Is it possible to read data from Microsoft Sql Server (and oracle, mysql, etc.) into an rdd in a Spark application? Or do we need to create an in memory set and parallize that into an RDD?
In Spark 1.4.0+ you can now use sqlContext.read.jdbc
That will give you a DataFrame instead of an RDD of Row objects.
The equivalent to the solution you posted above would be
sqlContext.read.jdbc("jdbc:sqlserver://omnimirror;databaseName=moneycorp;integratedSecurity=true;", "TABLE_NAME", "id", 1, 100000, 1000, new java.util.Properties)
It should pick up the schema of the table, but if you'd like to force it, you can use the schema method after read sqlContext.read.schema(...insert schema here...).jdbc(...rest of the things...)
Note that you won't get an RDD of SomeClass here (which is nicer in my view). Instead you'll get a DataFrame of the relevant fields.
More information can be found here: http://spark.apache.org/docs/latest/sql-programming-guide.html#jdbc-to-other-databases
Found a solution to this from the mailing list. JdbcRDD can be used to accomplish this. I needed to get the MS Sql Server JDBC driver jar and add it to the lib for my project. I wanted to use integrated security, and so needed to put sqljdbc_auth.dll (available in the same download) in a location that java.library.path can see. Then, the code looks like this:
val rdd = new JdbcRDD[Email](sc,
() => {DriverManager.getConnection(
"jdbc:sqlserver://omnimirror;databaseName=moneycorp;integratedSecurity=true;")},
"SELECT * FROM TABLE_NAME Where ? < X and X < ?",
1, 100000, 1000,
(r:ResultSet) => { SomeClass(r.getString("Col1"),
r.getString("Col2"), r.getString("Col3")) } )
This gives an Rdd of SomeClass.The second, third and fourth parameters are required and are for lower and upper bounds, and number of partitions. In other words, that source data needs to be partitionable by longs for this to work.
Related
I'm looking for an implementation to run text query ex: "g.V().limit(1).toList()" while using the PatitionStrategy in Apache TinkerPop.
I'm attempting to build a REST interface to run queries on selected graph paritions only. I know how to run a raw query using Client, but I'm looking for an implementation where I can create a multi-tenant graph (https://tinkerpop.apache.org/docs/current/reference/#partitionstrategy) and query only selected tenants using raw text query instead of a GLV. Im able to query only selected partitions using pythongremlin, but there is no reference implementation I could find to run a text query on a tenant.
Here is tenant query implementation
connection = DriverRemoteConnection('ws://megamind-ws:8182/gremlin', 'g')
g = traversal().withRemote(connection)
partition = PartitionStrategy(partition_key="partition_key",
write_partition="tenant_a",
read_partitions=["tenant_a"])
partitioned_g = g.withStrategies(partition)
x = partitioned_g.V.limit(1).next() <---- query on partition only
Here is how I execute raw query on entire graph, but Im looking for implementation to run text based queries on only selected partitions.
from gremlin_python.driver import client
client = client.Client('ws://megamind-ws:8182/gremlin', 'g')
results = client.submitAsync("g.V().limit(1).toList()").result().one() <-- runs on entire graph.
print(results)
client.close()
Any suggestions appreciated? TIA
It depends on how the backend store handles text mode queries, but for the query itself, essentially you just need to use the Groovy/Java style formulation. This will work with GremlinServer and Amazon Neptune. For other backends you will need to make sure that this syntax is supported. So from Python you would use something like:
client.submit('
g.withStrategies(new PartitionStrategy(partitionKey: "_partition",
writePartition: "b",
readPartitions: ["b"])).V().count()')
I have something like this, using Akka, Alpakka + Slick
Slick
.source(
sql"""select #${onlyTheseColumns.mkString(",")} from #${dbSource.table}"""
.as[Map[String, String]]
.withStatementParameters(rsType = ResultSetType.ForwardOnly, rsConcurrency = ResultSetConcurrency.ReadOnly, fetchSize = batchSize)
.transactionally
).map( doSomething )...
