python - 读取 (Pyspark? 中的 JDBC 源代码时出现不支持的数组错误)

Question

尝试将 postgreSQL DB 转换为 Dataframe 。以下是我的代码:

from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("Connect to DB") \
    .getOrCreate()

jdbcUrl = "jdbc:postgresql://XXXXXX" 
connectionProperties = {
  "user" : " ",
  "password" : " ",
  "driver" : "org.postgresql.Driver"
}

query = "(SELECT table_name FROM information_schema.tables) XXX"
df = spark.read.jdbc(url=jdbcUrl, table=query, properties=connectionProperties)

table_name_list = df.select("table_name").rdd.flatMap(lambda x: x).collect() 
    for table_name in table_name_list:
          df2 = spark.read.jdbc(url=jdbcUrl, table=table_name, properties=connectionProperties)

我收到错误:

java.sql.SQLException: Unsupported type ARRAY on generating df2 for table name

如果我对表名称值进行硬编码，则不会收到相同的错误

df2 = spark.read.jdbc(jdbcUrl,"conditions",properties=connectionProperties) 

我检查了 table_name 类型，它是 String ，这是正确的方法吗？

Answer 1

我猜你不想要属于 postgres 内部工作的表名，例如 pg_type , pg_policies等等，其架构类型为 pg_catalog导致错误的原因

py4j.protocol.Py4JJavaError: An error occurred while calling o34.jdbc. : java.sql.SQLException: Unsupported type ARRAY

当你尝试将它们读作

spark.read.jdbc(url=jdbcUrl, table='pg_type', properties=connectionProperties)

并且有诸如applicable_roles之类的表, view_table_usage等等，其架构类型为 information_schema这导致

py4j.protocol.Py4JJavaError: An error occurred while calling o34.jdbc. : org.postgresql.util.PSQLException: ERROR: relation "view_table_usage" does not exist

当你尝试将它们读作

spark.read.jdbc(url=jdbcUrl, table='view_table_usage', properties=connectionProperties)

可以使用上述jdbc命令将模式类型为公共(public)的表读入表中。

I checked table_name type and it is String , is this the correct approach ?

因此，您需要过滤掉这些表名称并将逻辑应用为

from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("Connect to DB") \
    .getOrCreate()

jdbcUrl = "jdbc:postgresql://hostname:post/" 
connectionProperties = {
  "user" : " ",
  "password" : " ",
  "driver" : "org.postgresql.Driver"
}

query = "information_schema.tables"
df = spark.read.jdbc(url=jdbcUrl, table=query, properties=connectionProperties)

table_name_list = df.filter((df["table_schema"] != 'pg_catalog') & (df["table_schema"] != 'information_schema')).select("table_name").rdd.flatMap(lambda x: x).collect() 
    for table_name in table_name_list:
          df2 = spark.read.jdbc(url=jdbcUrl, table=table_name, properties=connectionProperties)

这应该有效