Moving product recommendations from Hadoop to Redshift saves us time and money

http://engineering.monetate.com/2014/06/18/moving-product-recommendations-from-hadoop-to-redshift-saves-us-time-and-money/

by jpat • 12 years ago

84 62 12 years ago

engineering.monetate.com

rpedela• 12 years ago

I chuckled when I read "We have a legacy data warehouse based in Hive and Elastic MapReduce, with backing data stores in S3.". I guess things have come full circle. It wasn't long ago that a relational database solution would have been "legacy".

hyperliner• 12 years ago

But "legacy" is not only when technology gets old, but also when solutions get old. Maybe it was just a bad solution and they are moving it to a new solution, not necessarily a new platform.

zenjzen5• 12 years ago

I did too, mainly because S3 + Hadoop isn't going to provide the locality and speed that HDFS would. I don't think it's a fair comparison.

ergest• 12 years ago

Is it me or are people switching to non-relational data warehouse architectures simply because it's en vogue? How many companies do you know that have enough data where a non-relational DW would actually make sense? I wonder, have we really pushed relational databases to their breaking point?

bitL3• 12 years ago

If you optimize for latency relational databases won't cut it.

clubhi• 12 years ago

This might be the most inaccurate statement on the internet.

jpat3• 12 years ago

I'm the author of the article. At Monetate, we've chosen our data warehouses to maximize throughput, rather than minimize latency. That's where something like Redshift really shines, it's great a large bulk ingests and running large queries relatively quickly, but awful at running lots of small queries quickly.

On our busiest day last year, we ingested over a quarter billion page views across all of our clients' websites. I'm sure someone has made MySQL scale to that volume, but for us Redshift has been working great for a relatively low price point.

meritt2• 12 years ago

I've looked at and avoided doing anything serious with hdfs/mr for 6 years now. I'm glad some people are starting to realize that re-processing your entire dataset every single time you want to do something isn't very efficient. I'm still waiting for lightbulb moment where the usefulness of it really makes sense to me.

Can anyone point me to a book or blog that discusses good uses of hadoop/map-reduce?

jgrahamc• 12 years ago

I'm waiting for the day people realize that materialized views in databases are awesome and decide to incorporate them into a framework.

zenjzen5• 12 years ago

Simplistically speaking, you don't always have to do table scans. I run into this every day: "Let's use Hadoop and keep doing full table scans! It's scalable! We just add more machines!" Yeah, except continuing to scan all of your growing data each time you need it is inherently unscalable. :(

personZ3• 12 years ago

I wonder, have we really pushed relational databases to their breaking point?

The primary limitation of relational databases were traditionally that without expert level optimizations (which, realistically, data-focused organizations should have. But they very seldom do, especially in the start-up space), many queries would generate large numbers of effectively random IO. When you're rolling with magnetic drives, each drive offers maybe 60-150 IOPS, so this quickly becomes an enormous scaling problem. A large storage array offered maybe 2000 IOPS. Scaling becomes entirely about scaling IOPS, as CPU is seldom a limitation in databases.

Add that many firms were starting on EC2 which not only gave you minimal memory, it offered absolutely miserable IOPS performance.

Digg famously, and disastrously, solved this problem by essentially "denormalizing" every bit of data, enormously exploding the raw data they stored, but allowing for individual queries to be entirely localized, often served in a single, large IO: Instead of looking up all of your friends and finding the things they dug, the system would push every bit of data proactively to containers for every possible user. This is the model promoted by many advocates of alternative storage (e.g the advantage of MongoDb is always the "pull a single giant data bag versus pulling it together from various places").

If Kevin Rose dug something, it would update the "things my friends liked" containers for 40,000 or so of his friends, rather than having those 40,000 users check on-demand to see what each of their friends liked.

But they did that right when flash storage was coming into the mainstream. A technology that offers, on simple, inexpensive cards, 100s of thousands to millions of IOPS. Add that RAM has exploded, such that servers with 256GB of memory are very affordable (that was enough to put the entire universe of Digg's data in memory, where of course random IO is in the tens to hundreds of millions).

