I am assuming that more Spark support along with semantic changes below will be incorporated into Kudu 0.9.1.
Anyone know of a better way to make unique primary keys other than using UUID to make every row unique if there is no unique column (or combination thereof) to use.
This is what I am using. I know auto incrementing is coming down the line (don’t know when), but is there a way to simulate this in Kudu using Spark out of curiosity?
Thanks,BenOn Jun 14, 2016, at 6:08 PM, Dan Burkert <firstname.lastname@example.org> wrote:I'm not sure exactly what the semantics will be, but at least one of them will be upsert. These modes come from spark, and they were really designed for file-backed storage and not table storage. We may want to do append = upsert, and overwrite = truncate + insert. I think that may match the normal spark semantics more closely.- DanOn Tue, Jun 14, 2016 at 6:00 PM, Benjamin Kim <email@example.com> wrote:Dan,Thanks for the information. That would mean both “append” and “overwrite” modes would be combined or not needed in the future.Cheers,BenOn Jun 14, 2016, at 5:57 PM, Dan Burkert <firstname.lastname@example.org> wrote:Right now append uses an update Kudu operation, which requires the row already be present in the table. Overwrite maps to insert. Kudu very recently got upsert support baked in, but it hasn't yet been integrated into the Spark connector. So pretty soon these sharp edges will get a lot better, since upsert is the way to go for most spark workloads.- DanOn Tue, Jun 14, 2016 at 5:41 PM, Benjamin Kim <email@example.com> wrote:I tried to use the “append” mode, and it worked. Over 3.8 million rows in 64s. I would assume that now I can use the “overwrite” mode on existing data. Now, I have to find answers to these questions. What would happen if I “append” to the data in the Kudu table if the data already exists? What would happen if I “overwrite” existing data when the DataFrame has data in it that does not exist in the Kudu table? I need to evaluate the best way to simulate the UPSERT behavior in HBase because this is what our use case is.Thanks,BenOn Jun 14, 2016, at 5:05 PM, Benjamin Kim <firstname.lastname@example.org> wrote:Hi,Now, I’m getting this error when trying to write to the table.import scala.collection.JavaConverters._val key_seq = Seq(“my_id")val key_list = List(“my_id”).asJavakuduContext.createTable(tableName, df.schema, key_seq, new CreateTableOptions().setNumReplicas(1).addHashPartitions(key_list, 100))df.write.options(Map("kudu.master" -> kuduMaster,"kudu.table" -> tableName)).mode("overwrite").kudujava.lang.RuntimeException: failed to write 1000 rows from DataFrame to Kudu; sample errors: Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Not found: key not found (error 0)Does the key field need to be first in the DataFrame?Thanks,Ben
On Jun 14, 2016, at 4:28 PM, Dan Burkert <email@example.com> wrote:On Tue, Jun 14, 2016 at 4:20 PM, Benjamin Kim <firstname.lastname@example.org> wrote:Dan,Thanks! It got further. Now, how do I set the Primary Key to be a column(s) in the DataFrame and set the partitioning? Is it like this?kuduContext.createTable(tableName, df.schema, Seq(“my_id"), new CreateTableOptions().setNumReplicas(1).addHashPartitions(“my_id"))java.lang.IllegalArgumentException: Table partitioning must be specified using setRangePartitionColumns or addHashPartitionsYep. The `Seq("my_id")` part of that call is specifying the set of primary key columns, so in this case you have specified the single PK column "my_id". The `addHashPartitions` call adds hash partitioning to the table, in this case over the column "my_id" (which is good, it must be over one or more PK columns, so in this case "my_id" is the one and only valid combination). However, the call to `addHashPartition` also takes the number of buckets as the second param. You shouldn't get the IllegalArgumentException as long as you are specifying either `addHashPartitions` or `setRangePartitionColumns`.- DanThanks,Ben
On Jun 14, 2016, at 4:07 PM, Dan Burkert <email@example.com> wrote:Looks like we're missing an import statement in that example. Could you try:
and try again?
