semvec/pypi-packages huggingface dataset:
dbt-spark-livy 1.3.1
The dbt-spark-livy adapter plugin for Spark in Cloudera DataHub with Livy interface
Requires Python: >=3.7

# dbt-spark-livy

The `dbt-spark-livy` adapter allows you to use [dbt](https://www.getdbt.com/) along with [Apache spark-livy](https://spark.apache.org/) and [Cloudera Data Platform](https://cloudera.com) with Livy server support. This code bases use the dbt-spark project (https://github.com/dbt-labs/dbt-spark), and provides a Livy connectivity support over it. 

## Getting started

- [Install dbt](https://docs.getdbt.com/docs/installation)
- Read the [introduction](https://docs.getdbt.com/docs/introduction/) and [viewpoint](https://docs.getdbt.com/docs/about/viewpoint/)

## Running locally
A `docker-compose` environment starts a Spark Thrift server and a Postgres database as a Hive Metastore backend.
Note: dbt-spark now supports Spark 3.1.1 (formerly on Spark 2.x).

Python >= 3.8

dbt-core ~= 1.3.0

pyspark

sqlparams

requests_kerberos

requests-toolbelt

python-decouple


### Installing dbt-spark-livy

`pip install dbt-spark-livy`

### Profile Setup

```
demo_project:
  target: dev
  outputs:
    dev:
     type: spark_livy
     method: livy
     schema: my_db
     host: https://spark-livy-gateway.my.org.com/dbt-spark/cdp-proxy-api/livy_for_spark3/
     user: my_user
     password: my_pass
```

### Caveats
- While using livy , in the Livy UI if you notice sessions change state to dead from starting instead of idle, make sure there is a proper mapping for the user in the IDBroker mapping section 
- Actions > Manage Access > IDBroker Mappings . [Reference](https://docs.cloudera.com/cdf-datahub/7.2.15/flink-analyzing-data/topics/cdf-datahub-sa-create-idbroker-mapping.html)
- Also make sure the workload password is set either through UI or CLI. [Reference](https://docs.cloudera.com/management-console/cloud/user-management/topics/mc-setting-the-ipa-password.html)

## Supported features
Please see the original adapter documentation: https://github.com/dbt-labs/dbt-spark and https://docs.getdbt.com/reference/warehouse-profiles/spark-profile
cherrry 0.0.2
Cherrry Semantic Search API SDK
Requires Python: >=3.6

# cherrry-py

## Cherrry Python SDK

<img width="644" alt="CleanShot 2022-11-30 at 21 40 41@2x" src="https://user-images.githubusercontent.com/42971022/204960579-371f96b7-1281-4c7a-b95a-81e4c3139020.png">

[App](https://cherrry.com/dashboard) | [Docs](https://cherrry.com/docs) | [Blog](https://cherrry.com/blog)

## Installation + API keys

### pip package

Install with pip. [See it on pypi](https://pypi.org/project/cherrry)

```python
pip install cherrry
```

### API Keys

From https://cherrry.com/dashboard/api to get your API Keys

#### Private Key

Private keys start with `ch_prv`

**keep it secret and never use it client-side**. It has service role privilages: it can read + write data.

#### Public Key

Public keys start with `ch_pub`

They're intended to be use client-side and have read-only privilages.

### Initalize

```python
from cherrry import CherrryClient
```

initialize the client

```python
client = CherrryClient("your_api_key")
```

## Concepts

### Table

A table is a collection of documents.

### Document

A document is respresented as a JSON object literal with three fields: `text`, `image`, and `metadata`.
These fields are also JSON object literals, where the keys can be strings with any contents, and their values are also strings.

`text` and `image` are semantically searchable each by their type respectively. Each document must have either a `text` or `image` field. It can also have both fields.
`metadata` is used to store additional information and for filtering (feature in progress), it is an optional field.

## Basic Functions

### Create Table

```python
[success, error] = client.create_table("table_name")
```

### Insert a Doc

Documents must be of the following form

```python
{
    "text": {
        "a name for your text": "your desired text in a string"
    },
    "image": {
        "a name for your image": "a url to your downloadable image"
    },
    "metadata": {
        "key": "value"
    }
}
```

for example:

```python
[data, error] = client.table("recipes").insert({
    "text": {
        "name": "Octopus Cherry Pie"
    },
    "image": {
        "preview": "https://i.imgur.com/lFC8p0L.jpeg"
    },
    "metadata": {
        "author_name": "Davy Jones",
        "author_email": "octo@pus.com"
    }
})
```

### Search

```python
[data, error] = client
    .table("blogs")
    .search({ "prompt": "sea creature desert", "size": 1, "search_type": "image" });
```

### Get Doc by ID

The ID of documents are returned in the responses of `/search` or `/doc`

```python
[data, error] = client.table("blogs").doc("1234")
```

### Delete a Doc

```python
[success, error] = client.table("blogs").delete("1234")
```