So now we're at a situation where having non-duplicated, highly relational database is often the highest performance, outside of all of its other advantages, because it fits in memory, and fits on economical flash storage. It has completely flipped the equation.

http://www.commitstrip.com/en/2014/06/03/the-problem-is-not-...

tomphoolery• 12 years ago

The Digg thing is interesting...they pretty much took the complete opposite approach of Reddit, who basically store everything in two big SQL tables.

onion2k• 12 years ago

It's often cheaper to use the "wrong" architecture than optimising the right architecture. I know I could have a CouchDB datastore searching a few GB with an afternoon of work. I imagine I could get MySQL fast enough with a few days of optimisation. In terms of time, which is by far the biggest cost in most development, CouchDB is the better option.

personZ3• 12 years ago

In terms of time, which is by far the biggest cost in most development, CouchDB is the better option.

For a single, one-off utility, sure. For anything that you ever planned for production, that would be crazy.

Just to be clear, the mentality that onion proposes (at least from my interpretation, though I apologize if I'm misunderstanding), usually justified under a gross misinterpretation of the "premature optimization" warning, is exactly how disaster implementations that end up failing or requiring enormous amounts of engineering time to try to triage and bandage into something usable.

darkxanthos• 12 years ago

A lot are. I used to want to just because it was cool. Now that I actually do though my main use case is sifting through hundreds of GB of unstructured data. I use Hadoop to get the data into a structured form that I can then load into Redshift. It's awesome knowing that just about anything I throw at Redshift, it can handle.

crdb• 12 years ago

We switched to Redshift for our data warehouse because it was MUCH cheaper, declarative, allowed us to retain the relational model, and abstracted away most of the admin. Very happy so far. ~5,000 tables, 7-10TB all maintained by one guy in his spare time.

The workbench options weren't amazing so we made this for our BI team: https://github.com/zalora/redsift/

zenjzen5• 12 years ago

I have another question: Why are people still doing joins in this day and age? Big data + joins = teh suck.

I'm a big fan of compressed denormalized data.

lsb• 12 years ago

It's also unclear how many rows they're trying to do this on, and at what frequencies; that's the crux of what turns this from a small-to-medium-data problem, which you can easily solve on a large box with 10 lines of code, to a big data problem, which requires completely different tooling

jpat3• 12 years ago

In my testing of this query, I ran it against a time range that included over 40 million purchase lines, and our configuration of Redshift returned the result in ~6 minutes. That was much quicker than our legacy EMR implementation.

Currently, we update our product recommendations nightly. However, the speed up we see here from this reimplementation may allow us to update product recommendations more frequently.

zatkin• 12 years ago

Wasn't Hadoop the first of it's kind in Big Data?

crb• 12 years ago

Hadoop was the first (major) open-source implementation of Google's MapReduce framework. http://research.google.com/archive/mapreduce.html

In terms of data warehousing and near-real-time query over Big Data, Google's framework for that is called "Dremel", http://research.google.com/pubs/pub36632.html

Google offer Dremel as a service known as BigQuery.

mattj3• 12 years ago

I've gone through a similar transition (hive to redshift) in a very large scale data environment. Raw Hadoop / cascading is still very useful for more complicated workflows, but redshift is so vastly superior to hive it's not even funny. I thought I would miss adding my own UDFs, but this hasn't been an issue at all. I'm under the impression presto is a similar improvement, but I haven't spent any time with it.

One huge advantage of redshift over hive: you can connect with plain old Postgres libraries, so you can build redshift results into your admin interfaces, one off scripts, and anywhere else you're fine trading a few seconds of latency for extra data.

growse• 12 years ago

I'm not surprised, given that my experiences with Hive are that it's extremely quirky and hardly ever the fastest way to do anything. Given the fact that people seem to be falling over themselves to reinvent better solutions to the same sorts of problems in the Hadoop space (see: Impala, Shark), I don't think I'm alone on that.

endersshadow2• 12 years ago

Just as a quick note: You can use Postgres libraries because Redshift is a slightly modified version Postgres 8.1 under the covers. In fact, almost all massively-parallel-processing (MPP) databases are Postgres under the covers (including Microsoft's PDW). It really speaks to how impressive Postgres is at scaling. Even old releases, like 8.1!

mattj3• 12 years ago

Yup! My experience with redshift has actually made me curious to try out Postgres (I've always used MySQL before this). The stricter SQL dialect was a little odd at first, but I think I've become more comfortable with it over a few months.