- DanOn Tue, Jun 14, 2016 at 4:01 PM, Benjamin Kim <firstname.lastname@example.org> wrote:I encountered an error trying to create a table based on the documentation from a DataFrame.<console>:49: error: not found: type CreateTableOptionskuduContext.createTable(tableName, df.schema, Seq("key"), new CreateTableOptions().setNumReplicas(1))Is there something I’m missing?Thanks,BenOn Jun 14, 2016, at 3:00 PM, Jean-Daniel Cryans <email@example.com> wrote:It's only in Cloudera's maven repo: https://repository.cloudera.com/cloudera/cloudera-repos/org/kududb/kudu-spark_2.10/0.9.0/J-DOn Tue, Jun 14, 2016 at 2:59 PM, Benjamin Kim <firstname.lastname@example.org> wrote:Hi J-D,I installed Kudu 0.9.0 using CM, but I can’t find the kudu-spark jar for spark-shell to use. Can you show me where to find it?Thanks,Ben
On Jun 8, 2016, at 1:19 PM, Jean-Daniel Cryans <email@example.com> wrote:What's in this doc is what's gonna get released: https://github.com/cloudera/kudu/blob/master/docs/developing.adoc#kudu-integration-with-sparkJ-DOn Tue, Jun 7, 2016 at 8:52 PM, Benjamin Kim <firstname.lastname@example.org> wrote:Will this be documented with examples once 0.9.0 comes out?Thanks,Ben
On May 28, 2016, at 3:22 PM, Jean-Daniel Cryans <email@example.com> wrote:It will be in 0.9.0.J-DOn Sat, May 28, 2016 at 8:31 AM, Benjamin Kim <firstname.lastname@example.org> wrote:Hi Chris,Will all this effort be rolled into 0.9.0 and be ready for use?Thanks,Ben
On May 18, 2016, at 9:01 AM, Chris George <Christopher.George@rms.com> wrote:There is some code in review that needs some more refinement.It will allow upsert/insert from a dataframe using the datasource api. It will also allow the creation and deletion of tables from a dataframe
Example usages will look something like:
On 5/18/16, 9:45 AM, "Benjamin Kim" <email@example.com> wrote:
Can someone tell me what the state is of this Spark work?
Also, does anyone have any sample code on how to update/insert data in Kudu using DataFrames?
On Apr 13, 2016, at 8:22 AM, Chris George <Christopher.George@rms.com> wrote:
SparkSQL cannot support these type of statements but we may be able to implement similar functionality through the api.-Chris
On 4/12/16, 5:19 PM, "Benjamin Kim" <firstname.lastname@example.org> wrote:
It would be nice to adhere to the SQL:2003 standard for an “upsert” if it were to be implemented.
MERGE INTO table_name USING table_reference ON (condition)WHEN MATCHED THENUPDATE SET column1 = value1 [, column2 = value2 ...]WHEN NOT MATCHED THENINSERT (column1 [, column2 ...]) VALUES (value1 [, value2 …])
On Apr 11, 2016, at 12:21 PM, Chris George <Christopher.George@rms.com> wrote:
I have a wip kuduRDD that I made a few months ago. I pushed it into gerrit if you want to take a look. http://gerrit.cloudera.org:8080/#/c/2754/It does pushdown predicates which the existing input formatter based rdd does not.
Within the next two weeks I’m planning to implement a datasource for spark that will have pushdown predicates and insertion/update functionality (need to look more at cassandra and the hbase datasource for best way to do this) I agree that server side upsert would be helpful.Having a datasource would give us useful data frames and also make spark sql usable for kudu.
My reasoning for having a spark datasource and not using Impala is: 1. We have had trouble getting impala to run fast with high concurrency when compared to spark 2. We interact with datasources which do not integrate with impala. 3. We have custom sql query planners for extended sql functionality.
On 4/11/16, 12:22 PM, "Jean-Daniel Cryans" <email@example.com> wrote:
You guys make a convincing point, although on the upsert side we'll need more support from the servers. Right now all you can do is an INSERT then, if you get a dup key, do an UPDATE. I guess we could at least add an API on the client side that would manage it, but it wouldn't be atomic.
