broadcast joins aqe adaptive query execution

Download 1M+ code from https://codegive.com/a126670
certainly! let's delve into broadcast joins and adaptive query execution (aqe) in apache spark, which are essential for optimizing the performance of spark sql queries.

what is a broadcast join?

a **broadcast join** is a type of join operation in spark used when one of the tables being joined is small enough to fit into memory. instead of shuffling the larger dataset across the network, spark broadcasts the smaller dataset to all executors. this minimizes data transfer and significantly improves performance.

what is adaptive query execution (aqe)?

**adaptive query execution (aqe)** is a feature introduced in spark 3.0 that allows spark to optimize query execution plans at runtime based on the actual data statistics. aqe can make decisions such as whether to use a broadcast join or a sort-merge join based on the size of the datasets involved.

enabling aqe

to enable aqe in spark, you need to set the following configurations:

```python
spark.conf.set("spark.sql.adaptive.enabled", "true")
spark.conf.set("spark.sql.adaptive.shuffle.targetpostshuffleinputsize", "134217728") 128 mb
```

code example of broadcast joins with aqe

let’s illustrate the use of broadcast joins and aqe with a simple example.

```python
from pyspark.sql import sparksession
from pyspark.sql.functions import broadcast

create a spark session
spark = sparksession.builder \
.appname("broadcast join and aqe example") \
.config("spark.sql.adaptive.enabled", "true") \
.getorcreate()

create a large dataframe
large_data = [(i, f"largedata_{i}") for i in range(100000)]
large_df = spark.createdataframe(large_data, ["id", "value"])

create a small dataframe
small_data = [(i, f"smalldata_{i}") for i in range(10)]
small_df = spark.createdataframe(small_data, ["id", "value"])

show dataframe sizes
print(f"largest dataframe count: {large_df.count()}")
print(f"small dataframe count: {small_df.count()}")

using a broadcast join
this will utilize aqe to decide whether to broadcast the ...

#BroadcastJoins #AQE #numpy
Broadcast joins
AQE
Adaptive Query Execution
Spark optimization
distributed computing
query performance
data processing
big data analytics
join strategies
data shuffling
execution plan
cluster efficiency
workload balancing
runtime optimization
SQL performance

Видео broadcast joins aqe adaptive query execution канала CodeHelp