata.

Type:

Optional[Metadata]

rule_metadata

The rule configuration metadata.

Type:

Optional[Rule.RuleMetadata]

schedule

The rule schedule.

Type:

Schedule

input

The rule input configuration.

Type:

Rule.Input

observables

A list of observables.

Type:

Optional[List[Rule.Observable]]

output

The rule output configuration.

Type:

Rule.Output

collate

The collate configuration.

Type:

Optional[Rule.Collate]

status

The current status of the rule.

Type:

Optional[ResourceStatus]

Parameters:

data (Any)

class RuleMetadata(**data)

Bases: BaseModel

RuleMetadata object wrapping CoreV1RuleSpecMetadata.

version

The current version of the rule.

Type:

Optional[Union[float, int]]

category

The category of this detection. The available values are configured in workspace config.

Type:

Optional[str]

severity

The threat level associated with the notable.

Type:

Optional[str]

fidelity

Fidelity is used to capture the maturity of a rule. Newly created, untested rules should be marked as Investigative fidelity. Older, more well-tested rules should be marked as High fidelity. This helps an analyst determine how likely a notable is a false positive.

Type:

Optional[str]

mitre

Mitre ATT&CK tactic information.

Type:

Optional[List[Rule.RuleMetadata.Mitre]]

objective

A longer form description of what this rule is attempting to detect (objectMeta.comment is a summary).

Type:

Optional[str]

response

Response configuration for the rule.

Type:

Optional[Rule.RuleMetadata.Response]

Parameters:

data (Any)

class Mitre(**data)

Bases: BaseModel

Mitre ATT&CK details associated with a Rule.

taxonomy

Mitre ATT&CK taxonomy.

Type:

Optional[str]

tactic

Mitre ATT&CK tactic.

Type:

Optional[str]

technique_id

The Mitre ATT&CK technique identifier.

Type:

Optional[str]

technique

The Mitre ATT&CK technique human-readable name.

Type:

Optional[str]

sub_technique_id

The Mitre ATT&CK sub-technique identifier.

Type:

Optional[str]

sub_technique

The Mitre ATT&CK sub-technique human-readable name.

Type:

Optional[str]

Parameters:

data (Any)

taxonomy: Optional[str]

tactic: Optional[str]

technique_id: Optional[str]

technique: Optional[str]

sub_technique_id: Optional[str]

sub_technique: Optional[str]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1RuleSpecMetadataMitreInner])

Return type:

Mitre

to_api_obj()

Return type:

CoreV1RuleSpecMetadataMitreInner

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class Response(**data)

Bases: BaseModel

Details regarding how to respond to a notable generated by the Rule.

guidelines

Response guidelines.

Type:

Optional[str]

playbooks

Suggested response playbooks.

Type:

Optional[List[Rule.RuleMetadata.Response.Playbook]]

Parameters:

data (Any)

class Playbook(**data)

Bases: BaseModel

Databricks notebook and template values to generate a playbook for the analyst from the notable.

notebook

Notebook to run.

Type:

Optional[str]

options

These are templated, if they contain ${} this will be filled in using the notable.

Type:

Optional[Dict[str, str]]

Parameters:

data (Any)

notebook: Optional[str]

options: Optional[Dict[str, str]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1RuleSpecMetadataResponsePlaybooksInner])

Return type:

Playbook

to_api_obj()

Return type:

CoreV1RuleSpecMetadataResponsePlaybooksInner

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

guidelines: Optional[str]

playbooks: Optional[List[Rule.RuleMetadata.Response.Playbook]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1RuleSpecMetadataResponse])

Return type:

Response

to_api_obj()

Return type:

CoreV1RuleSpecMetadataResponse

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

version: Union[float, int, None]

category: Optional[str]

severity: Optional[str]

fidelity: Optional[str]

mitre: Optional[List[Rule.RuleMetadata.Mitre]]

objective: Optional[str]

response: Optional[Rule.RuleMetadata.Response]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1RuleSpecMetadata])

Return type:

RuleMetadata

to_api_obj()

Return type:

CoreV1RuleSpecMetadata

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class Input(**data)

Bases: BaseModel

Specification of input data for the Rule.

stream

Input if the Rule should operate on streaming input data.

Type:

Optional[Rule.Input.Stream]

batch

Input if the rule should operate on batched input data.

