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anguillanneuf merged 2 commits into from
Jan 28, 2022
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feat(pubsublite): attributes field in sink example #7405

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feat(pubsublite): attributes field in sink example
anguillanneuf Jan 28, 2022
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Merge branch 'main' into pubsublite-spark-attributes
anguillanneuf Jan 28, 2022
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28 changes: 24 additions & 4 deletions pubsublite/spark-connector/spark_streaming_to_pubsublite_example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@ def spark_streaming_to_pubsublite(
) -> None:
# [START pubsublite_spark_streaming_to_pubsublite]
from pyspark.sql import SparkSession
from pyspark.sql.functions import array, create_map, col, lit, when
from pyspark.sql.types import BinaryType, StringType
import uuid

Expand All @@ -35,13 +36,32 @@ def spark_streaming_to_pubsublite(
# |-- value: long (nullable = true)
sdf = spark.readStream.format("rate").option("rowsPerSecond", 1).load()

# Transform the dataframe to match the required data fields and data types:
# https://github.com/googleapis/java-pubsublite-spark#data-schema
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sdf = (
sdf.withColumn("key", (sdf.value % 5).cast(StringType()).cast(BinaryType()))
.withColumn("event_timestamp", sdf.timestamp)
.withColumn("data", sdf.value.cast(StringType()).cast(BinaryType()))
.drop("value", "timestamp")
sdf.withColumn("key", lit("example").cast(BinaryType()))
.withColumn("data", col("value").cast(StringType()).cast(BinaryType()))
.withColumnRenamed("timestamp", "event_timestamp")
# Populate the attributes field. For example, an even value will
# have {"key1", [b"even"]}.
.withColumn(
"attributes",
create_map(
lit("key1"),
array(when(col("value") % 2 == 0, b"even").otherwise(b"odd")),
),
)
.drop("value")
)

# After the transformation, the schema of the dataframe should look like:
# |-- key: binary (nullable = false)
# |-- data: binary (nullable = true)
# |-- event_timestamp: timestamp (nullable = true)
# |-- attributes: map (nullable = false)
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@jiangmichaellll jiangmichaellll Jan 28, 2022

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our guide and everywhere actually specifies attributes is nullable.

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@jiangmichaellll jiangmichaellll Jan 28, 2022 •
edited
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but our code doesn't really check since they are compatible ie our codebase is ok with nullable, I think it's fine.

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@anguillanneuf anguillanneuf Jan 28, 2022 •
edited
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@jiangmichaellll Yes, all the fields can be nulls. It's just we are printing out of the dataframe created by the code above.

# | |-- key: string
# | |-- value: array (valueContainsNull = false)
# | | |-- element: binary (containsNull = false)
sdf.printSchema()

query = (
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