updateStateByKey
时间: 2023-10-29 18:16:00 浏览: 39
updateStateByKey is a Spark Streaming operation that allows you to maintain state across batches of data. It is used to update the state of a DStream by applying a state update function to each batch of data in the stream.
updateStateByKey takes two arguments:
1. The state update function, which takes the current state and the new values for a key and returns the updated state for that key.
2. A checkpoint directory, which is used to store the state information between batches.
The state update function should take two arguments: the current state for a key and the new values for that key in the current batch. The function should then return the updated state for that key.
For example, if you have a DStream of (key, value) pairs and you want to maintain a count of the values for each key, you can use updateStateByKey to update the count for each key across batches.
Here is an example of how to use updateStateByKey:
```
from pyspark.streaming import StreamingContext
ssc = StreamingContext(sparkContext, 1)
# Create a DStream of (key, value) pairs
lines = ssc.socketTextStream("localhost", 9999)
pairs = lines.map(lambda x: (x.split(" ")[0], int(x.split(" ")[1])))
# Define the update function
def updateFunc(newValues, currentSum):
if currentSum is None:
currentSum = 0
return sum(newValues, currentSum)
# Use updateStateByKey to update the state
stateDstream = pairs.updateStateByKey(updateFunc)
# Print the state
stateDstream.pprint()
ssc.start()
ssc.awaitTermination()
```
In this example, we create a DStream of (key, value) pairs from a socket connection. We then define the update function to sum the new values for each key with the current sum. Finally, we use updateStateByKey to update the state and print the result.
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