semvec/pypi-packages huggingface dataset:
ocean-spark-connect 0.3.7
Spark Connect adapter for Ocean Spark
Requires Python: <4.0,>=3.9

# ocean-spark-connect
## Wrapper to create Spark Connect session for Spark Applications in Ocean

```
from ocean_spark_connect.ocean_spark_session import OceanSparkSession

spark = OceanSparkSession.Builder().cluster_id("osc-cluster").appid("appid").profile("default").getOrCreate()
spark.sql("select random()").show()
spark.stop()
```

To use periodic ping to keep the session alive, use the ping_interval option (in seconds). 
The default value is off (-1).

```
spark = OceanSparkSession.Builder() \
    .ping_interval(5.0) \
    .cluster_id("osc-cluster") \
    .appid("appid") \
    .profile("default") \
    .getOrCreate()
```

To use java Spark plugin for the websocket bridge instead, add the use_java(True) option.

### Options for OceanSparkSession.Builder with and without default values

In addition to the existing SparkSession.Builder option, the following options are available:

* token
* profile
* appid
* account_id
* cluster_id
* host = "api.spotinst.io"
* port = "15002"
* bind_address = "0.0.0.0"
JsonsPract 0.0.4
This is the simplest module for quick work with json files.
Requires Python: >=3.6

# Jsons Library #

## What is this? ##
The module allows you to work with json format files

## Quick Guide ##
The module allows you to create an object of the Jsons class and work with it as with standard json
	str1 = Jsons('name', 'Vasya')
	json_str1 = str1.to_json()


----------


### Using ###



Using the library is simple:

Let's import it first:
First, import everything from the library (use the `from `...` import *` construct).

Examples of all operations:

Create a JSON object from a Jsons string using the 'to_json' function:

	json1 = jsons1.to_json()


Create a Jsons object from a JSON string using the 'from_json' function:

	jsons1 = json1.from_json()

Concatenate two JSON structures using the 'concatenate_json' function:

	json12 = Jsons.concatenate_json(json1, json2)

Check if the JSON structure has balanced curly brackets and proper comma separators using the 'is_valid_json_structure' function:

	Jsons.is_valid_json_structure(json1)

Calculate the maximum depth of a JSON structure using the 'max_json_depth' function:

	Jsons.max_json_depth(json1)