Скачать или смотреть Databricks Interview Question: How do you optimize a slow streaming job in Databricks?

Hey everyone! 👋 Welcome back to the channel!

Today, we’re covering another important Databricks interview question:

What steps would you take if a streaming job in Databricks was processing events too slowly?

"The answer? You can optimize performance by:

✅ Increasing micro-batch intervals
✅ Optimizing stateful aggregations

Let’s break it down step by step!

In a real-world scenario, slow streaming jobs can cause delays, high latency, and memory bottlenecks. But don’t worry! We can tune them with the right optimizations.

Now, let's dive into some PySpark code to see how we can speed up a slow streaming job!

Step 1: Initialize Spark Session
from pyspark.sql import SparkSession
from pyspark.sql.functions import col

spark = SparkSession.builder \
.appName("Optimize_Slow_Streaming") \
.getOrCreate()

Step 2: Read Streaming Data with Optimized Micro-Batch Interval
df = (spark.readStream
.format("rate")
.option("rowsPerSecond", 100) # Increase micro-batch interval by adjusting event rate
.load())

Step 3: Perform Optimized Stateful Aggregation
from pyspark.sql.functions import window
aggregated_df = (df.groupBy(window(col("timestamp"), "10 seconds"))
.agg({'value': 'sum'}))

Step 4: Write Streaming Output with Optimized Checkpointing
query = (aggregated_df.writeStream
.format("console")
.outputMode("update")
.option("checkpointLocation", "/tmp/checkpoint_slow_stream") # Ensure checkpointing for fault tolerance
.trigger(processingTime="10 seconds") # Increase processing interval to optimize performance
.start())

query.awaitTermination()

Now, let’s break this down :

📌 Step 1: We start a Spark session to process streaming data.

📌 Step 2: We read streaming data from a rate source, simulating an event stream.

Here, we set rowsPerSecond to 100. This controls how often new events are processed, effectively increasing the micro-batch interval.

📌 Step 3: We optimize stateful aggregations by using window functions.
We group events into 10-second windows and calculate the sum of values.

📌 Step 4: We write the streaming output to the console.
Checkpointing is enabled to ensure fault tolerance.
We set processingTime to 10 seconds, helping Databricks batch events efficiently and reduce overhead.