DataChain¶
The DataChain
class creates a data chain, which is a sequence of data manipulation
steps such as reading data from storages, running AI or LLM models or calling external
services API to validate or enrich data. See DataChain
for examples of how to create a chain.
DataChain
¶
DataChain(
query: DatasetQuery,
settings: Settings,
signal_schema: SignalSchema,
setup: Optional[dict] = None,
_sys: bool = False,
)
DataChain - a data structure for batch data processing and evaluation.
It represents a sequence of data manipulation steps such as reading data from storages, running AI or LLM models or calling external services API to validate or enrich data.
Data in DataChain is presented as Python classes with arbitrary set of fields,
including nested classes. The data classes have to inherit from DataModel
class.
The supported set of field types include: majority of the type supported by the
underlyind library Pydantic
.
See Also
DataChain.from_storage("s3://my-bucket/my-dir/")
- reading unstructured
data files from storages such as S3, gs or Azure ADLS.
DataChain.save("name")
- saving to a dataset.
DataChain.from_dataset("name")
- reading from a dataset.
DataChain.from_values(fib=[1, 2, 3, 5, 8])
- generating from values.
DataChain.from_pandas(pd.DataFrame(...))
- generating from pandas.
DataChain.from_json("file.json")
- generating from json.
DataChain.from_csv("file.csv")
- generating from csv.
DataChain.from_parquet("file.parquet")
- generating from parquet.
Example
import os
from mistralai.client import MistralClient
from mistralai.models.chat_completion import ChatMessage
from datachain.dc import DataChain, Column
PROMPT = (
"Was this bot dialog successful? "
"Describe the 'result' as 'Yes' or 'No' in a short JSON"
)
model = "mistral-large-latest"
api_key = os.environ["MISTRAL_API_KEY"]
chain = (
DataChain.from_storage("gs://datachain-demo/chatbot-KiT/")
.limit(5)
.settings(cache=True, parallel=5)
.map(
mistral_response=lambda file: MistralClient(api_key=api_key)
.chat(
model=model,
response_format={"type": "json_object"},
messages=[
ChatMessage(role="user", content=f"{PROMPT}: {file.read()}")
],
)
.choices[0]
.message.content,
)
.save()
)
try:
print(chain.select("mistral_response").results())
except Exception as e:
print(f"do you have the right Mistral API key? {e}")
Source code in datachain/lib/dc.py
__or__
¶
agg
¶
agg(
func: Optional[Callable] = None,
partition_by: Optional[PartitionByType] = None,
params: Union[None, str, Sequence[str]] = None,
output: OutputType = None,
**signal_map
) -> Self
Aggregate rows using partition_by
statement and apply a function to the
groups of aggregated rows. The function needs to return new objects for each
group of the new rows. It returns a chain itself with new signals.
Input-output relationship: N:M
This method bears similarity to gen()
and map()
, employing a comparable set
of parameters, yet differs in two crucial aspects:
1. The partition_by
parameter: This specifies the column name or a list of
column names that determine the grouping criteria for aggregation.
2. Group-based UDF function input: Instead of individual rows, the function
receives a list all rows within each group defined by partition_by
.
Example
Source code in datachain/lib/dc.py
apply
¶
Apply any function to the chain.
Useful for reusing in a chain of operations.
Example
Source code in datachain/lib/dc.py
batch_map
¶
batch_map(
func: Optional[Callable] = None,
params: Union[None, str, Sequence[str]] = None,
output: OutputType = None,
batch: int = 1000,
**signal_map
) -> Self
This is a batch version of map()
.
Input-output relationship: N:N
It accepts the same parameters plus an additional parameter:
batch : Size of each batch passed to `func`. Defaults to 1000.
Example
Source code in datachain/lib/dc.py
c
¶
Returns Column instance attached to the current chain.
chunk
¶
Split a chain into smaller chunks for e.g. parallelization.
Example
Note
Bear in mind that index
is 0-indexed but total
isn't.
Use 0/3, 1/3 and 2/3, not 1/3, 2/3 and 3/3.
Source code in datachain/lib/dc.py
clone
¶
collect
¶
Yields rows of values, optionally limited to the specified columns.
Parameters:
-
*cols
(str
, default:()
) –Limit to the specified columns. By default, all columns are selected.
