Skip to main content

How Hadooped is HANA? Part 6:



As you will see HANA may well have the best RDBMS-Hadoop integration in the market. I try hard not to blow foam about HANA in this blog… and I hope that the objective criteria I have devised to evaluate all of the products will keep this post credible… but please look at this post harder than most and push back if you think that I overstep.
First… surprisingly, HANA’s first release has only a single pipe to the Hadoop side. This is worrisome but easily fixed. It will negatively impact performance when large tables/files have to be moved up for processing.
But HANA includes Hadoop as a full partner in a federated data architecture using the Smart Data Access (SDA) engine inside the HANA address space. As a result, HANA not only pushes predicates but it uses cost-based optimization to determine what to push down and what to pull up. HANA interrogates the Hadoop system to gather statistics and uses the HANA optimizer to develop smart execution plans with awareness of both the speed of in-memory and the limited memory resources. When data in HANA is joined with data in Hadoop SDA effectively uses semi-joins to minimize the data pulled up.
Finally, HANA can develop execution plans that executes joins in Hadoop. This includes both joins between two Hadoop tables and joins where small in-memory tables are pushed down to execute the joins in Hadoop. The current limitation is that Hadoop files must be defined as Hive tables.
Here is the HANA execution plan for TPC-H query 19. HANA has pushed down all of the steps behind the Remote Row Scan step… so in this case the entire query including a nested loop join was pushed down. In other queries HANA will push only parts of the plan to Hadoop.




So HANA possesses a very sophisticated integration with Hadoop… with capabilities that minimize the amount of data moved based on the cost of the movement. This is where all products need to go. But without parallel pipes this sophisticated capability provides only a moderate advantage.
Note that this is not the ultimate in integration… there is another level… but I’ll leave some ideas for extending integration even further for my final post in the series.

Comments

Post a Comment

Popular posts from this blog

Python and Parquet Performance

In Pandas, PyArrow, fastparquet, AWS Data Wrangler, PySpark and Dask. This post outlines how to use all common Python libraries to read and write Parquet format while taking advantage of  columnar storage ,  columnar compression  and  data partitioning . Used together, these three optimizations can dramatically accelerate I/O for your Python applications compared to CSV, JSON, HDF or other row-based formats. Parquet makes applications possible that are simply impossible using a text format like JSON or CSV. Introduction I have recently gotten more familiar with how to work with  Parquet  datasets across the six major tools used to read and write from Parquet in the Python ecosystem:  Pandas ,  PyArrow ,  fastparquet ,  AWS Data Wrangler ,  PySpark  and  Dask . My work of late in algorithmic trading involves switching between these tools a lot and as I said I often mix up the APIs. I use Pandas and PyArrow for in-RAM comput...
Post a Comment
Read more

How to construct a File System that lives in Shared Memory.

Shared Memory File System Goals 1. MOUNTED IN SHARED MEMORY The result is a very fast, real time file system. We use Shared Memory so that the file system is public and not private. 2. PERSISTS TO DISK When the file system is unmounted, what happens to it? We need to be able to save the file system so that a system reboot does not destroy it. A great way to achieve this is to save the file system to disk. 3. EXTENSIBLE IN PLACE We want to be able to grow the file system in place. 4. SUPPORTS CONCURRENCY We want multiple users to be able to access the file system at the same time. In fact, we want multiple users to be able to access the same file at the same time. With the goals now in mind we can now talk about the major design issues: FAT File System & Design Issues The  FAT File System  has been around for quite some time. Basically it provides a pretty good file structure. But I have two problems with it: 1. FAT IS NOT EXTENSIBLE IN PLAC...
Post a Comment
Read more

Fetching Facebook Friends using Windows Azure Mobile Services

This tutorial shows you how to fetch Facebook Friends if you have Facebook accessToken. Here is the the code for Scheduled task called getFriends function getFriends() { //Name of the table where accounts are stored var accountTable = tables.getTable('FacebookAccounts'); //Name of the table where friends are stored var friendsTable = tables.getTable('Friends'); checkAccounts(); function checkAccounts(){ accountTable .read({success: function readAccounts(accounts){ if (accounts.length){ for (var i = 0; i < accounts.length; i++){ console.log("Creating query"); //Call createQuery function for all of the accounts that are found createQuery(accounts[i], getDataFromFacebook); } } else { console.log("Didn't find any account"); prepareAccountTable(); } }}); } function prepareAccountTable(){ var myAccount = { accessToken: "", //enter here you facebook accessToken. You can retrieve ...
Post a Comment
Read more