For federated queries: “N” requires all data from the source to be copied into SQL Server 2016 and then filtered. For “Y”, the query is pushed down into the data source and only the results are returned back, which can be much faster for large amounts of data.
I mention “Maybe” for age out data in SQL DW as you can use PolyBase to access the aged-out data in blob or Azure Data Lake Storage (ADLS), but it will have to import all the data so may have slower performance (which is usually ok for accessing data that is aged-out). For SQL Server 2016, it will have to import the data unless you use HDP/Cloudera, in which case the creation of the MapReduce job will add overhead.
The thing that I like about this chart is that the new Polybase sources (SQL Server, Oracle, Teradata, Mongo, and generic ODBC) do support predicate pushdown. For large data sets, that’s huge: it lets the database engine on the opposite end do as much filtering as possible before sending results back to your SQL Server head node.
Most common patterns using Azure Data Lake Store (ADLS) involve customers ingesting and storing raw data into ADLS. This data is then cooked and prepared by analytic workloads like Azure Data Lake Analytics and HDInsight. Once cooked this data is then explored using engines like Azure SQL Data Warehouse. One key pain point for customers is having to wait for a substantial time after the data was cooked to be able to explore it and gather insights. This was because the data stored in ADLS would have to be loaded into SQL Data Warehouse using tools row-by-row insertion. But now, you don’t have to wait that long anymore. With the new SQL Data Warehouse PolyBase support for ADLS, you will now be able to load and access the cooked data rapidly and lessen your time to start performing interactive analytics. PolyBase support will allow to you access unstructured/semi-structured files in ADLS faster because of a highly scalable loading design. You can load the files stored in ADLS into SQL Data Warehouse to perform analytics with fast response times or you use can the files in ADLS as external tables. So get ready to unlock the value stored in your petabytes of data stored in ADLS.
I’ve been waiting for this support, and I’m happy that they were able to integrate the two products.
That’s a header row, and I’m okay with it not making its way in. As a quick aside, I should note that I picked tailnum as my distribution key. The airplane’s tail number is unique to that craft, so there absolutely will be more than 60 distinct values, and as I recall, this data set didn’t have too many NULL values. After loading the 2008 data, I loaded all years’ data the same way, except selecting from dbo.Flights instead of Flights2008.
Click through for more details, including the CETAS statement, which I’d love to see in on-prem SQL Server.
Using a view, we were able to create a “partitioned” Polybase experience, similar to what we had in SQL Server 2000. This form of poor man’s partitioning allows us to segment out data sets and query them independently, something which can be helpful when storing very large amounts of data off-site and only occasionally needing to query it. The thing to remember, though, is that if you store this in Azure Blob Storage, you will need to pull down the entire table’s worth of data to do any processing.
This leads to a concept I first heard from Ginger Grant: pseudo-StretchDB. Instead of paying for what Stretch offers, you get an important subset of the functionality at a much, much lower price. If you do store the data in Azure Blob Storage, you’re paying pennies per gigabyte per month. For cold storage, like a scenario in which you need to keep data around to keep the auditors happy but your main application doesn’t use that information, it can work fine. But if you need to query this data frequently, performance might be a killer.
For Polybase tables without the ability to perform external pushdown, coming up with a good partitioning strategy is probably one of the two best ways to improve performance, with creating a Polybase scale-out cluster the other method.
When compiling a SQL query that references an external table stored in an HDFS file, the PDW Engine Service contacts the Hadoop Namenode for information about the file. This information, combined with the number of DMS instances in the PDW cluster, is used to calculate the portion (offset and length) of the input file(s) each DMS instance should read from HDFS. This information is passed to DMS in the HDFS Shuffle step of the DSQL (distributed SQL) plan along with other information needed to read the file, including the file’s path, the location of the appropriate Namenode, and the name of the RecordReader that the bridge should use.
The system attempts to evenly balance the number of bytes read by each DMS instance. Once the DMS instances obtain split information from the Namenode, each can independently read the portion of the file it is assigned, directly communicating with the appropriate Datanodes without any centralized control.
This is a very clear paper which helps describe the core constructs of Polybase. Highly recommended.
One additional question I have involves whether the process for loading data is round-robin on a row-by-row basis. My conjecture is that it is not (particularly given that our first example had 4 files with zero records in them!), but I figured I’d create a new table and test. In this case, I’m using three fixed-width data types and loading 10 million identical records. I chose to use identical record values to make sure that the text length of the columns in this line were exactly the same; the reason is that we’re taking data out of SQL Server (where an int is stored in a 4-byte block) and converting that int to a string (where each numeric value in the int is stored as a one-byte character). I chose 10 million because I now that’s well above the cutoff point for data to go into each of the eight files, so if there’s special logic to handle tiny row counts, I’d get past it.
Read on for the exciting(?) conclusion.
In this case, all of those packets were 1514 bytes, so it’s an easy multiplication problem to see that we downloaded approximately 113 MB. The 2008.csv.bz2 file itself is 108 MB, so factoring in TCP packet overhead and that there were additional, smaller packets in the stream, I think that’s enough to show that we did in fact download the entire file. Just like in the Hadoop scenario without MapReduce, the Polybase engine needs to take all of the data and load it into a temp table (or set of temp tables if you’re using a Polybase scale-out cluster) before it can pull out the relevant rows based on our query.
The upshot is that Polybase behaves very similarly on Azure Blob Storage as it does with on-prem Hadoop for non-MapReduce queries.
To this point, I have focused my Polybase series on interactions with on-premises Hadoop, as it’s the use case most apropos to me. I want to start expanding that out to include other interaction mechanisms, and I’m going to start with one of the easiest: Azure Blob Storage.
Ayman El-Ghazali has a great blog post the topic, which he turned into a full-length talk. As such, this post will fill in the gaps rather than start from scratch. In today’s post, my intention is to retrieve data from Azure Blob Storage and get an idea of what’s happening. From there, we’ll spend a couple more posts on Azure Blob Storage, looking a bit deeper into the process. That said, my expectation going into this series is that much of what we do with Azure Blob Storage will mimic what we did with Hadoop, as there are no Polybase core concepts unique to Azure Blob Storage, at least any of which I am aware.
Spoilers: I’m still not aware of any core concepts unique to Azure Blob Storage.
What’s interesting is the error message itself is correct, but could be confusing. Note that it’s looking for a path with this name, but it isn’t seeing a path; it’s seeing a file with that name. Therefore, it throws an error.
This proves that you cannot control insertion into a single file by specifying the file at create time. If you do want to keep the files nicely packed (which is a good thing for Hadoop!), you could run a job on the Hadoop cluster to concatenate all of the results of the various files into one big file and delete the other files. You might do this as part of a staging process, where Polybase inserts into a staging table and then something kicks off an append process to put the data into the real tables.
Sometime in the future, I plan to see how it scales: with multiple files writing to a multi-node Hadoop cluster, do I get better write performance with a Polybase scaleout cluster? And if so, how close to linear scale can I get?
Even for a simple query, I’m not going to expect you to read 174 lines of XML; I’m not a sadist, after all…
What follows is a look at significant lines and my commentary.
Don’t listen to me there; that guy really is a sadist who wants you to read 174 lines of XML.