Yields:
-
DataType
–Yields a single item if a column is selected.
-
tuple[DataType, ...]
–Yields a tuple of items if multiple columns are selected.
Example
Iterating over all rows:
Iterating over all rows with selected columns:
Iterating over a single column:
Source code in datachain/lib/dc.py
collect_flatten
¶
Yields flattened rows of values as a tuple.
Parameters:
-
row_factory
–A callable to convert row to a custom format. It should accept two arguments: a list of column names and a tuple of row values.
Source code in datachain/lib/dc.py
column
¶
Returns Column instance with a type if name is found in current schema, otherwise raises an exception.
Source code in datachain/lib/dc.py
datasets
classmethod
¶
datasets(
session: Optional[Session] = None,
settings: Optional[dict] = None,
in_memory: bool = False,
object_name: str = "dataset",
include_listing: bool = False,
) -> DataChain
Generate chain with list of registered datasets.
Example
Source code in datachain/lib/dc.py
distinct
¶
Removes duplicate rows based on uniqueness of some input column(s) i.e if rows are found with the same value of input column(s), only one row is left in the result set.
Example:
Source code in datachain/lib/dc.py
exec
¶
export_files
¶
export_files(
output: str,
signal="file",
placement: ExportPlacement = "fullpath",
use_cache: bool = True,
) -> None
Method that exports all files from chain to some folder.
Source code in datachain/lib/dc.py
filter
¶
filter(*args: Any) -> Self
Filter the chain according to conditions.
Example
Basic usage with built-in operators
Using glob to match patterns
Using datachain.sql.functions
Combining filters with "or"
Combining filters with "and"
Source code in datachain/lib/dc.py
from_csv
classmethod
¶
from_csv(
path,
delimiter: str = ",",
header: bool = True,
output: OutputType = None,
object_name: str = "",
model_name: str = "",
source: bool = True,
nrows=None,
session: Optional[Session] = None,
settings: Optional[dict] = None,
**kwargs
) -> DataChain
Generate chain from csv files.
Parameters:
-
path
–Storage URI with directory. URI must start with storage prefix such as
s3://
,gs://
,az://
or "file:///". -
delimiter
–Character for delimiting columns.
-
header
–Whether the files include a header row.
-
output
–Dictionary or feature class defining column names and their corresponding types. List of column names is also accepted, in which case types will be inferred.
-
object_name
–Created object column name.
-
model_name
–Generated model name.
-
source
–Whether to include info about the source file.
-
nrows
–Optional row limit.
-
session
–Session to use for the chain.
-
settings
–Settings to use for the chain.
Example
Reading a csv file:
Reading csv files from a directory as a combined dataset:
Source code in datachain/lib/dc.py
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|
from_dataset
classmethod
¶
from_dataset(
name: str,
version: Optional[int] = None,
session: Optional[Session] = None,
settings: Optional[dict] = None,
) -> Self
Get data from a saved Dataset. It returns the chain itself.
Parameters:
-
name
–dataset name
-
version
–dataset version
Source code in datachain/lib/dc.py
from_hf
classmethod
¶
from_hf(
dataset: Union[str, HFDatasetType],
*args,
session: Optional[Session] = None,
settings: Optional[dict] = None,
object_name: str = "",
model_name: str = "",
**kwargs
) -> DataChain
Generate chain from huggingface hub dataset.
Parameters:
-
dataset
–Path or name of the dataset to read from Hugging Face Hub, or an instance of
datasets.Dataset
-like object. -
session
–Session to use for the chain.
-
settings
–Settings to use for the chain.
-
object_name
–Generated object column name.
-
model_name
–Generated model name.
-
kwargs
–Parameters to pass to datasets.load_dataset.
Example
Load from Hugging Face Hub:
Generate chain from loaded dataset:
Source code in datachain/lib/dc.py
from_json
classmethod
¶
from_json(
path,
type: Literal["binary", "text", "image"] = "text",
spec: Optional[DataType] = None,
schema_from: Optional[str] = "auto",
jmespath: Optional[str] = None,
object_name: Optional[str] = "",
model_name: Optional[str] = None,
print_schema: Optional[bool] = False,
meta_type: Optional[str] = "json",
nrows=None,
**kwargs
) -> DataChain
Get data from JSON. It returns the chain itself.
