Hi, Josh:
Thanks for the answer. Do you know the underlining difference between the
following two ways of Loading a Dataframe? (using the Data Source API, or Load
as a DataFrame directly using a Configuration object)
Is there a Java interface to use the functionality of phoenixTableAsDataFrame,
saveToPhoenix ?
Thanks
Xindian
Load as a DataFrame using the Data Source API
import org.apache.spark.SparkContext
import org.apache.spark.sql.SQLContext
import org.apache.phoenix.spark._
val sc = new SparkContext("local", "phoenix-test")
val sqlContext = new SQLContext(sc)
val df = sqlContext.load(
"org.apache.phoenix.spark",
Map("table" -> "TABLE1", "zkUrl" -> "phoenix-server:2181")
)
df
.filter(df("COL1") === "test_row_1" && df("ID") === 1L)
.select(df("ID"))
.show
Or Load Load as a DataFrame directly using a Configuration object
import org.apache.hadoop.conf.Configuration
import org.apache.spark.SparkContext
import org.apache.spark.sql.SQLContext
import org.apache.phoenix.spark._
val configuration = new Configuration()
// Can set Phoenix-specific settings, requires 'hbase.zookeeper.quorum'
val sc = new SparkContext("local", "phoenix-test")
val sqlContext = new SQLContext(sc)
// Load the columns 'ID' and 'COL1' from TABLE1 as a DataFrame
val df = sqlContext.phoenixTableAsDataFrame(
"TABLE1", Array("ID", "COL1"), conf = configuration
)
df.show
From: Josh Mahonin [mailto:jmaho...@gmail.com]
Sent: 2016年6月9日 9:44
To: user@phoenix.apache.org
Subject: Re: phoenix spark options not supporint query in dbtable
Hi Xindian,
The phoenix-spark integration is based on the Phoenix MapReduce layer, which
doesn't support aggregate functions. However, as you mentioned, both filtering
and pruning predicates are pushed down to Phoenix. With an RDD or DataFrame
loaded, all of Spark's various aggregation methods are available to you.
Although the Spark JDBC data source supports the full complement of Phoenix's
supported queries, the way it achieves parallelism is to split the query across
a number of workers and connections based on a 'partitionColumn' with a
'lowerBound' and 'upperBound', which must be numeric. If your use case has
numeric primary keys, then that is potentially a good solution for you. [1]
The phoenix-spark parallelism is based on the splits provided by the Phoenix
query planner, and has no requirements on specifying partition columns or
upper/lower bounds. It's up to you to evaluate which technique is the right
method for your use case. [2]
Good luck,
Josh
[1]
http://spark.apache.org/docs/latest/sql-programming-guide.html#jdbc-to-other-databases
[2] https://phoenix.apache.org/phoenix_spark.html
On Wed, Jun 8, 2016 at 6:01 PM, Long, Xindian
<xindian.l...@sensus.com<mailto:xindian.l...@sensus.com>> wrote:
The Spark JDBC data source supports to specify a query as the “dbtable” option.
I assume all queries in the above query in pushed down to the database instead
of done in Spark.
The phoenix spark plug in seems not supporting that. Why is that? Any plan in
the future to support it?
I know phoenix spark does support an optional select clause and predicate push
down in some cases, but it is limited.
Thanks
Xindian
-------------------------------------------
Xindian “Shindian” Long
Mobile: 919-9168651<tel:919-9168651>
Email: xindian.l...@gmail.com<mailto:xindian.l...@gmail.com>
