One thing we are proud of in Spark is creating APIs that are simple, intuitive, and expressive. Spark 2.0 continues this tradition, with focus on two areas: (1) standard SQL support and (2) unifying DataFrame/Dataset API.
On the SQL side, we have significantly expanded the SQL capabilities of Spark, with the introduction of a new ANSI SQL parser and support for subqueries. Spark 2.0 can run all the 99 TPC-DS queries, which require many of the SQL:2003 features. Because SQL has been one of the primary interfaces Spark applications use, this extended SQL capabilities drastically reduce the porting effort of legacy applications over to Spark.
There’s some great stuff coming out of DataBricks. Spark 2.0 looks to be an exciting product.
Grafana is deployed, managed and pre-configured to work with the Ambari Metrics service. We are including a curated set dashboards for core HDP components, giving operators at-a-glance views of the same metrics Hortonworks Support & Engineering review when helping customers troubleshoot complex issues.
Metrics displayed on each dashboard can be filtered by time, component, and contextual information (YARN queues for example) to provide greater flexibility, granularity and context.
Ambari is really shaping up to be a nice framework for managing a Hadoop cluster. I’m excited to see improved monitoring capabilities.
However, the logs can be corrupted. For example, the second line is a blank line, the fourth line reports some network issues and finally the last line shows a sales value of zero (which cannot happen!).
We can use accumulators to analyse the transaction log to find out the number of blank logs (blank lines), number of times the network failed, any product that does not have a category or even number of times zero sales were recorded. The full sample log can be found here.
Accumulators are applicable to any operation which are,
1. Commutative -> f(x, y) = f(y, x), and
2. Associative -> f(f(x, y), z) = f(f(x, z), y) = f(f(y, z), x)
For example, sum and max functions satisfy the above conditions whereas average does not.
Accumulators are an important way of measuring just how messy your semi-structured data is.
Apache Falcon is a framework for managing data life cycle in Hadoop clusters. It establishes relationship between various data and processing elements on a Hadoop environment, and also provides feed management services such as feed retention, replications across clusters, archival etc.
Let us first discuss how to setup Apache Falcon. Run the below given command to download git repository of Falcon:
Command: git clone https://git-wip-us.apache.org/repos/asf/falcon.git falcon
Falcon comes as part of the Hortonworks Data Platform; Cloudera has its own alternative.
This distributed testing infrastructure started out as a Cloudera hackathon project in 2014. Todd Lipcon and I worked on a shared backend for running test tasks on a cluster, with Todd focusing on onboarding the Apache Kudu (incubating) tests, and myself on Apache Hadoop. Our prototype implementation reduced the runtime of the 1,700+ Hadoop unit tests from 8.5 hours to 15 minutes.
Since then, we’ve spent time improving the infrastructure and on-boarding additional projects. Besides Kudu and Hadoop, our distributed testing infrastructure is also being used by our Apache Hive and Apache HBase teams. We can now run all the Hadoop unit tests in less than 10 minutes!
Finally, we’re happy to announce that both our infrastructure and code are public! You can browse the webUI at http://dist-test.cloudera.org and see all the source code (ASLv2 licensed) at the cloudera/dist_test github repository. This infrastructure is already being used at upstream Apache to run the Kudu pre-commit tests.
This is an interesting look at how to scale out unit tests. It’s a bit of a long read (especially with all the videos) but worth your time.
This week, we’re excited that Forrester recognized Microsoft Azure as a leader in their Big Data Hadoop Cloud Solutions. Apache Hadoop as a technology has become popular amongst organizations to unlock insights from data of all size, shape, and speed. Hadoop power solutions to help businesses improve their performance, educators to better connect with the needs of their students, medical professionals to improve the quality of their care, or researchers to accelerate new advancements in science.
As an example, Ultra Tendency uses Hadoop to achieve something not possible before – visualize more than 27 million distinct sensor readings to give Japanese citizens accurate, up-to-date information about the radiation contamination from the Fukushima nuclear plant meltdown. More and more organizations are also deploying Hadoop in the cloud with 47% of Forrester’s respondents to a 2015 survey increasing their cloud deployments either by 5-10% (37%) or more than 10% (10%).1 This makes sense because the cloud allows you to scale elastically on demand to handle the processing of any amount of data.
AWS and IBM also have very good solutions, and Google is trying to get a stronger foothold on the cloud game.
Ex-Googler (and current Amazon Web Services employee) Tim Bray argues “there is a real cost to this continuous widening of the base of knowledge a developer has to have to remain relevant.” RedMonk analyst Stephen O’Grady takes this a step further: “It could be that we’re approaching the too-much-of-a-good-thing stage. In which case, the logical outcome will be a gradual slowing of fragmentation followed by gradual consolidation.”
In other words, niche data stores that do one thing really well are giving way to more generally applicable databases that can serve a broader range of enterprise needs.
The second part of Keep’s sentence above, however, spells out another reason HBase is struggling: It’s really hard to use.
I have a statement which is 90% serious and 10% joke: a database product is truly mature once it supports SQL. So what’s the answer for HBase? The current attempt at an answer is Phoenix, which is…SQL for HBase.
Support subscription revenue during the quarter was up sharply from $13.1 million to $27.6 million, an increase of 110 percent compared to the first quarter of 2015, which was Hortonworks’ first quarter as a public company following an IPO in late 2014. Professional services revenue accounted for $13.7 million in revenue, a 49 percent increase.
Hortonworks holds about 40% of the Hadoop market share, with Cloudera holding another 40%.
That’s the basics. Peeling back the onion more reveals other distinct differences, further making the case more strongly for a Hadoop-RDBMS coexistence strategy. RDBMS has the backing of the biggest names in the software industry, and as such has fostered an install base of IT talent probably second to none. RDBMS integrate very well with other systems, and represent a very mature technology having venerable, 40-year old roots. RDBMS are baked into the very fabric of just about every mid-to large sized IT organization in the world. Believe it – RDBMS aren’t going away any time soon, nor should they.
Relational databases have a strong mathematical footing which provides unparalleled data integrity. Hadoop has a strong mathematical footing which provides near-linear scale out. The key is knowing the problem you need to solve and how to integrate the results.
The question is what is the right time period to use? The answer is it depends on the size of your partitions. Generally, for managed tables in U-SQL, you want to target about 1 GB per partition. So, if you are bringing in say 800 mb per day then daily partitions are about right. If instead you are bringing in 20 GB per day, you should look at hourly partitions of the data.
In this post, I’d like to take a look at two common scenarios that people run into. The first is full re-compute of partitions data and the second is a partial re-compute of a partition. The examples I will be using are based off of the U-SQL Ambulance Demo’s on Github and will be added to the solution for ease of your consumption.
The ability to reprocess data is vital in any ETL or ELT process.