Parameters:
-
path
–storage URI with directory. URI must start with storage prefix such as
s3://
,gs://
,az://
or "file:///" -
type
–read file as "binary", "text", or "image" data. Default is "binary".
-
spec
–optional Data Model
-
schema_from
–path to sample to infer spec (if schema not provided)
-
object_name
–generated object column name
-
model_name
–optional generated model name
-
print_schema
–print auto-generated schema
-
jmespath
–optional JMESPATH expression to reduce JSON
-
nrows
–optional row limit for jsonl and JSON arrays
Example
infer JSON schema from data, reduce using JMESPATH
infer JSON schema from a particular path
Source code in datachain/lib/dc.py
from_jsonl
classmethod
¶
from_jsonl(
path,
type: Literal["binary", "text", "image"] = "text",
spec: Optional[DataType] = None,
schema_from: Optional[str] = "auto",
jmespath: Optional[str] = None,
object_name: Optional[str] = "",
model_name: Optional[str] = None,
print_schema: Optional[bool] = False,
meta_type: Optional[str] = "jsonl",
nrows=None,
**kwargs
) -> DataChain
Get data from JSON lines. It returns the chain itself.
Parameters:
-
path
–storage URI with directory. URI must start with storage prefix such as
s3://
,gs://
,az://
or "file:///" -
type
–read file as "binary", "text", or "image" data. Default is "binary".
-
spec
–optional Data Model
-
schema_from
–path to sample to infer spec (if schema not provided)
-
object_name
–generated object column name
-
model_name
–optional generated model name
-
print_schema
–print auto-generated schema
-
jmespath
–optional JMESPATH expression to reduce JSON
-
nrows
–optional row limit for jsonl and JSON arrays
Example
infer JSONl schema from data, limit parsing to 1 row
Source code in datachain/lib/dc.py
from_pandas
classmethod
¶
from_pandas(
df: DataFrame,
name: str = "",
session: Optional[Session] = None,
settings: Optional[dict] = None,
in_memory: bool = False,
object_name: str = "",
) -> DataChain
Generate chain from pandas data-frame.
Source code in datachain/lib/dc.py
from_parquet
classmethod
¶
from_parquet(
path,
partitioning: Any = "hive",
output: Optional[dict[str, DataType]] = None,
object_name: str = "",
model_name: str = "",
source: bool = True,
session: Optional[Session] = None,
settings: Optional[dict] = None,
**kwargs
) -> DataChain
Generate chain from parquet files.
Parameters:
-
path
–Storage URI with directory. URI must start with storage prefix such as
s3://
,gs://
,az://
or "file:///". -
partitioning
–Any pyarrow partitioning schema.
-
output
–Dictionary defining column names and their corresponding types.
-
object_name
–Created object column name.
-
model_name
–Generated model name.
-
source
–Whether to include info about the source file.
-
session
–Session to use for the chain.
-
settings
–Settings to use for the chain.
Example
Reading a single file:
Reading a partitioned dataset from a directory:
Source code in datachain/lib/dc.py
from_records
classmethod
¶
from_records(
to_insert: Optional[Union[dict, list[dict]]],
session: Optional[Session] = None,
settings: Optional[dict] = None,
in_memory: bool = False,
schema: Optional[dict[str, DataType]] = None,
) -> Self
Create a DataChain from the provided records. This method can be used for programmatically generating a chain in contrast of reading data from storages or other sources.
Parameters:
-
to_insert
–records (or a single record) to insert. Each record is a dictionary of signals and theirs values.
-
schema
–describes chain signals and their corresponding types
Source code in datachain/lib/dc.py
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|
from_storage
classmethod
¶
from_storage(
uri,
*,
type: Literal["binary", "text", "image"] = "binary",
session: Optional[Session] = None,
settings: Optional[dict] = None,
in_memory: bool = False,
recursive: Optional[bool] = True,
object_name: str = "file",
update: bool = False,
anon: bool = False
) -> Self
Get data from a storage as a list of file with all file attributes. It returns the chain itself as usual.
Parameters:
-
uri
–storage URI with directory. URI must start with storage prefix such as
s3://
,gs://
,az://
or "file:///" -
type
–read file as "binary", "text", or "image" data. Default is "binary".