I want to update this plain sql query with skipping the first N-th element.
But that is very DB specific.
Is is possible to get the pagination bit generated by Slick? [like for type-safe queries one just do a drop, filter, take?]
ps: I don't have the Schema, so I cannot go the type-safe way, just want all tables as Map, filter, drop etc on them.
ps2: at akka level, the flow.drop works, but it's not optimal/slow, coz it still consumes the rows.
Cheers
Since you are using the plain SQL, you have to provide a workable SQL in code snippet. Plain SQL may not type-safe, but agile.
BTW, the most optimal way is to skip N-th element by Database, such as limit in mysql.
depending on your database engine, you could use something like
val page = 1
val pageSize = 10
val query = sql"""
select #${onlyTheseColumns.mkString(",")}
from #${dbSource.table}
limit #${pageSize + 1}
offset #${pageSize * (page - 1)}
"""
the pageSize+1 part tells you whether the next page exists
I want to update this plain sql query with skipping the first N-th element. But that is very DB specific.
As you're concerned about changing the SQL for different databases, I suggest you abstract away that part of the SQL and decide what to do based on the Slick profile being used.
If you are working with multiple database product, you've probably already abstracted away from any specific profile, perhaps using JdbcProfile. In that case you could place your "skip N elements" helper in a class and use the active slickProfile to decide on the SQL to use. (As an alternative you could of course check via some other means, such as an environment value you set).
In practice that could be something like this:
case class Paginate(profile: slick.jdbc.JdbcProfile) {
// Return the correct LIMIT/OFFSET SQL for the current Slick profile
def page(size: Int, firstRow: Int): String =
if (profile.isInstanceOf[slick.jdbc.H2Profile]) {
s"LIMIT $size OFFSET $firstRow"
} else if (profile.isInstanceOf[slick.jdbc.MySQLProfile]) {
s"LIMIT $firstRow, $size"
} else {
// And so on... or a default
// Danger: I've no idea if the above SQL is correct - it's just placeholder
???
}
}
Which you could use as:
// Import your profile
import slick.jdbc.H2Profile.api._
val paginate = Paginate(slickProfile)
val action: DBIO[Seq[Int]] =
sql""" SELECT cols FROM table #${paginate.page(100, 10)}""".as[Int]
In this way, you get to isolate (and control) RDBMS-specific SQL in one place.
To make the helper more usable, and as slickProfile is implicit, you could instead write:
def page(size: Int, firstRow: Int)(implicit profile: slick.jdbc.JdbcProfile) =
// Logic for deciding on SQL goes here
I feel obliged to comment that using a splice (#$) in plain SQL opens you to SQL injection attacks if any of the values are provided by a user.
I am pretty new to R and spark. I want to read a parquet file with the following code. Anyone knows how to specify schema there?
library(sparklyr)
sc <- spark_connect(master = "yarn",
appname = "test")
df <- spark_read_parquet(sc,
"name",
"path/to/the/file",
repartition = 0,
schema = "?")
I looked at the link https://spark.rstudio.com/reference/spark_read_parquet/, there isn't any detail or example regarding how to set schema in the function to optimize it.
If you are only trying to read a parquet file, a schema does not need to be used, it is just an available option. The following code should work.
df <- spark_read_parquet(sc,
"name",
"path/to/the/file",
repartition = 0,
schema = Null)
But if you want to use a schema, there are many options and choosing the right one depends on your data and what you are using it for. But try running your code without a schema option to see if that works for your data.
try
tbl_change_db(sc, "dbName")
and if u are using RStudio then click the refresh button on the upper right part of snippet
I am new to spark and spark sql and i was trying to query some data using spark SQL.
I need to fetch the month from a date which is given as a string.
I think it is not possible to query month directly from sparkqsl so i was thinking of writing a user defined function in scala.