Type:

Optional[Rule.Input.Batch]

Parameters:

data (Any)

class CustomStream(**data)

Bases: BaseModel

Specification of a stream custom notebook for generating input to the Rule.

notebook

Type:

Optional[str]

options

Type:

Optional[Dict[str, str]]

Parameters:

data (Any)

notebook: Optional[str]

options: Optional[Dict[str, str]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1RuleSpecInputStreamCustom])

Return type:

CustomStream

to_api_obj()

Return type:

CoreV1RuleSpecInputStreamCustom

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class CustomBatch(**data)

Bases: BaseModel

Specification of a batch custom notebook for generating input to the Rule.

notebook

Type:

Optional[str]

options

Type:

Optional[Dict[str, str]]

Parameters:

data (Any)

notebook: Optional[str]

options: Optional[Dict[str, str]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1RuleSpecInputBatchCustom])

Return type:

CustomBatch

to_api_obj()

Return type:

CoreV1RuleSpecInputBatchCustom

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class Stream(**data)

Bases: BaseModel

Specification for streaming input from a table or tables, from a custom notebook, or directly from a SQL statement.

tables

List of input tables and join rules.

Type:

Optional[List[Rule.Input.Stream.Table]]

filter

A filter expression to be applied to the input stream. Note that this cannot be used in conjunction with a custom SQL expression (i.e. the sql field).

Type:

Optional[str]

sql

A custom SQL expression to apply to the input stream before matching. Note that this cannot be used in conjunction with a filter expression (i.e. the filter member).

Type:

Optional[str]

custom

Type:

Optional[Rule.Input.CustomStream]

starting_timestamp

Starting timestamp for streaming input data. If this value is not specified, then the timestamp when this rule was created will be used. This setting is used to determine the starting point for streaming historical data, and only applies on the first run of the rule. Once some data has been streamed and a checkpoint has been created, this setting no longer has any impact.

Type:

Optional[datetime]

Parameters:

data (Any)

class Table(**data)

Bases: BaseModel

Specification of a table for streaming input.

name

Name of the table.

Type:

Optional[str]

watermark

Watermark configuration.

Type:

Optional[Rule.Input.Stream.Table.Watermark]

alias

Alias name for the table.

Type:

Optional[str]

join_type

For tables other than the first, how to join to the preceding table.

Type:

Optional[str]

join_expr

For tables other than the first, the join condition expression to join with the preceding table.

Type:

Optional[str]

streaming

For tables other than the first, is this a streaming join or static. Default is false, except on the first table.

Type:

Optional[bool]

Parameters:

data (Any)

class Watermark(**data)

Bases: BaseModel

Watermark for a streaming input table.

event_time_column

Which column is the event time for the delay threshold.

Type:

str

delay_threshold

A time duration string for the watermark delay.

Type:

str

drop_duplicates

Pass into pyspark dropDuplicates (effectively columns for group by).

Type:

Optional[List[str]]

Parameters:

data (Any)

event_time_column: str

delay_threshold: str

drop_duplicates: Optional[List[str]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1RuleSpecInputStreamTablesInnerWatermark])

Return type:

Watermark

to_api_obj()

Return type:

CoreV1RuleSpecInputStreamTablesInnerWatermark

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: Optional[str]

watermark: Optional[Rule.Input.Stream.Table.Watermark]

alias: Optional[str]

join_type: Optional[str]

join_expr: Optional[str]

streaming: Optional[bool]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1RuleSpecInputStreamTablesInner])

Return type:

Table

to_api_obj()

Return type:

CoreV1RuleSpecInputStreamTablesInner

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

tables: Optional[List[Rule.Input.Stream.Table]]

filter: Optional[str]

sql: Optional[str]

custom: Optional[Rule.Input.CustomStream]

starting_timestamp: Optional[datetime]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1RuleSpecInputStream])

Return type:

Stream

to_api_obj()

Return type:

CoreV1RuleSpecInputStream

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class Batch(**data)

Bases: BaseModel

Specification for batch input to a Rule, either from a SQL statement or a custom notebook.

sql

Type:

Optional[str]

custom

Type:

Optional[Rule.Input.CustomBatch]

Parameters:

data (Any)

sql: Optional[str]

custom: Optional[Rule.Input.CustomBatch]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1RuleSpecInputBatch])

Return type:

Batch

to_api_obj()

Return type:

CoreV1RuleSpecInputBatch

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

stream: Optional[Rule.Input.Stream]

batch: Optional[Rule.Input.Batch]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1RuleSpecInput])

Return type:

Input

to_api_obj()

Return type:

CoreV1RuleSpecInput

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class Observable(**data)

Bases: BaseModel

Observable associated with a Rule.

kind

Type:

str

value

Type:

str

relationship

Type:

str

risk

Type:

Rule.Observable.Risk

Parameters:

data (Any)

class Risk(**data)

Bases: BaseModel

Risk level associated with an Observable.

impact

A SQL expression indicating the impact (should evaluate to a number between 0-100).