CurtHagenlocher• 12 years ago

There may have been Postgres inside when Microsoft purchased the company whose technology ended up in PDW, but the last two versions shipped by Microsoft have definitely been based on SQL Server.

sixdimensional• 12 years ago

I work with the folks who built PDW. It's all SQL Server now. That said, I'm often amazed at how many commercial db products are based on Postgres and other open source dbs. Postgres has a nice page on their site showing all the products derived from it - for example, Netezza or Pivotal's Greenplum. Relational dbs and SQL (especially SQL) are far from dead.

Link: https://wiki.postgresql.org/wiki/PostgreSQL_derived_database...

bjt• 12 years ago

Anyone who likes Postgres and is looking for a good analytics DB should check out the cstore_fdw Postgres extension. [1] It allows Postgres to create and query files in the Optimized Row Columnar (ORC) format [2] from Hive.

It was created and recently open-sourced by Citus Data (YC S11), who've made it a key component of their MPP Postgres offering.

I don't work there. I'm just a fan.

[1] https://github.com/citusdata/cstore_fdw [2] http://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.0.0.2/ds...

mattj3• 12 years ago

A teammate of mine wrote a post about our redshift setup a few months ago with some more details: http://engineering.pinterest.com/post/75186894499/powering-i...

alaiacano• 12 years ago

You should use something like tf-idf to normalize your cooccurance counts for your recommender, otherwise you'll just end up recommending the most globally popular products.

monstrado6• 12 years ago

These type of articles baffle me, you're comparing a high-performance analytical database to a batch-orientated SQL engine.

The whole point behind these query engines on Hadoop (Hive, Presto, Impala, etc) is to separate the database from the query engine. With these engines you can project schemas over raw data in its original form, without having to load it into a table. With Redshift, or other similar analytical databases, you're forced to define a schema, and then load the data in row by row...bulk inserts are very slow in comparison to Hadoop technologies.

Regardless, Hive in general should nver be used for interactive analytics, that's not what it's intended for. Where Hive shines is when you can dump 250TB of raw text data into a folder and then run a SQL query to extract useful information out of it. The extracted data could then be loaded into a RDBMS like RedShift for real-time reporting.

With all that being said, if you want to run SQL queries on data in Hadoop at the speeds of Redshift, you should have used Impala with Parquet, which is known to be even faster than Redshift in many cases, and is based on the same technology Google uses (Dremel and F1). The benefits of keeping your data in Hadoop are enormous, not every problem can be solved using SQL. The same data you're querying with Impala could actually be used to do machine learning using Spark or Mahout. Maybe you want to start indexing one of your tables into Solr to provide search capabilities on a subset of your columns to your users...or maybe you want to use Giraph or Sparks' GraphX to do parallel graph computation. The data never moves, there's still only ONE copy of that data in Hadoop, and you can bring any kind of workload to it.

bbrunner• 12 years ago

Redshift is an especially limited SQL engine considering it doesn't support UDFs. It is wicked fast, but what you get in speed you lose in flexibility. Current (well, February, but fairly current) benchmarks[0] place Impala and Shark (SQL on top of Spark) within grasp of Redshift while pulling data from disk and, for certain workloads, on par or faster than Redshift. This is without using a columnar file format.

Impala is impressive technology, but it does require you to run dedicated Impala daemons as it doesn't use map reduce under the hood. Shark is especially interesting, however, because it is fast AND build on top of spark, so you can run raw Spark jobs, SQL queries, graph processing and ML all on the same cluster. Shark currently uses Hive to generate it's query plans, but the Spark project is working on implementing it's own SQL engine called Catalyst[1] that promises to be a significant improvement.

[0] https://amplab.cs.berkeley.edu/benchmark/

[1] https://spark-summit.org/talk/armbrust-catalyst-a-query-opti...

monstrado6• 12 years ago

Although I have a lot of respect for the amplab, they did not do their due diligence with that benchmark. Mainly for a few reasons, they didn't test using columnar storage in Hadoop (ORC / Parquet), which is what Redshift is using underneath (a proprietary columnar store). Also, the most complicated query they ran was a two table join, and from what I can tell, there wasn't any concurrent workload testing.