Is it possible to write udf in sparkSQL and if possible can anybody suggest the best method of writing an udf.
You can do this, at least for filtering, if you're willing to use a language-integrated query.
For a data file dates.txt containing:
one,2014-06-01
two,2014-07-01
three,2014-08-01
four,2014-08-15
five,2014-09-15
You can pack as much Scala date magic in your UDF as you want but I'll keep it simple:
def myDateFilter(date: String) = date contains "-08-"
Set it all up as follows -- a lot of this is from the Programming guide.
val sqlContext = new org.apache.spark.sql.SQLContext(sc)
import sqlContext._
// case class for your records
case class Entry(name: String, when: String)
// read and parse the data
val entries = sc.textFile("dates.txt").map(_.split(",")).map(e => Entry(e(0),e(1)))
You can use the UDF as part of your WHERE clause:
val augustEntries = entries.where('when)(myDateFilter).select('name, 'when)
and see the results:
augustEntries.map(r => r(0)).collect().foreach(println)
Notice the version of the where method I've used, declared as follows in the doc:
def where[T1](arg1: Symbol)(udf: (T1) ⇒ Boolean): SchemaRDD
So, the UDF can only take one argument, but you can compose several .where() calls to filter on multiple columns.
Edit for Spark 1.2.0 (and really 1.1.0 too)
While it's not really documented, Spark now supports registering a UDF so it can be queried from SQL.
The above UDF could be registered using:
sqlContext.registerFunction("myDateFilter", myDateFilter)
and if the table was registered
sqlContext.registerRDDAsTable(entries, "entries")
it could be queried using
sqlContext.sql("SELECT * FROM entries WHERE myDateFilter(when)")
For more details see this example.
In Spark 2.0, you can do this:
// define the UDF
def convert2Years(date: String) = date.substring(7, 11)
// register to session
sparkSession.udf.register("convert2Years", convert2Years(_: String))
val moviesDf = getMoviesDf // create dataframe usual way
moviesDf.createOrReplaceTempView("movies") // 'movies' is used in sql below
val years = sparkSession.sql("select convert2Years(releaseDate) from movies")
In PySpark 1.5 and above, we can easily achieve this with builtin function.
Following is an example:
raw_data =
[
("2016-02-27 23:59:59", "Gold", 97450.56),
("2016-02-28 23:00:00", "Silver", 7894.23),
("2016-02-29 22:59:58", "Titanium", 234589.66)]
Time_Material_revenue_df =
sqlContext.createDataFrame(raw_data, ["Sold_time", "Material", "Revenue"])
from pyspark.sql.functions import *
Day_Material_reveneu_df = Time_Material_revenue_df.select(to_date("Sold_time").alias("Sold_day"), "Material", "Revenue")
I am trying to avoid a sql injection. This topic has been dealt with in Java (How to prevent query injection on Google Big Query) and Php.
How is this accomplished in App Scripts? I did not find how to add a parameter to a SQL statement. Here is what I had hoped to do:
var sql = 'SELECT [row],etext,ftext FROM [hcd.hdctext] WHERE (REGEXP_MATCH(etext, esearch = ?) AND REGEXP_MATCH(ftext, fsearch = ?));';
var queryResults;
var resource = {
query: sql,
timeoutMs: 1000,
esearch='r"[^a-zA-z]comfortable"',
fsearch='r"[a-z,A-z]confortable"'
};
queryResults = BigQuery.Jobs.query(resource,projectNumber);
And then have esearch and fsearch filled in with the values (which could be set elsewhere).
That does not work, according to the doc.
Any suggestions on how to get a parameter in an SQL query? (I could not find a setString function...)
Thanks!
Unfortunately, BigQuery doesn't support this type of parameter substitution. It is on our list of features to consider, and I'll bump the priority since it seems like this is a common request.
The only suggestion that I can make in the mean time is that if you are building query strings by hand, you will need to make sure you escape them carefully (which is a non-trivial operation).