Type:

str

confidence

A SQL expression indicating the confidence (should evaluate to a number between 0-100).

Type:

str

Parameters:

data (Any)

impact: str

confidence: str

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1RuleObservableRisk])

Return type:

Risk

to_api_obj()

Return type:

CoreV1RuleObservableRisk

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

kind: str

value: str

relationship: str

risk: Rule.Observable.Risk

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1RuleObservable])

Return type:

Observable

to_api_obj()

Return type:

CoreV1RuleObservable

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class Output(**data)

Bases: BaseModel

Output from a Rule after matching.

summary

Type:

Optional[str]

context

Type:

Optional[Dict[str, str]]

Parameters:

data (Any)

summary: Optional[str]

context: Optional[Dict[str, str]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1RuleSpecOutput])

Return type:

Output

to_api_obj()

Return type:

CoreV1RuleSpecOutput

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class Collate(**data)

Bases: BaseModel

Configuration for collating multiple notables generated from a Rule within a given timeframe.

collate_on

SQL column(s) to collate notables on. This must be either ‘summary’, ‘context’, or a JSON key within ‘context’. Only one level of nesting is currently supported.

Type:

Optional[List[str]]

within

A time duration string for the window to collate within.

Type:

Optional[str]

action

append | updateStats | inhibit.

Type:

Optional[str]

Parameters:

data (Any)

collate_on: Optional[List[str]]

within: Optional[str]

action: Optional[str]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1RuleSpecCollate])

Return type:

Collate

to_api_obj()

Return type:

CoreV1RuleSpecCollate

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

metadata: Optional[Metadata]

rule_metadata: Optional[Rule.RuleMetadata]

schedule: Schedule

input: Rule.Input

observables: Optional[List[Rule.Observable]]

output: Rule.Output

collate: Optional[Rule.Collate]

status: Optional[ResourceStatus]

static from_api_obj(obj)

Parameters:

obj (CoreV1Rule)

Return type:

Rule

to_api_obj()

Return type:

CoreV1Rule

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class dasl_client.types.workspace_config.WorkspaceConfig(**data)

Bases: BaseModel

General configuration settings for the Workspace.

metadata

Common resource metadata; generally managed by the control plane.

Type:

Optional[Metadata]

system_tables_config

Configuration for the storage of metadata by the control plane.

Type:

SystemTablesConfig

default_sql_warehouse

Default SQL warehouse to use for executing certain queries. May be overridden in some cases.

Type:

Optional[str]

detection_rule_metadata

Detection rule metadata.

Type:

Optional[DetectionRuleMetadata]

notable_export

Settings related to the export of notables to various destinations.

Type:

Optional[ExportConfig]

operational_alert_export

Settings related to the export of operational alerts to various destinations.

Type:

Optional[ExportConfig]

observables

Declaration of the types of observables generated by the system.

Type:

Optional[WorkspaceConfigObservables]

dasl_storage_path

The path to a directory where DASL can store internal files and state.

Type:

Optional[str]

dasl_custom_presets_path

An optional path to a directory containing user defined presets.

Type:

Optional[str]

default_rule_schedule

A default schedule for detections. If a detection is created without a schedule, it will inherit the schedule provided here. Note that, should this schedule be updated, it will affect all detections inheriting it.

Type:

Optional[str]

default_config

(DEPRECATED) Configuration settings regarding storage of bronze, silver, and gold tables and related assets for each resource type.

Type:

Optional[DefaultConfig]

default_custom_notebook_location

The storage location for custom user-provided notebooks. Also used as the prefix for relative paths to custom notebooks.

Type:

Optional[str]

datasources

Configuration items that apply specifically to datasources.

Type:

Optional[DatasourcesConfig]

rules

Configuration items that apply specifically to rules.

Type:

Optional[RulesConfig]

managed_retention

Configuration of regular cleanup (i.e. pruning) jobs for various catalogs, schemas, and tables.

Type:

Optional[List[ManagedRetention]]

status

Common resource status; wholly managed by the control plane.