-
recursive
–search recursively for the given path.
-
object_name
–Created object column name.
-
update
–force storage reindexing. Default is False.
-
anon
–If True, we will treat cloud bucket as public one
Source code in datachain/lib/dc.py
from_values
classmethod
¶
from_values(
ds_name: str = "",
session: Optional[Session] = None,
settings: Optional[dict] = None,
in_memory: bool = False,
output: OutputType = None,
object_name: str = "",
**fr_map
) -> DataChain
Generate chain from list of values.
Source code in datachain/lib/dc.py
gen
¶
gen(
func: Optional[Union[Callable, Generator]] = None,
params: Union[None, str, Sequence[str]] = None,
output: OutputType = None,
**signal_map
) -> Self
Apply a function to each row to create new rows (with potentially new signals). The function needs to return a new objects for each of the new rows. It returns a chain itself with new signals.
Input-output relationship: 1:N
This method is similar to map()
, uses the same list of parameters, but with
one key differences: It produces a sequence of rows for each input row (like
extracting multiple file records from a single tar file or bounding boxes from a
single image file).
Example
Source code in datachain/lib/dc.py
limit
¶
limit(n: int) -> Self
Return the first n
rows of the chain.
If the chain is unordered, which rows are returned is undefined.
If the chain has less than n
rows, the whole chain is returned.
Parameters:
-
n
(int
) –Number of rows to return.
Source code in datachain/lib/dc.py
listings
classmethod
¶
listings(
session: Optional[Session] = None,
in_memory: bool = False,
object_name: str = "listing",
**kwargs
) -> DataChain
Generate chain with list of cached listings. Listing is a special kind of dataset which has directory listing data of some underlying storage (e.g S3 bucket).
Source code in datachain/lib/dc.py
map
¶
map(
func: Optional[Callable] = None,
params: Union[None, str, Sequence[str]] = None,
output: OutputType = None,
**signal_map
) -> Self
Apply a function to each row to create new signals. The function should return a new object for each row. It returns a chain itself with new signals.
Input-output relationship: 1:1
Parameters:
-
func
–Function applied to each row.
-
params
–List of column names used as input for the function. Default is taken from function signature.
-
output
–Dictionary defining new signals and their corresponding types. Default type is taken from function signature. Default can be also taken from kwargs - **signal_map (see below). If signal name is defined using signal_map (see below) only a single type value can be used.
-
**signal_map
–kwargs can be used to define
func
together with it's return signal name in format ofmap(my_sign=my_func)
. This helps define signal names and function in a nicer way.
Example
Using signal_map and single type in output:
Using func and output as a map:
Source code in datachain/lib/dc.py
merge
¶
merge(
right_ds: DataChain,
on: Union[
str,
ColumnElement,
Sequence[Union[str, ColumnElement]],
],
right_on: Union[
str,
ColumnElement,
Sequence[Union[str, ColumnElement]],
None,
] = None,
inner=False,
rname="right_",
) -> Self
Merge two chains based on the specified criteria.
Parameters:
-
right_ds
–Chain to join with.
-
on
–Predicate or list of Predicates to join on. If both chains have the same predicates then this predicate is enough for the join. Otherwise,
right_on
parameter has to specify the predicates for the other chain. -
right_on
(Union[str, ColumnElement, Sequence[Union[str, ColumnElement]], None]
, default:None
) –Optional predicate or list of Predicates for the
right_ds
to join. -
inner
(bool
, default:False
) –Whether to run inner join or outer join.
-
rname
(str
, default:'right_'
) –name prefix for conflicting signal names.
Source code in datachain/lib/dc.py
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|
mutate
¶
Create new signals based on existing signals.
This method cannot modify existing columns. If you need to modify an
existing column, use a different name for the new column and then use
select()
to choose which columns to keep.
This method is vectorized and more efficient compared to map(), and it does not extract or download any data from the internal database. However, it can only utilize predefined built-in functions and their combinations.
The supported functions
Numerical: +, -, *, /, rand(), avg(), count(), func(), greatest(), least(), max(), min(), sum() String: length(), split() Filename: name(), parent(), file_stem(), file_ext() Array: length(), sip_hash_64(), euclidean_distance(), cosine_distance()
Example:
dc.mutate(
area=Column("image.height") * Column("image.width"),
extension=file_ext(Column("file.name")),
dist=cosine_distance(embedding_text, embedding_image)
)
This method can be also used to rename signals. If the Column("name") provided as value for the new signal - the old column will be dropped. Otherwise a new column is created.
