I’m comparing the data ingested in hive table with that of that source and storing the differences in mariadb There are no primary keys for the tables and would like to have a optimise solution and though I’ve used except method to check the difference I’m finding difficult in printing out the difference in the columns for the same row which are different.
As far as I can think it's not possible to solve your problem in the absence of primary key as in that case each row of one DataFrame is potentially different than each row of the other DataFrame and practically you wouldn't want to report difference with each row of the other DataFrame.
Related
I am creating a process in spark scala within an ETL that checks for some events occurred during the ETL process. I start with an empty dataframe and if events occur this dataframe is filled with information ( a dataframe can't be filled it can only be joined with other dataframes with the same structure ). The thing is that at the end of the process, the dataframe that has been generated is loaded into a table but it can happen that the dataframe ends up being empty because no event has occured and I don't want to load a dataframe that is empty because it has no sense. So, I'm wondering if there is an elegant way to load the dataframe into the table only if it is not empty without using the if condition. Thanks!!
I recommend to create the dataframe anyway; If you don't create a table with the same schema, even if it's empty, your operations/transformations on DF could fail as it could refer to columns that may not be present.
To handle this, you should always create a DataFrame with the same schema, which means the same column names and datatypes regardless if the data exists or not. You might want to populate it with data later.
If you still want to do it your way, I can point a few ideas for Spark 2.1.0 and above:
df.head(1).isEmpty
df.take(1).isEmpty
df.limit(1).collect().isEmpty
These are equivalent.
I don't recommend using df.count > 0 because it is linear in time complexity and you would still have to do a check like df != null before.
A much better solution would be:
df.rdd.isEmpty
Or since Spark 2.4.0 there is also Dataset.isEmpty.
As you can see, whatever you decide to do, there is a check somewhere that you need to do, so you can't really get rid of the if condition - as the sentence implies: if you want to avoid creating an empty dataframe.
I have a scala dataframe with two columns:
id: String
updated: Timestamp
From this dataframe I just want to get out the latest date, for which I use the following code at the moment:
df.agg(max("updated")).head()
// returns a row
I've just read about the collect() function, which I'm told to be
safer to use for such a problem - when it runs as a job, it appears it is not aggregating the max on the whole dataset, it looks perfectly fine when it is running in a notebook -, but I don't understand how it should
be used.
I found an implementation like the following, but I could not figure how it should be used...
df1.agg({"x": "max"}).collect()[0]
I tried it like the following:
df.agg(max("updated")).collect()(0)
Without (0) it returns an Array, which actually looks good. So idea is, we should apply the aggregation on the whole dataset loaded in the drive, not just the partitioned version, otherwise it seems to not retrieve all the timestamps. My question now is, how is collect() actually supposed to work in such a situation?
Thanks a lot in advance!
I'm assuming that you are talking about a spark dataframe (not scala).
If you just want the latest date (only that column) you can do:
df.select(max("updated"))
You can see what's inside the dataframe with df.show(). Since df are immutable you need to assign the result of the select to another variable or add the show after the select().
This will return a dataframe with just one row with the max value in "updated" column.
To answer to your question:
So idea is, we should apply the aggregation on the whole dataset loaded in the drive, not just the partitioned version, otherwise it seems to not retrieve all the timestamp
When you select on a dataframe, spark will select data from the whole dataset, there is not a partitioned version and a driver version. Spark will shard your data across your cluster and all the operations that you define will be done on the entire dataset.
My question now is, how is collect() actually supposed to work in such a situation?
The collect operation is converting from a spark dataframe into an array (which is not distributed) and the array will be in the driver node, bear in mind that if your dataframe size exceed the memory available in the driver you will have an outOfMemoryError.
In this case if you do:
df.select(max("Timestamp")).collect().head
You DF (that contains only one row with one column which is your date), will be converted to a scala array. In this case is safe because the select(max()) will return just one row.
Take some time to read more about spark dataframe/rdd and the difference between transformation and action.
It sounds weird. First of all you don´t need to collect the dataframe to get the last element of a sorted dataframe. There are many answers to this topics:
How to get the last row from DataFrame?
To give you a bit of background. I have a process which does this large complex calculation which takes a while to complete. It runs on a timer. After some investigation I realise that what is causing the slowness isn't the actual calculation but the internal q function, union.
I am trying to union two simple tables, table A and table B. A is approximately 5m rows and B is 500. Both tables have only two columns. First column is a symbol. Table A is actually a compound primary key of a table. (Also, how do you copy directly from the console?)
n:5000000
big:([]n?`4;n?100)
small:([]500?`4;500?100)
\ts big union small
I tried keying both columns and upserting, join and then distinct, "big, small where not small in big" but nothing seems to work :(
Any help will be appreciated!
