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

# Spark: show function

### **`show()` Function in Spark**

The `show()` function in Spark is used to display the contents of a [DataFrame](/glossary/dataframe) or [Dataset](/glossary/dataset) in a tabular format. It is one of the most commonly used actions in Spark for debugging and inspecting data. By default, `show()` displays the first 20 rows of the DataFrame, but you can customize the number of rows and whether to truncate long strings.

***

## 1. **Syntax**

**PySpark:**

```python theme={"system"}
df.show(n=20, truncate=True)
```

**Spark SQL**:

* There is no direct equivalent in Spark SQL, but you can use `SELECT * FROM table_name LIMIT n` to achieve similar results.

## 2. **Parameters**

* **n**: The number of rows to display (default is 20).
* **truncate**: If `True`, truncates long strings to 20 characters. If an integer is provided, truncates strings to that length.

## 3. **Key Features**

* **Action**: `show()` is an action, meaning it triggers the execution of the Spark job.
* **Tabular Format**: Displays the DataFrame in a readable tabular format.
* **Customizable**: You can control the number of rows and truncation of long strings.

## 4. **Examples**

### **Example 1: Displaying the First 20 Rows**

**PySpark:**

```python theme={"system"}
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("ShowExample").getOrCreate()

# Create DataFrame
data = [("Anand", 25), ("Bala", 30), ("Kavitha", 28), ("Raj", 35)]
columns = ["Name", "Age"]

df = spark.createDataFrame(data, columns)

# Display the first 20 rows
df.show()
```

**Output:**

```
+-------+---+
|   Name|Age|
+-------+---+
|  Anand| 25|
|   Bala| 30|
|Kavitha| 28|
|    Raj| 35|
+-------+---+
```

### **Example 2: Displaying a Specific Number of Rows**

**PySpark:**

```python theme={"system"}
# Display the first 2 rows
df.show(2)
```

**Output:**

```
+-----+---+
| Name|Age|
+-----+---+
|Anand| 25|
| Bala| 30|
+-----+---+
only showing top 2 rows
```

### **Example 3: Disabling Truncation**

**PySpark:**

```python theme={"system"}
# Create DataFrame with long strings
data = [("Anand", "This is a very long string that will be truncated"), 
        ("Bala", "Another long string that will be truncated")]
columns = ["Name", "Description"]

df = spark.createDataFrame(data, columns)

# Display without truncation
df.show(truncate=False)
```

**Output:**

```
+-----+---------------------------------------------+
|Name |Description                                  |
+-----+---------------------------------------------+
|Anand|This is a very long string that will be truncated|
|Bala |Another long string that will be truncated   |
+-----+---------------------------------------------+
```

### **Example 4: Displaying All Columns Without Truncation**

**PySpark:**

```python theme={"system"}
# Display all columns without truncation
df.show(truncate=False)
```

**Output:**

```
+-------+---------------------------------------------+
|Name   |Description                                  |
+-------+---------------------------------------------+
|Anand  |This is a very long string that will be truncated|
|Bala   |Another long string that will be truncated   |
+-------+---------------------------------------------+
```

### **Example 5: Displaying a Subset of Columns**

**PySpark:**

```python theme={"system"}
# Display only the 'Name' column
df.select("Name").show()
```

**Output:**

```
+-------+
|   Name|
+-------+
|  Anand|
|   Bala|
|Kavitha|
|    Raj|
+-------+
```

### **Example 6: Displaying Data with Custom Truncation Length**

**PySpark:**

```python theme={"system"}
# Display with custom truncation length (e.g., 10 characters)
df.show(truncate=10)
```

**Output:**

```
+-------+-----------+
|   Name| Description|
+-------+-----------+
|  Anand|This is a...|
|   Bala|Another ...|
+-------+-----------+
```

### **Example 7: Displaying Data with Vertical Format**

**PySpark:**

```python theme={"system"}
# Display data in vertical format
df.show(vertical=True)
```

**Output:**

```
-RECORD 0-------------------
 Name        | Anand         
 Description | This is a very long string that will be truncated
-RECORD 1-------------------
 Name        | Bala          
 Description | Another long string that will be truncated
```

## 5. **Common Use Cases**

* Debugging and inspecting data during development.
* Displaying sample data for analysis or reporting.
* Verifying the results of transformations or aggregations.

## 6. **Performance Considerations**

* **Execution Overhead**: `show()` triggers the execution of the entire DataFrame lineage, so use it carefully for large datasets.
* **Truncation**: By default, long strings are truncated to 20 characters. Use `truncate=False` to display full strings.

## 7. **Key Takeaways**

1. **Purpose**: The `show()` function is used to display the contents of a DataFrame or Dataset in a tabular format.
2. **Action**: It is an action that triggers the execution of the Spark job.
3. In Spark SQL, similar functionality can be achieved using `SELECT * FROM table_name LIMIT n`.