On Mon, Apr 11, 2016 at 9:34 AM, Mark Hamstra <firstname.lastname@example.org> wrote:
It's pretty simple, actually. I need to support versioned datasets in a Spark SQL environment. Instead of a hack on top of a Parquet data store, I'm hoping (among other reasons) to be able to use Kudu's write and timestamp-based read operations to support not only appending data, but also updating existing data, and even some schema migration. The most typical use case is a dataset that is updated periodically (e.g., weekly or monthly) in which the the preliminary data in the previous window (week or month) is updated with values that are expected to remain unchanged from then on, and a new set of preliminary values for the current window need to be added/appended.
Using Kudu's Java API and developing additional functionality on top of what Kudu has to offer isn't too much to ask, but the ease of integration with Spark SQL will gate how quickly we would move to using Kudu and how seriously we'd look at alternatives before making that decision.
On Mon, Apr 11, 2016 at 8:14 AM, Jean-Daniel Cryans <email@example.com> wrote:
Thanks for taking some time to reply in this thread, glad it caught the attention of other folks!
On Sun, Apr 10, 2016 at 12:33 PM, Mark Hamstra <firstname.lastname@example.org> wrote:
Do they care being able to insert into Kudu with SparkSQL
I care about insert into Kudu with Spark SQL. I'm currently delaying a refactoring of some Spark SQL-oriented insert functionality while trying to evaluate what to expect from Kudu. Whether Kudu does a good job supporting inserts with Spark SQL will be a key consideration as to whether we adopt Kudu.
I'd like to know more about why SparkSQL inserts in necessary for you. Is it just that you currently do it that way into some database or parquet so with minimal refactoring you'd be able to use Kudu? Would re-writing those SQL lines into Scala and directly use the Java API's KuduSession be too much work?
Additionally, what do you expect to gain from using Kudu VS your current solution? If it's not completely clear, I'd love to help you think through it.
On Sun, Apr 10, 2016 at 12:23 PM, Jean-Daniel Cryans <email@example.com> wrote:
Yup, starting to get a good idea.
What are your DS folks looking for in terms of functionality related to Spark? A SparkSQL integration that's as fully featured as Impala's? Do they care being able to insert into Kudu with SparkSQL or just being able to query real fast? Anything more specific to Spark that I'm missing?
FWIW the plan is to get to 1.0 in late Summer/early Fall. At Cloudera all our resources are committed to making things happen in time, and a more fully featured Spark integration isn't in our plans during that period. I'm really hoping someone in the community will help with Spark, the same way we got a big contribution for the Flume sink.
On Sun, Apr 10, 2016 at 11:29 AM, Benjamin Kim <firstname.lastname@example.org> wrote:
Yes, we took Kudu for a test run using 0.6 and 0.7 versions. But, since it’s not “production-ready”, upper management doesn’t want to fully deploy it yet. They just want to keep an eye on it though. Kudu was so much simpler and easier to use in every aspect compared to HBase. Impala was great for the report writers and analysts to experiment with for the short time it was up. But, once again, the only blocker was the lack of Spark support for our Data Developers/Scientists. So, production-level data population won’t happen until then.
I hope this helps you get an idea where I am coming from…
On Apr 10, 2016, at 11:08 AM, Jean-Daniel Cryans <email@example.com> wrote:
On Sun, Apr 10, 2016 at 12:30 AM, Benjamin Kim <firstname.lastname@example.org> wrote:
The main thing I hear that Cassandra is being used as an updatable hot data store to ensure that duplicates are taken care of and idempotency is maintained. Whether data was directly retrieved from Cassandra for analytics, reports, or searches, it was not clear as to what was its main use. Some also just used it for a staging area to populate downstream tables in parquet format. The last thing I heard was that CQL was terrible, so that rules out much use of direct queries against it.
I'm no C* expert, but I don't think CQL is meant for real analytics, just ease of use instead of plainly using the APIs. Even then, Kudu should beat it easily on big scans. Same for HBase. We've done benchmarks against the latter, not the former.
As for our company, we have been looking for an updatable data store for a long time that can be quickly queried directly either using Spark SQL or Impala or some other SQL engine and still handle TB or PB of data without performance degradation and many configuration headaches. For now, we are using HBase to take on this role with Phoenix as a fast way to directly query the data. I can see Kudu as the best way to fill this gap easily, especially being the closest thing to other relational databases out there in familiarity for the many SQL analytics people in our company. The other alternative would be to go with AWS Redshift for the same reasons, but it would come at a cost, of course. If we went with either solutions, Kudu or Redshift, it would get rid of the need to extract from HBase to parquet tables or export to PostgreSQL to support more of the SQL language using by analysts or the reporting software we use..