Example:
Source code in datachain/lib/dc.py
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|
offset
¶
offset(offset: int) -> Self
Return the results starting with the offset row.
If the chain is unordered, which rows are skipped in undefined.
If the chain has less than offset
rows, the result is an empty chain.
Parameters:
-
offset
(int
) –Number of rows to skip.
Source code in datachain/lib/dc.py
order_by
¶
order_by(*args, descending: bool = False) -> Self
Orders by specified set of signals.
Parameters:
-
descending
(bool
, default:False
) –Whether to sort in descending order or not.
Source code in datachain/lib/dc.py
parse_tabular
¶
parse_tabular(
output: OutputType = None,
object_name: str = "",
model_name: str = "",
source: bool = True,
nrows: Optional[int] = None,
**kwargs
) -> Self
Generate chain from list of tabular files.
Parameters:
-
output
–Dictionary or feature class defining column names and their corresponding types. List of column names is also accepted, in which case types will be inferred.
-
object_name
–Generated object column name.
-
model_name
–Generated model name.
-
source
–Whether to include info about the source file.
-
nrows
–Optional row limit.
-
kwargs
–Parameters to pass to pyarrow.dataset.dataset.
Example
Reading a json lines file:
Reading a filtered list of files as a dataset:
Source code in datachain/lib/dc.py
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|
print_json_schema
¶
Print JSON data model and save it. It returns the chain itself.
Parameters:
-
jmespath
–JMESPATH expression to reduce JSON
-
model_name
–generated model name
Example
print JSON schema and save to column "meta_from":
Source code in datachain/lib/dc.py
print_jsonl_schema
¶
Print JSON data model and save it. It returns the chain itself.
Parameters:
-
jmespath
–JMESPATH expression to reduce JSON
-
model_name
–generated model name
Source code in datachain/lib/dc.py
print_schema
¶
sample
¶
Return a random sample from the chain.
Parameters:
-
n
(int
) –Number of samples to draw.
NOTE: Samples are not deterministic, and streamed/paginated queries or multiple workers will draw samples with replacement.
Source code in datachain/lib/dc.py
save
¶
Save to a Dataset. It returns the chain itself.
Parameters:
-
name
–dataset name. Empty name saves to a temporary dataset that will be removed after process ends. Temp dataset are useful for optimization.
-
version
–version of a dataset. Default - the last version that exist.
Source code in datachain/lib/dc.py
select
¶
Select only a specified set of signals.
Source code in datachain/lib/dc.py
select_except
¶
select_except(*args: str) -> Self
Select all the signals expect the specified signals.
Source code in datachain/lib/dc.py
settings
¶
settings(
cache=None,
parallel=None,
workers=None,
min_task_size=None,
sys: Optional[bool] = None,
) -> Self
Change settings for chain.
This function changes specified settings without changing not specified ones. It returns chain, so, it can be chained later with next operation.
Parameters:
-
cache
–data caching (default=False)
-
parallel
–number of thread for processors. True is a special value to enable all available CPUs (default=1)
-
workers
–number of distributed workers. Only for Studio mode. (default=1)
-
min_task_size
–minimum number of tasks (default=1)
Example
Source code in datachain/lib/dc.py
setup
¶
Setup variables to pass to UDF functions.
Use before running map/gen/agg/batch_map to save an object and pass it as an argument to the UDF.
Example
import anthropic
from anthropic.types import Message
(
DataChain.from_storage(DATA, type="text")
.settings(parallel=4, cache=True)
.setup(client=lambda: anthropic.Anthropic(api_key=API_KEY))
.map(
claude=lambda client, file: client.messages.create(
model=MODEL,
system=PROMPT,
messages=[{"role": "user", "content": file.get_value()}],
),
output=Message,
)
)
Source code in datachain/lib/dc.py
show
¶
show(
limit: int = 20,
flatten=False,
transpose=False,
truncate=True,
) -> None
Show a preview of the chain results.
Parameters:
-
limit
–How many rows to show.
