How to partition Spark RDD when importing Postgres using JDBC? - postgresql

Question

I am importing a Postgres database into Spark. I know that I can partition on import, but that requires that I have a numeric column (I don't want to use the value column because it's all over the place and doesn't maintain order):
df = spark.read.format('jdbc').options(url=url, dbtable='tableName', properties=properties).load()
df.printSchema()
root
|-- id: string (nullable = false)
|-- timestamp: timestamp (nullable = false)
|-- key: string (nullable = false)
|-- value: double (nullable = false)
Instead, I am converting the dataframe into an rdd (of enumerated tuples) and trying to partition that instead:
rdd = df.rdd.flatMap(lambda x: enumerate(x)).partitionBy(20)
Note that I used 20 because I have 5 workers with one core each in my cluster, and 5*4=20.
Unfortunately, the following command still takes forever to execute:
result = rdd.first()
Therefore I am wondering if my logic above makes sense? Am I doing anything wrong? From the web GUI, it looks like the workers are not being used:

Answer 1

Since you already know you can partition by a numeric column this is probably what you should do. Here is the trick. First lets find a minimum and maximum epoch:
url = ...
properties = ...
min_max_query = """(
SELECT
CAST(min(extract(epoch FROM timestamp)) AS bigint),
CAST(max(extract(epoch FROM timestamp)) AS bigint)
FROM tablename
) tmp"""
min_epoch, max_epoch = spark.read.jdbc(
url=url, table=min_max_query, properties=properties
).first()
and use it to query the table:
numPartitions = ...
query = """(
SELECT *, CAST(extract(epoch FROM timestamp) AS bigint) AS epoch
FROM tablename) AS tmp"""
spark.read.jdbc(
url=url, table=query,
lowerBound=min_epoch, upperBound=max_epoch + 1,
column="epoch", numPartitions=numPartitions, properties=properties
).drop("epoch")
Since this splits data into ranges of the same size it is relatively sensitive to data skew so you should use it with caution.
You could also provide a list of disjoint predicates as a predicates argument.
predicates= [
"id BETWEEN 'a' AND 'c'",
"id BETWEEN 'd' AND 'g'",
... # Continue to get full coverage an desired number of predicates
]
spark.read.jdbc(
url=url, table="tablename", properties=properties,
predicates=predicates
)
The latter approach is much more flexible and can address certain issues with non-uniform data distribution but requires more knowledge about the data.
Using partitionBy fetches data first and then performs full shuffle to get desired number of partitions so it is relativistically expensive.