Type:

Optional[ResourceStatus]

Parameters:

data (Any)

metadata: Optional[Metadata]

system_tables_config: SystemTablesConfig

default_sql_warehouse: Optional[str]

detection_rule_metadata: Optional[DetectionRuleMetadata]

notable_export: Optional[ExportConfig]

operational_alert_export: Optional[ExportConfig]

observables: Optional[WorkspaceConfigObservables]

dasl_storage_path: Optional[str]

dasl_custom_presets_path: Optional[str]

default_rule_schedule: Optional[Schedule]

default_config: Optional[DefaultConfig]

default_custom_notebook_location: Optional[str]

datasources: Optional[DatasourcesConfig]

rules: Optional[RulesConfig]

managed_retention: Optional[List[ManagedRetention]]

status: Optional[ResourceStatus]

static from_api_obj(obj)

Parameters:

obj (WorkspaceV1WorkspaceConfig)

Return type:

WorkspaceConfig

to_api_obj()

Return type:

WorkspaceV1WorkspaceConfig

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Common Helper Types

class dasl_client.types.types.Schedule(**data)

Bases: BaseModel

A schedule for recurring dispatch of the Job(s) associated with a resource.

at_least_every

A duration string defining the schedule. The string may contain components for days, hours, minutes and seconds. Examples include ‘5d’, ‘5d3h’, ‘3d2h5m12s’, and ‘12m16s’.

Type:

Optional[str]

exactly

A quartz cron expression defining the precise schedule for dispatch.

Type:

Optional[str]

continuous

True if the Job should be continuously dispatched, False/None otherwise.

Type:

Optional[bool]

compute_group

‘dedicated’, ‘automatic’ or a custom group name.

Type:

Optional[str]

enabled

Whether the schedule (and hence Job associated with the resource) is active.

Type:

Optional[bool]

Parameters:

data (Any)

at_least_every: Optional[str]

exactly: Optional[str]

continuous: Optional[bool]

compute_group: Optional[str]

enabled: Optional[bool]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1Schedule])

Return type:

Schedule

to_api_obj()

Return type:

CoreV1Schedule

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Datasource Helper Types

class dasl_client.types.datasource.BronzeSpec(**data)

Bases: BaseModel

Configuration for bronze table within a DataSource.

clustering

Describes optional liquid clustering configuration for the bronze table.

Type:

Optional[BronzeSpec.Clustering]

bronze_table

The name of the bronze table to create and hold the imported data.

Type:

Optional[str]

skip_bronze_loading

Indicates whether to skip the bronze loading step.

Type:

Optional[bool]

load_as_single_variant

Indicates whether to ingest data into a single VARIANT-typed column called data

Type:

Optional[bool]

pre_transform

A list of pre-transform steps to execute. The outer list form stages and the inner list contains SQL select expressions to be executed within each stage

Type:

Optional[List[List[str]]]

Parameters:

data (Any)

class Clustering(**data)

Bases: BaseModel

Configuration of liquid clustering for a bronze table.

column_names

List of column names to include in liquid clustering.

Type:

Optional[List[str]]

time_column

Name of the column that holds ‘time’ information for clustering.

Type:

Optional[str]

Parameters:

data (Any)

column_names: Optional[List[str]]

time_column: Optional[str]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecBronzeClustering])

Return type:

Clustering

to_api_obj()

Return type:

CoreV1DataSourceSpecBronzeClustering

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

clustering: Optional[BronzeSpec.Clustering]

bronze_table: Optional[str]

skip_bronze_loading: Optional[bool]

load_as_single_variant: Optional[bool]

pre_transform: Optional[List[List[str]]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecBronze])

Return type:

BronzeSpec

to_api_obj()

Return type:

CoreV1DataSourceSpecBronze

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class dasl_client.types.datasource.SilverSpec(**data)

Bases: BaseModel

Configuration for silver table in a DataSource.

bronze_tables

A list of bronze tables to be joined for silver transformation.

Type:

Optional[List[SilverSpec.BronzeTable]]

pre_transform

Pretransformation configuration.

Type:

Optional[SilverSpec.PreTransform]

transform

Transformation configuration for silver processing.

Type:

Optional[SilverSpec.Transform]

Parameters:

data (Any)

class BronzeTable(**data)

Bases: BaseModel

Reference to a bronze table for a silver table.

name

Name of the bronze table.

Type:

Optional[str]

streaming

True if the input should be streamed from the bronze table.

Type:

Optional[bool]

watermark

Bronze table watermark.

Type:

Optional[SilverSpec.BronzeTable.Watermark]

alias

Alias name for the table.

Type:

Optional[str]

join_type

How to join to the preceding table.

Type:

Optional[str]

join_expr

The join condition expression.

Type:

Optional[str]

Parameters:

data (Any)

class Watermark(**data)

Bases: BaseModel

Watermark for a bronze source table within a silver table.

event_time_column

Which column is the event time for the delay threshold.

Type:

Optional[str]

delay_threshold

A time duration string for the watermark delay.