Ok, the usual then *smile*. Looks like we're not too far off with Kudu. Have you folks tried Kudu with Impala yet with those use cases?
I hope this helps.
It does, thanks for nice reply.
On Apr 9, 2016, at 2:00 PM, Jean-Daniel Cryans <email@example.com> wrote:
Ha first time I'm hearing about SMACK. Inside Cloudera we like to refer to "Impala + Kudu" as Kimpala, but yeah it's not as sexy. My colleagues who were also there did say that the hype around Spark isn't dying down.
There's definitely an overlap in the use cases that Cassandra, HBase, and Kudu cater to. I wouldn't go as far as saying that C* is just an interim solution for the use case you describe.
Nothing significant happened in Kudu over the past month, it's a storage engine so things move slowly *smile*. I'd love to see more contributions on the Spark front. I know there's code out there that could be integrated in kudu-spark, it just needs to land in gerrit. I'm sure folks will happily review it.
Do you have relevant experiences you can share? I'd love to learn more about the use cases for which you envision using Kudu as a C* replacement.
On Fri, Apr 8, 2016 at 12:45 PM, Benjamin Kim <firstname.lastname@example.org> wrote:
My colleagues recently came back from Strata in San Jose. They told me that everything was about Spark and there is a big buzz about the SMACK stack (Spark, Mesos, Akka, Cassandra, Kafka). I still think that Cassandra is just an interim solution as a low-latency, easily queried data store. I was wondering if anything significant happened in regards to Kudu, especially on the Spark front. Plus, can you come up with your own proposed stack acronym to promote?
On Mar 1, 2016, at 12:20 PM, Jean-Daniel Cryans <email@example.com> wrote:
AFAIK no one in the dev community committed to any timeline. I know of one person on the Kudu Slack who's working on a better RDD, but that's about it.
On Tue, Mar 1, 2016 at 11:00 AM, Benjamin Kim <firstname.lastname@example.org> wrote:
Quick question… Is there an ETA for KUDU-1214? I want to target a version of Kudu to begin real testing of Spark against it for our devs. At least, I can tell them what timeframe to anticipate.
On Feb 24, 2016, at 3:51 PM, Jean-Daniel Cryans <email@example.com> wrote:
The DStream stuff isn't there at all. I'm not sure if it's needed either.
The kuduRDD is just leveraging the MR input format, ideally we'd use scans directly.
The SparkSQL stuff is there but it doesn't do any sort of pushdown. It's really basic.
The goal was to provide something for others to contribute to. We have some basic unit tests that others can easily extend. None of us on the team are Spark experts, but we'd be really happy to assist one improve the kudu-spark code.
On Wed, Feb 24, 2016 at 3:41 PM, Benjamin Kim <firstname.lastname@example.org> wrote:
It looks like it fulfills most of the basic requirements (kudu RDD, kudu DStream) in KUDU-1214. Am I right? Besides shoring up more Spark SQL functionality (Dataframes) and doing the documentation, what more needs to be done? Optimizations?
I believe that it’s a good place to start using Spark with Kudu and compare it to HBase with Spark (not clean).
On Feb 24, 2016, at 3:10 PM, Jean-Daniel Cryans <email@example.com> wrote:
AFAIK no one is working on it, but we did manage to get this in for 0.7.0: https://issues.cloudera.org/browse/KUDU-1321
It's a really simple wrapper, and yes you can use SparkSQL on Kudu, but it will require a lot more work to make it fast/useful.
Hope this helps,
On Wed, Feb 24, 2016 at 3:08 PM, Benjamin Kim <firstname.lastname@example.org> wrote:
I see this KUDU-1214 targeted for 0.8.0, but I see no progress on it. When this is complete, will this mean that Spark will be able to work with Kudu both programmatically and as a client via Spark SQL? Or is there more work that needs to be done on the Spark side for it to work?