-
flatten
–Whether to use a multiindex or flatten column names.
-
transpose
–Whether to transpose rows and columns.
-
truncate
–Whether or not to truncate the contents of columns.
Source code in datachain/lib/dc.py
shuffle
¶
subtract
¶
subtract(
other: DataChain,
on: Optional[Union[str, Sequence[str]]] = None,
right_on: Optional[Union[str, Sequence[str]]] = None,
) -> Self
Remove rows that appear in another chain.
Parameters:
-
other
(DataChain
) –chain whose rows will be removed from
self
-
on
(Optional[Union[str, Sequence[str]]]
, default:None
) –columns to consider for determining row equality in
self
. If unspecified, defaults to all common columns betweenself
andother
. -
right_on
(Optional[Union[str, Sequence[str]]]
, default:None
) –columns to consider for determining row equality in
other
. If unspecified, defaults to the same values ason
.
Source code in datachain/lib/dc.py
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to_columnar_data_with_names
¶
to_columnar_data_with_names(
chunk_size: int = DEFAULT_PARQUET_CHUNK_SIZE,
) -> tuple[list[str], Iterator[list[list[Any]]]]
Returns column names and the results as an iterator that provides chunks, with each chunk containing a list of columns, where each column contains a list of the row values for that column in that chunk. Useful for columnar data formats, such as parquet or other OLAP databases.
Source code in datachain/lib/dc.py
to_csv
¶
Save chain to a csv (comma-separated values) file.
Parameters:
-
path
–Path to save the file.
-
delimiter
–Delimiter to use for the resulting file.
Source code in datachain/lib/dc.py
to_pandas
¶
to_pandas(flatten=False) -> DataFrame
Return a pandas DataFrame from the chain.
Parameters:
-
flatten
–Whether to use a multiindex or flatten column names.
Source code in datachain/lib/dc.py
to_parquet
¶
to_parquet(
path: Union[str, PathLike[str], BinaryIO],
partition_cols: Optional[Sequence[str]] = None,
chunk_size: int = DEFAULT_PARQUET_CHUNK_SIZE,
**kwargs
) -> None
Save chain to parquet file with SignalSchema metadata.
Parameters:
-
path
–Path or a file-like binary object to save the file.
-
partition_cols
–Column names by which to partition the dataset.
-
chunk_size
–The chunk size of results to read and convert to columnar data, to avoid running out of memory.
Source code in datachain/lib/dc.py
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to_pytorch
¶
Convert to pytorch dataset format.
Parameters:
-
transform
(Transform
, default:None
) –Torchvision transforms to apply to the dataset.
-
tokenizer
(Callable
, default:None
) –Tokenizer to use to tokenize text values.
-
tokenizer_kwargs
(dict
, default:None
) –Additional kwargs to pass when calling tokenizer.
-
num_samples
(int
, default:0
) –Number of random samples to draw for each epoch. This argument is ignored if
num_samples=0
(the default).
Example
Source code in datachain/lib/dc.py
to_records
¶
Convert every row to a dictionary.
union
¶
Return the set union of the two datasets.
Parameters:
-
other
(Self
) –chain whose rows will be added to
self
.
DataChainError
¶
Session
¶
Session(
name="",
catalog: Optional[Catalog] = None,
client_config: Optional[dict] = None,
in_memory: bool = False,
)
Session is a context that keeps track of temporary DataChain datasets for a proper cleanup. By default, a global session is created.
Temporary or ephemeral datasets are the ones created without specified name. They are useful for optimization purposes and should be automatically removed.
Temp dataset has specific name format
"session_
The
Temp dataset examples
session_myname_624b41_48e8b4 session_4b962d_2a5dff
Parameters:
name (str): The name of the session. Only latters and numbers are supported. It can be empty. catalog (Catalog): Catalog object.
Source code in datachain/query/session.py
get
classmethod
¶
get(
session: Optional[Session] = None,
catalog: Optional[Catalog] = None,
client_config: Optional[dict] = None,
in_memory: bool = False,
) -> Session
Creates a Session() object from a catalog.
Parameters:
-
session
(Session
, default:None
) –Optional Session(). If not provided a new session will be created. It's needed mostly for simplie API purposes.
-
catalog
(Catalog
, default:None
) –Optional catalog. By default a new catalog is created.