(disclaimer: I'm a Cloudera employee):

I recommend checking out the following blog, not because my employer wrote it, but because the guys behind the benchmark did an incredible job making the benchmark competitive. They also show metrics that a lot of the other people are not showing, for example concurrent workload capabilities, CPU efficiency, etc.

Impala, Hive (on Tez), Shark, Presto

http://blog.cloudera.com/blog/2014/05/new-sql-choices-in-the...

NorthernDaemon2• 12 years ago

Actian Matrix (Paraccel) does support UDFs, but you'd have to run your own cluster on premise.

alanctgardner32• 12 years ago

Much like the Clouderan commenter, I wouldn't put a lot of stock in Berkeley's Big Data Benchmark. I reran a similar test with columnar storage and found Impala handily beats Shark. Operationally it's also much easier to deploy (provided you're on EMR or CDH). The "dedicated nodes" argument is kind of FUD, you can use LLAMA for resource sharing, and you need to colocate imapalad with DataNodes to achieve decent performance anyways. So YARN, Spark and Impala can all play nice together on the same cluster.

The queries and data set Berkeley chooses are bizarre. TPC-DS or TPC-H are much more representative of real-world performance, and the differences are more pronounced as the queries get more complex.

edit: I also don't understand why the Spark team is reinventing the wheel for Spark SQL when Hive running on Tez will produce very similar query plans. The two projects are converging to the same place, but they insist on having divergent code bases ;)

verelo• 12 years ago

So we recently did this same switch. I can say that in our experience data pulls that were taking (on a good day) 6 hours now take under 10 seconds. There are some jobs we had that literally took days to run that now complete in minutes. Redshift is amazingly simple to use, crazy fast and so very cost effective.

Generally we're dealing with datasets that are around 1-3TB, and pretty well organized. Its just amazing how forgiving Redshift is when it comes to unusually written SQL and how useful it is to us as a business. Extracting serious insights was once a job that only a few people could do, now its something that anyone with a SQL background can manage.

zenjzen5• 12 years ago

Completely agree with everything you said. I disagree about Impala, right now.

It has great potential, but I don't think it's prod-ready yet.

Also, why no mention of HBase?

monstrado6• 12 years ago

Although Impala is still a fairly new product, my team has been using it internally at Cloudera in production for over a year for real-time log analysis to our support engineers (http://bit.ly/USFQdh), among other ad-hoc BI analytics. We also have a bunch of customers who are using Impala to power very critical interactive workloads. What about Impala makes you feel like it's not production ready?

Good question about HBase, I didn't mention HBase because although it's a super fast NoSQL database, it's a lousy analytical database. Sure it's great at doing really fast scans over small slices of data (and/or updating data), but full table scans are extremely slow when compared to analyzing flat files in HDFS. For doing analytics in Hadoop, the format you almost always want is Parquet. Not only is reading files directly from HDFS faster, but Parquet is a true columnar store, so you pay a minimal IO penalty for queries since you only read necessary data. Also, Parquet uses some really efficient column encoding formats like (dictionary, delta, run length, ..) to reduce both IO and to increase the effectiveness of compression.

callesgg• 12 years ago

I generally love sql and use it in all My stuff more or less.

The one thing I fucking hate is how hard it is to get the database to execute querys in a efficient way on anything that is more than a simple join and select.

NorthernDaemon2• 12 years ago

Those who want on-premise Redshift should try Actian Matrix. Redshift is just Amazon's version of Actian Matrix (formerly Paraccel)

alanctgardner32• 12 years ago

Another commenter pointed this out, but what you're trying to compute is cosine similarity, in which case you're missing the normalizing part in the denominator (the product of the magnitude of both vectors). In other words, two items which both occur frequently will score higher than two items which occur infrequently, but which co-occur higher than usual. This leads to a tendency to over-recommend popular items.

When you were on EMR, you could have used Mahout's distributed collaborative filtering, which has the benefits of being correct, and requiring zero coding.

Wikipedia explains here: http://en.wikipedia.org/wiki/Cosine_similarity

jpat3• 12 years ago

Thanks for the tip, I'll look into this more.

shamney• 12 years ago

aside from the example in the original article, what sort of questions are these tool used to answer?

Moving product recommendations from Hadoop to Redshift saves us time and money

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