Type:

Optional[str]

drop_duplicates

Columns to pass to pyspark dropDuplicates.

Type:

Optional[List[str]]

Parameters:

data (Any)

event_time_column: Optional[str]

delay_threshold: Optional[str]

drop_duplicates: Optional[List[str]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecSilverBronzeTablesInnerWatermark])

Return type:

Watermark

to_api_obj()

Return type:

CoreV1DataSourceSpecSilverBronzeTablesInnerWatermark

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: Optional[str]

streaming: Optional[bool]

watermark: Optional[SilverSpec.BronzeTable.Watermark]

alias: Optional[str]

join_type: Optional[str]

join_expr: Optional[str]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecSilverBronzeTablesInner])

Return type:

BronzeTable

to_api_obj()

Return type:

CoreV1DataSourceSpecSilverBronzeTablesInner

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class PreTransform(**data)

Bases: BaseModel

Pre-transform for a silver table.

use_preset

Preset to use.

Type:

Optional[str]

skip_pre_transform

If True, skip pre-transform entirely.

Type:

Optional[bool]

custom

Custom pretransform function and options.

Type:

Optional[SilverSpec.PreTransform.Custom]

filter

A SQL filter to apply at the beginning of the preTransform phase.

Type:

Optional[str]

post_filter

A SQL filter to apply at the end of the preTransform phase.

Type:

Optional[str]

preset_overrides

Overrides for preset filters.

Type:

Optional[SilverSpec.PreTransform.PresetOverrides]

add_fields

User defined fields to add to the transformation.

Type:

Optional[List[FieldSpec]]

Parameters:

data (Any)

class Custom(**data)

Bases: BaseModel

Custom pre-transform function for silver table.

function

Type:

Optional[str]

options

Type:

Optional[Dict[str, str]]

Parameters:

data (Any)

function: Optional[str]

options: Optional[Dict[str, str]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecSilverPreTransformCustom])

Return type:

Custom

to_api_obj()

Return type:

CoreV1DataSourceSpecSilverPreTransformCustom

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class PresetOverrides(**data)

Bases: BaseModel

Overrides for the preset.

omit_fields

A list of fields to omit from the chosen preset.

Type:

Optional[List[str]]

Parameters:

data (Any)

omit_fields: Optional[List[str]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecSilverPreTransformPresetOverrides])

Return type:

PresetOverrides

to_api_obj()

Return type:

CoreV1DataSourceSpecSilverPreTransformPresetOverrides

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

use_preset: Optional[str]

skip_pre_transform: Optional[bool]

custom: Optional[SilverSpec.PreTransform.Custom]

filter: Optional[str]

post_filter: Optional[str]

preset_overrides: Optional[SilverSpec.PreTransform.PresetOverrides]

add_fields: Optional[List[FieldSpec]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecSilverPreTransform])

Return type:

PreTransform

to_api_obj()

Return type:

CoreV1DataSourceSpecSilverPreTransform

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class Transform(**data)

Bases: BaseModel

Silver table transform.

skip_silver_transform

If True, skip transform entirely.

Type:

Optional[bool]

preset_overrides

Preset overrides for the silver transformation.

Type:

Optional[SilverSpec.Transform.PresetOverrides]

Parameters:

data (Any)

class PresetOverrides(**data)

Bases: BaseModel

Overrides for preset transform settings.

modify_tables

Modifications forexisting tables.

Type:

Optional[List[SilverSpec.Transform.PresetOverrides.ModifyTables]]

omit_tables

A list of tables to omit from the preset.

Type:

Optional[List[str]]

add_tables

User defined tables to include in the transformation.

Type:

Optional[List[SilverSpec.Transform.PresetOverrides.AddTables]]

Parameters:

data (Any)

class Custom(**data)

Bases: BaseModel

Custom function for use in silver table transform.

function

Type:

Optional[str]

options

Type:

Optional[Dict[str, str]]

Parameters:

data (Any)

function: Optional[str]

options: Optional[Dict[str, str]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecSilverTransformPresetOverridesModifyTablesInnerCustom])

Return type:

Custom

to_api_obj()

Return type:

CoreV1DataSourceSpecSilverTransformPresetOverridesModifyTablesInnerCustom

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class ModifyTables(**data)

Bases: BaseModel

Table modifications as part of a silver transform.

name

Type:

Optional[str]

custom

Type:

Optional[SilverSpec.Transform.PresetOverrides.Custom]

omit_fields

Type:

Optional[List[str]]

override_liquid_columns

Type:

Optional[List[str]]

add_fields

Type:

Optional[List[FieldSpec]]

filter

Type:

Optional[str]

post_filter

Type:

Optional[str]

utils

Type:

Optional[FieldUtils]

Parameters:

data (Any)

name: Optional[str]

custom: Optional[SilverSpec.Transform.PresetOverrides.Custom]

omit_fields: Optional[List[str]]

override_liquid_columns: Optional[List[str]]

add_fields: Optional[List[FieldSpec]]

filter: Optional[str]

post_filter: Optional[str]

utils: Optional[FieldUtils]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecSilverTransformPresetOverridesModifyTablesInner])

Return type:

ModifyTables

to_api_obj()

Return type:

CoreV1DataSourceSpecSilverTransformPresetOverridesModifyTablesInner

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class AddTables(**data)

Bases: BaseModel

Tables to add during a silver table transform.

custom

Type:

Optional[SilverSpec.Transform.PresetOverrides.Custom]

name

Type:

Optional[str]

filter

Type:

Optional[str]

post_filter

Type:

Optional[str]

override_liquid_columns

Type:

Optional[List[str]]

fields

Type:

Optional[List[FieldSpec]]

utils

Type:

Optional[FieldUtils]

Parameters:

data (Any)

custom: Optional[SilverSpec.Transform.PresetOverrides.Custom]

name: Optional[str]

filter: Optional[str]

post_filter: Optional[str]

override_liquid_columns: Optional[List[str]]

fields: Optional[List[FieldSpec]]

utils: Optional[FieldUtils]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecSilverTransformPresetOverridesAddTablesInner])

Return type:

AddTables

to_api_obj()

Return type:

CoreV1DataSourceSpecSilverTransformPresetOverridesAddTablesInner

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

modify_tables: Optional[List[SilverSpec.Transform.PresetOverrides.ModifyTables]]

omit_tables: Optional[List[str]]

add_tables: Optional[List[SilverSpec.Transform.PresetOverrides.AddTables]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecSilverTransformPresetOverrides])

Return type:

PresetOverrides

to_api_obj()

Return type:

CoreV1DataSourceSpecSilverTransformPresetOverrides

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

skip_silver_transform: Optional[bool]

do_not_materialize: Optional[bool]

preset_overrides: Optional[SilverSpec.Transform.PresetOverrides]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecSilverTransform])

Return type:

Transform

to_api_obj()

Return type:

CoreV1DataSourceSpecSilverTransform

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

bronze_tables: Optional[List[SilverSpec.BronzeTable]]

pre_transform: Optional[SilverSpec.PreTransform]

transform: Optional[SilverSpec.Transform]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecSilver])

Return type:

SilverSpec

to_api_obj()

Return type:

CoreV1DataSourceSpecSilver

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class dasl_client.types.datasource.GoldSpec(**data)

Bases: BaseModel

Configuration for gold table in a DataSource.

omit_tables

A list of tables to omit from the preset.

Type:

Optional[List[str]]

modify_tables

Modifications for existing gold table definitions.

Type:

Optional[List[GoldSpec.ModifyTables]]

add_tables

User defined tables to add to the gold configuration.

Type:

Optional[List[GoldSpec.AddTables]]

Parameters:

data (Any)

class ModifyTables(**data)

Bases: BaseModel

Modification to gold tables during transformation.

name

Table name.

Type:

Optional[str]

source_table

Used to match against the preset’s gold stanzas input fields.

Type:

Optional[str]

custom

Custom function for modifying tables.

Type:

Optional[GoldSpec.ModifyTables.Custom]

omit_fields

A list of fields to omit.

Type:

Optional[List[str]]

add_fields

Fields to add.

Type:

Optional[List[FieldSpec]]

filter

A SQL filter to apply before processing.

Type:

Optional[str]

post_filter

A SQL filter to apply after processing.

Type:

Optional[str]

Parameters:

data (Any)

class Custom(**data)

Bases: BaseModel

Custom function to use as part of a gold table modification.

function

Type:

Optional[str]

options

Type:

Optional[Dict[str, str]]

Parameters:

data (Any)

function: Optional[str]

options: Optional[Dict[str, str]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecSilverTransformPresetOverridesModifyTablesInnerCustom])

Return type:

Custom

to_api_obj()

Return type:

CoreV1DataSourceSpecSilverTransformPresetOverridesModifyTablesInnerCustom

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: Optional[str]

source_table: Optional[str]

custom: Optional[GoldSpec.ModifyTables.Custom]

omit_fields: Optional[List[str]]

add_fields: Optional[List[FieldSpec]]

filter: Optional[str]

post_filter: Optional[str]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecGoldPresetOverridesModifyTablesInner])

Return type:

ModifyTables

to_api_obj()

Return type:

CoreV1DataSourceSpecGoldPresetOverridesModifyTablesInner

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class AddTables(**data)

Bases: BaseModel

Tables to add during gold table transformation.

custom

Custom function for adding tables.

