How to use a predicate while reading from JDBC connection?

Question

By default, spark_read_jdbc() reads an entire database table into Spark. I've used the following syntax to create these connections.

library(sparklyr)
library(dplyr)

config <- spark_config()
config$`sparklyr.shell.driver-class-path` <- "mysql-connector-java-5.1.43/mysql-connector-java-5.1.43-bin.jar"

sc <- spark_connect(master         = "local",
                    version        = "1.6.0",
                    hadoop_version = 2.4,
                    config         = config)

db_tbl <- sc %>%
  spark_read_jdbc(sc      = .,
                  name    = "table_name",  
                  options = list(url      = "jdbc:mysql://localhost:3306/schema_name",
                                 user     = "root",
                                 password = "password",
                                 dbtable  = "table_name"))

However, I've now encountered the scenario where I have a table in a MySQL database and I would prefer to only read in a subset of this table into Spark.

How do I get spark_read_jdbc to accept a predicate? I've tried adding the predicate to the options list without success,

db_tbl <- sc %>%
  spark_read_jdbc(sc      = .,
                  name    = "table_name",  
                  options = list(url      = "jdbc:mysql://localhost:3306/schema_name",
                                 user       = "root",
                                 password   = "password",
                                 dbtable    = "table_name",
                                 predicates = "field > 1"))

Mccool

Answer 1

You can replace dbtable with query:

db_tbl <- sc %>%
  spark_read_jdbc(sc      = .,
              name    = "table_name",  
              options = list(url      = "jdbc:mysql://localhost:3306/schema_name",
                             user     = "root",
                             password = "password",
                             dbtable  = "(SELECT * FROM table_name WHERE field > 1) as my_query"))

but with simple condition like this Spark should push it automatically when you filter:

db_tbl %>% filter(field > 1)

Just make sure to set:

memory = FALSE

in spark_read_jdbc.

Disappear