> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wherobots.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Set Up WherobotsDB

WherobotsDB provides various SQL APIs to work with vector and raster data.
For vector data, it follows SQL/MM Part3 Spatial SQL Standard. WherobotsDB maintains
support for the existing public APIs found in Apache Sedona.

To make existing Sedona users feel comfortable, our documentation still
uses the word `Sedona` in the context of WherobotsDB.

## Create SedonaContext

To start using WherobotsDB, we need to launch a SedonaContext in Wherobots cloud:

<Tabs>
  <Tab title="Python">
    ```python theme={"system"}
    from sedona.spark import *

    config = SedonaContext.builder().getOrCreate()
    sedona = SedonaContext.create(config)
    ```
  </Tab>

  <Tab title="Scala">
    ```scala theme={"system"}
    import org.apache.sedona.spark.SedonaContext

    val config = SedonaContext.builder().getOrCreate()
    val sedona = SedonaContext.create(config)
    ```
  </Tab>

  <Tab title="Java">
    ```java theme={"system"}
    import org.apache.spark.sql.SparkSession;
    import org.apache.sedona.spark.SedonaContext;

    SparkSession config = SedonaContext.builder().getOrCreate();
    SparkSession sedona = SedonaContext.create(config);
    ```
  </Tab>
</Tabs>

Alternatively, `expr` and `selectExpr` can be used:

```scala theme={"system"}
myDataFrame.withColumn("geometry", expr("ST_*")).selectExpr("ST_*")
```

```scala theme={"system"}
myDataFrame.withColumn("raster", expr("RS_*")).selectExpr("RS_*")
```

Detailed Spatial SQL APIs for vector and raster can be found at [Vector SQL APIs](/reference/wherobots-db/geometry-data/overview/) and [Raster SQL APIs](/reference/wherobots-db/raster-data/overview/) respectively. References of Havasu table format can be found at [Havasu APIs](/reference/havasu/geometry/geometry-overview/)

## Quick start

After starting SedonaContext, users can start working with WherobotsDB on Wherobots cloud without any setup.

### Create a WherobotsDB Havasu table

Wherobots cloud provides a set of default configurations for the users to store data under the S3 prefix allocated for the users. Simply use `wherobots` as the catalog name when creating a Havasu table.

<Tabs>
  <Tab title="Python">
    ```python theme={"system"}
    sedona.sql("CREATE DATABASE IF NOT EXISTS org_catalog.test_db")
    sedona.sql("""
    CREATE TABLE IF NOT EXISTS org_catalog.test_db.taxi (
      pickup GEOMETRY,
      Trip_Pickup_DateTime STRING,
      Payment_Type STRING,
      Fare_Amt DECIMAL
    ) USING havasu.iceberg
    """)
    ```
  </Tab>

  <Tab title="Scala">
    ```scala theme={"system"}
    sedona.sql("CREATE DATABASE IF NOT EXISTS org_catalog.test_db")
    sedona.sql("""
    CREATE TABLE IF NOT EXISTS org_catalog.test_db.taxi (
      pickup GEOMETRY,
      Trip_Pickup_DateTime STRING,
      Payment_Type STRING,
      Fare_Amt DECIMAL
    )
    """)
    ```
  </Tab>

  <Tab title="Java">
    ```java theme={"system"}
    sedona.sql("CREATE DATABASE IF NOT EXISTS org_catalog.test_db");
    sedona.sql(
      "CREATE TABLE IF NOT EXISTS org_catalog.test_db.taxi (" +
      "pickup GEOMETRY," +
      "Trip_Pickup_DateTime STRING," +
      "Payment_Type STRING," +
      "Fare_Amt DECIMAL" +
      ")");
    ```
  </Tab>
</Tabs>

### Read data from external storage

User can ingest and query data using WherobotsDB. For example, users can load an example CSV file:

<Tabs>
  <Tab title="Python">
    ```python theme={"system"}
    taxidf = sedona.read.format('csv').option("header","true").option("delimiter", ",").load("s3://wherobots-examples/data/nyc-taxi-data.csv")
    taxidf = taxidf.selectExpr('ST_Point(CAST(Start_Lon AS Decimal(24,20)), CAST(Start_Lat AS Decimal(24,20))) AS pickup', 'Trip_Pickup_DateTime', 'Payment_Type', 'CAST(Fare_Amt AS DECIMAL)')
    taxidf = taxidf.filter(col("pickup").isNotNull())
    ```
  </Tab>

  <Tab title="Scala">
    ```scala theme={"system"}
    val taxidf = sedona.read.format("csv").option("header","true").option("delimiter", ",").load("s3://wherobots-examples/data/nyc-taxi-data.csv")
    val taxidf = taxidf.selectExpr("ST_Point(CAST(Start_Lon AS Decimal(24,20)), CAST(Start_Lat AS Decimal(24,20))) AS pickup", "Trip_Pickup_DateTime", "Payment_Type", "CAST(Fare_Amt AS DECIMAL)")
    val taxidf = taxidf.filter(col("pickup").isNotNull)
    ```
  </Tab>

  <Tab title="Java">
    ```java theme={"system"}
    Dataset<Row> taxidf = sedona.read().format("csv").option("header","true").option("delimiter", ",").load("s3://wherobots-examples/data/nyc-taxi-data.csv");
    Dataset<Row> taxidf = taxidf.selectExpr("ST_Point(CAST(Start_Lon AS Decimal(24,20)), CAST(Start_Lat AS Decimal(24,20))) AS pickup", "Trip_Pickup_DateTime", "Payment_Type", "CAST(Fare_Amt AS DECIMAL)");
    Dataset<Row> taxidf = taxidf.filter(col("pickup").isNotNull());
    ```
  </Tab>
</Tabs>

### Insert data to a WherobotsDB Havasu table

User can insert the data above to an existing WherobotsDB table.

<Tabs>
  <Tab title="Python">
    ```python theme={"system"}
    taxidf.writeTo("org_catalog.test_db.taxi").append()
    ```
  </Tab>

  <Tab title="Scala">
    ```scala theme={"system"}
    taxidf.writeTo("org_catalog.test_db.taxi").append()
    ```
  </Tab>

  <Tab title="Java">
    ```java theme={"system"}
    taxidf.writeTo("org_catalog.test_db.taxi").append();
    ```
  </Tab>
</Tabs>

### Read from a WherobotsDB Havasu table

User can immediately query the data stored in Havasu table.

<Tabs>
  <Tab title="Python">
    ```python theme={"system"}
    df_result = sedona.sql("SELECT ST_Buffer(pickup, 0.001) pickup_buf FROM org_catalog.test_db.taxi WHERE ST_Within(pickup, ST_PolygonFromEnvelope(-74, 40, -73, 41))")
    df_result.show()
    ```
  </Tab>

  <Tab title="Scala">
    ```scala theme={"system"}
    val df_result = sedona.sql("SELECT ST_Buffer(pickup, 0.001) pickup_buf FROM org_catalog.test_db.taxi WHERE ST_Within(pickup, ST_PolygonFromEnvelope(-74, 40, -73, 41))")
    df_result.show()
    ```
  </Tab>

  <Tab title="Java">
    ```java theme={"system"}
    Dataset<Row> df_result = sedona.sql("SELECT ST_Buffer(pickup, 0.001) pickup_buf FROM org_catalog.test_db.taxi WHERE ST_Within(pickup, ST_PolygonFromEnvelope(-74, 40, -73, 41))");
    df_result.show();
    ```
  </Tab>
</Tabs>

## Feature Limitations

* **MOR Tables**: Havasu does not support MOR tables. We will add MOR support to Havasu in the future.
* **File Formats**: Havasu only supports storing geometry or raster values in parquet files. Other formats such as ORC or Avro are not supported.