Type:

Optional[GoldSpec.AddTables.Custom]

name

The name of the table to add.

Type:

Optional[str]

source_table

The source table/dataframe for the gold table.

Type:

Optional[str]

filter

A SQL filter to apply.

Type:

Optional[str]

post_filter

A SQL filter to apply after processing.

Type:

Optional[str]

fields

Field specifications for the new table.

Type:

Optional[List[FieldSpec]]

Parameters:

data (Any)

class Custom(**data)

Bases: BaseModel

Custom function for adding tables during gold transformation.

function

Type:

Optional[str]

options

Type:

Optional[Dict[str, str]]

Parameters:

data (Any)

function: Optional[str]

options: Optional[Dict[str, str]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecGoldPresetOverridesAddTablesInnerCustom])

Return type:

Custom

to_api_obj()

Return type:

CoreV1DataSourceSpecGoldPresetOverridesAddTablesInnerCustom

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: Optional[str]

source_table: Optional[str]

custom: Optional[GoldSpec.AddTables.Custom]

filter: Optional[str]

post_filter: Optional[str]

fields: Optional[List[FieldSpec]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecGoldPresetOverridesAddTablesInner])

Return type:

AddTables

to_api_obj()

Return type:

CoreV1DataSourceSpecGoldPresetOverridesAddTablesInner

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

omit_tables: Optional[List[str]]

modify_tables: Optional[List[GoldSpec.ModifyTables]]

add_tables: Optional[List[GoldSpec.AddTables]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceSpecGold])

Return type:

GoldSpec

to_api_obj()

Return type:

CoreV1DataSourceSpecGold

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class dasl_client.types.datasource.FieldSpec(**data)

Bases: BaseModel

name

The name of the field.

Type:

Optional[str]

comment

The comment to apply to the field.

Type:

Optional[str]

var_assert

A list of SQL expressions that must evaluate to true for every processed row. If the assertion is false, an operational alert is raised using ‘message’ for each row.

Type:

Optional[List[FieldSpec.Assert]]

var_from

This field obtains its value from the source column of this name. Use this to bring in a column from some upstream table.

Type:

Optional[str]

alias

This field obtains its value from the destination (transformed) column of this name. Use this to alias a column from within the same table (ie. silver table). You cannot alias a column from some upstream table.

Type:

Optional[str]

expr

This field obtains its value from the given SQL expression.

Type:

Optional[str]

literal

This field obtains its value from the given literal string. For other data types, use expr.

Type:

Optional[str]

join

This field obtains its value from joining to another table.

Type:

Optional[FieldSpec.Join]

Parameters:

data (Any)

class Assert(**data)

Bases: BaseModel

An assertion within a FieldSpec.

expr

The SQL expression that must evaluate to true for every processed row.

Type:

Optional[str]

message

The message to include in the operational alert if the assertion fails.

Type:

Optional[str]

Parameters:

data (Any)

expr: Optional[str]

message: Optional[str]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceFieldSpecAssertInner])

Return type:

Assert

to_api_obj()

Return type:

CoreV1DataSourceFieldSpecAssertInner

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class Join(**data)

Bases: BaseModel

A join expression within a FieldSpec.

with_table

The table to join to.

Type:

Optional[str]

with_csv

The CSV configuration used for the join.

Type:

Optional[FieldSpec.Join.WithCSV]

lhs

The column in the source dataframe to join on.

Type:

Optional[str]

rhs

The column in the joined table to join on.

Type:

Optional[str]

select

A SQL expression to create the new field from the joined dataset.

Type:

Optional[str]

Parameters:

data (Any)

class WithCSV(**data)

Bases: BaseModel

A CSV file used for joins within a FieldSpec.

path

The path to the CSV file.

Type:

Optional[str]

Parameters:

data (Any)

path: Optional[str]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceFieldSpecJoinWithCSV])

Return type:

WithCSV

to_api_obj()

Return type:

CoreV1DataSourceFieldSpecJoinWithCSV

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

with_table: Optional[str]

with_csv: Optional[FieldSpec.Join.WithCSV]

lhs: Optional[str]

rhs: Optional[str]

select: Optional[str]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceFieldSpecJoin])

Return type:

Join

to_api_obj()

Return type:

CoreV1DataSourceFieldSpecJoin

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: Optional[str]

comment: Optional[str]

var_assert: Optional[List[FieldSpec.Assert]]

var_from: Optional[str]

alias: Optional[str]

expr: Optional[str]

literal: Optional[str]

join: Optional[FieldSpec.Join]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceFieldSpec])

Return type:

FieldSpec

to_api_obj()

Return type:

CoreV1DataSourceFieldSpec

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class dasl_client.types.datasource.FieldUtils(**data)

Bases: BaseModel

unreferenced_columns

Defines whether columns not referenced in the FieldSpecs should be preserved or omitted.