If you want to upsert the big table it has to be keyed and upsert operator should be used. For example
n:5000000
//big ids are unique numbers from 0 to 499999
//table is keyed with 1! operator
big:1!([]id:(neg n)?n;val:n?100)
//big ids are unique numbers. 250 from 0-4999999 and 250 from 500000-1000000 intervals
small:([]id:(-250?n),(n+-250?n);val:500?100)
If big is global variable it is efficient to upsert it as
`big upsert small
if big is local
big: big upsert small
As the result big will have 500250 elements, because there are 250 common keys (id column) in big and small tables
this may not be relevant, but just a quick thought. If your big table has a column which has type `sym and if this column does not really show up that much throughout your program, why not cast it to string or other value? if you are doing this update process every single day then as the data gets packed in your partitioned hdb, whenever the new data is added, kdb+ process has to reassign/rewrite its sym file and i believe this is the part that actually takes a lot of time, not the union calculation itself..
if above is true, i'd suggest either rewriting your schema for the table which minimises # of rehashing(not sure if this is the right term though!) on your symfile. or, as the above person mentioned, try to assign attribute to your table.. this may reduce the time too.
I'm building a simple Scala Play app which stores data in a Cassandra DB using the Phantom DSL driver for Scala. One of the nice features of Cassandra is that you can do partial updates i.e. so long as you provide the key columns, you do not have to provide values for all the other columns in the table. Cassandra will merge the data into your existing record based on the key.
Unfortunately, it seems this doesn't work with Phantom DSL. I have a table with several columns, and I want to be able to do an update, specifying values just for the key and one of the data columns, and let Cassandra merge this into the record as usual, while leaving all the other data columns for that record unchanged.
But Phantom DSL overwrites existing columns with null if you don't specify values in your insert/update statement.
Does anybody know of a work-around for this? I don't want to have to read/write all the data columns every time, as eventually the data columns will be quite large.
FYI I'm using the same approach to my Phantom coding as in these examples:
https://github.com/thiagoandrade6/cassandra-phantom/blob/master/src/main/scala/com/cassandra/phantom/modeling/model/GenericSongsModel.scala
It would be great to see some code, but partial updates are possible with phantom. Phantom is an immutable builder, it will not override anything with null by default. If you don't specify a value it won't do anything about it.
database.table.update.where(_.id eqs id).update(_.bla setTo "newValue")
will produce a query where only the values you've explicitly set to something will be set to null. Please provide some code examples, your problem seems really strange as queries don't keep track of table columns to automatically add in what's missing.
Update
If you would like to delete column values, e.g set them to null inside Cassandra basically, phantom offers a different syntax which does the same thing:
database.table.delete(_.col1, _.col2).where(_.id eqs id)`
Furthermore, you can even delete map entries in the same fashion:
database.table.delete(_.props("test"), _.props("test2").where(_.id eqs id)
This assumes props is a MapColumn[Table, Record, String, _], as the props.apply(key: T) is typesafe, so it will respect the keytype you define for the map column.
I'm working on some POC.
I have the Column Family which stores server event. Avoiding to get row oversize we are splitting each row to N another rows using compositeType in row key:
CREATE COLUMN FAMILY logs with comparator='ReversedType(TimeUUIDType)' and key_validation_class='CompositeType(UTF8Type,IntegerType)' and default_validation_class=UTF8Type;
so for each server name we have N rows and we are writing data to each row using Very Simple Round Robin algorithm.
I have no problem to write data to any row:
Mutator<Composite> mutator = HFactory.createMutator(keySpace, CompositeSerializer.get());
HColumn<UUID,String> col =
HFactory.createColumn( TimeUUIDUtils.getUniqueTimeUUIDinMillis(), log);
Composite rowName = new Composite();
rowName.addComponent(serverName, StringSerializer.get());
rowName.addComponent(this.roundRobinDestributor.getRow(), IntegerSerializer.get());
mutator.insert(rowName, columnFamilyName, col);
}
So far so good, but now I have two quetions:
1) Due to the fact that if I want to get all logs for some serverName I would scan row keys, should I use ByteOrderedPartitioner?
2) Can any body help me, or point me on some help how to create Hector query which will bring all rows for server1 ( {server1:0}, {server1:1} {server1:2), etc...)? I saw a lot of example using CompositeType as comparator, but no example for key validator.
Any help or comment is highly appreciated.
First of all, row oversizing shouldn't be a problem in cassandra. Despite that, it might worth to spilt rows, since data distribution across cluster will be more even in this situation.
ByteOrderedPartitioner doesn't look like a good option here, since it would be hard to achieve uniform distribution of rows across cluster, that will lead to hotspots.
There's no way to query range of keys when using RandomPartitioner. However, if the maximum N value is reasonably small (up to 256) MultigetSliceQuery might be used to query whole set of rows.