Type:

Optional[FieldUtils.UnreferencedColumns]

json_extract

A list of configurations for extracting JSON fields from a column.

Type:

Optional[List[FieldUtils.JsonExtract]]

Parameters:

data (Any)

class UnreferencedColumns(**data)

Bases: BaseModel

Configuration related to unreferenced columns.

preserve

Indicates whether columns not referenced in the FieldSpecs should be preserved.

Type:

Optional[bool]

embed_column

Specifies a name for a new column to contain all unreferenced fields.

Type:

Optional[str]

omit_columns

Lists columns to exclude from the output.

Type:

Optional[List[str]]

duplicate_prefix

Adds a prefix to resolve ambiguous duplicate field names.

Type:

Optional[str]

Parameters:

data (Any)

preserve: Optional[bool]

embed_column: Optional[str]

omit_columns: Optional[List[str]]

duplicate_prefix: Optional[str]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceFieldUtilsUnreferencedColumns])

Return type:

UnreferencedColumns

to_api_obj()

Return type:

CoreV1DataSourceFieldUtilsUnreferencedColumns

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class JsonExtract(**data)

Bases: BaseModel

Configuration for extracting JSON fields from table columns.

source

The column name containing JSON string(s) to extract from.

Type:

Optional[str]

omit_fields

Specifies high-level fields to exclude from extraction.

Type:

Optional[List[str]]

duplicate_prefix

Adds a prefix to resolve duplicate field names during extraction.

Type:

Optional[str]

embed_column

Specifies a column name to store the extracted JSON object.

Type:

Optional[str]

Parameters:

data (Any)

source: Optional[str]

omit_fields: Optional[List[str]]

duplicate_prefix: Optional[str]

embed_column: Optional[str]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceFieldUtilsJsonExtractInner])

Return type:

JsonExtract

to_api_obj()

Return type:

CoreV1DataSourceFieldUtilsJsonExtractInner

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

unreferenced_columns: Optional[FieldUtils.UnreferencedColumns]

json_extract: Optional[List[FieldUtils.JsonExtract]]

static from_api_obj(obj)

Parameters:

obj (Optional[CoreV1DataSourceFieldUtils])

Return type:

FieldUtils

to_api_obj()

Return type:

CoreV1DataSourceFieldUtils

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Configuration Helper Types

class dasl_client.types.workspace_config.DatasourcesConfig(**data)

Bases: BaseModel

Configuration settings used by Datasources.

bronze_schema

Name of the bronze schema in the catalog.

Type:

Optional[str]

silver_schema

Name of the silver schema in the catalog.

Type:

Optional[str]

gold_schema

Name of the gold schema in the catalog.

Type:

Optional[str]

catalog_name

The catalog name to use as the resource’s default.

Type:

Optional[str]

checkpoint_location

The base checkpoint location to use in Rule notebooks.

Type:

Optional[str]

default_compute_mode

The default compute mode to use for datasource jobs.

Type:

Optional[str]

Parameters:

data (Any)

catalog_name: Optional[str]

bronze_schema: Optional[str]

silver_schema: Optional[str]

gold_schema: Optional[str]

checkpoint_location: Optional[str]

default_compute_mode: Optional[str]

static from_api_obj(obj)

Parameters:

obj (Optional[WorkspaceV1WorkspaceConfigSpecDatasources])

Return type:

Optional[DatasourcesConfig]

to_api_obj()

Return type:

WorkspaceV1WorkspaceConfigSpecDatasources

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class dasl_client.types.workspace_config.RulesConfig(**data)

Bases: BaseModel

Configuration settings used by Rules.

checkpoint_location

The location to store checkpoints for streaming writes. If not provided, the daslStoragePath will be used.

Type:

Optional[str]

Parameters:

data (Any)

checkpoint_location: Optional[str]

static from_api_obj(obj)

Parameters:

obj (Optional[WorkspaceV1WorkspaceConfigSpecRules])

Return type:

RulesConfig

to_api_obj()

Return type:

WorkspaceV1WorkspaceConfigSpecRules

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].