NUTCH FIGHT! 1.7 vs 2.2.1

We've had releases in the Nutch 2.x branch for over a year now. As I described in a previous post, the main difference with the 1.x branch is the use of Apache Gora as a storage abstraction layer, which allows to use various flavours of NoSQL databases such as HBase, Cassandra or Accumulo as backends.

There seems to be a growing number of 2.x users, even though 1.x probably still holds the lead, and 2.x (and Gora) is being improved rapidly as a result. The venerable 1.x version has for it its reliability and a few more functionalities currently missing in 2.x, however how do they compare in terms of performance?

Procedure

We have measured the performance of Nutch 1.7 against 2.2.1 (HBase and Cassandra) using 3 million URLs from the CommonCrawl project. These URLs were obtained using the Commoncrawl module in Behemoth.

For this test, we are less interested in fetching the entire contents of the 3M crawl database, but rather how performance varies when using common Nutch commands (inject / generate / parse / update). The fetch time is less relevant here as it is mainly network-bound and is affected less by the differences in storage between both versions.

Disclaimer : It is important to note that we are not comparing Cassandra and HBase themselves, but via  their respective Gora modules and any conclusions drawn are not necessarily applicable in general.  What we are presenting here is what a user gets when using Nutch 2.x with the default configuration for these backends. As we will see later, a lot depends also on the design of Nutch 2.x itself.

Setup

Nutch 1 version: apache-nutch-1.7

Nutch 2 version: apache-nutch-2.2.1

Cassandra version: cassandra-1.2.9

Hbase version: hbase-90.4

We used a large AWS EC2 instance (available at http://aws.amazon.com/ec2/) with 7.5 GB Memory with Hadoop 1.2.0 installed with Apache Whirr. Mapreduce has been configured to allow a maximum of 2 Mappers and 2 Reducers available.

Nutch  configuration

To make the different crawls comparable in terms of the handled urls, newly-discovered links on the webpages are not added to the crawl database, but we only fetch the ones that belong to the original 3M. Furthermore, we limit the number of urls per host to 100 and the size of the fetchlist to 5K.

These parameters are set in nutch-site.xml with the following properties: 

<property>

 <name>db.update.additions.allowed</name>

<value>false</value>

<description>If true, updatedb will add newly discovered URLs, if false

only already existing URLs in the CrawlDb will be updated and no new

URLs will be added.

</description>

</property>

<property>

<name>generate.max.count</name>

<value>100</value>

<description>The maximum number of urls in a single

fetchlist. -1 if unlimited. The urls are counted according

to the value of the parameter generator.count.mode.

</description>

</property>

Note that the the number of urls per fetchlist can also be set in the crawl script, which we run from runtime/deploy/bin. We also removed the lines in the script which were related to indexing operations as these were less relevant for the comparison.

Results

The results can be found in the table below where the values are the averages for each step over 3 runs. The average time per iteration excludes the fetching as explained above. The steps vary a bit between Nutch 1.x and 2.x (e.g. generation done in a singe step, inlinks computed as part of the update in 2.x) but 

Task on 3M urls/ 5K per iteration as listed in Nutch 1.x/ 100 urls/host	Nutch 1.7 Time (min) per Tasks in Iteration averaged over 3 runs	Nutch 2.2.1 & Cassandra Time (min) per Tasks in Iteration averaged over 3 runs	Nutch 2.2.1 & Hbase Time (min) per Tasks in Iteration averaged over 3 runs
inject	15.17	85.41	27.25
crawldb	2.20	-	-
Iteration:
generate:select	5.11	11.8	18.54
generate:partition	0.33	-	-
fetch	12.9	34.0	38.9
parse	2.0	7.6	13.64
crawldb:update	3.14	26.3	18.27
linkdb	0.41	-	-
linkdb-merge	1.08 (last 2 it.)	-	-
Avg. per Iteration (min.)	12	45	50
Total Time (min.)	29	130	78

As we can see from these figures, Nutch 1 beats Nutch 2 with both Cassandra (N2C below) and HBase (N2H) on all tasks and with a considerable margin. Who is to be on second place is less clear, as we shall see when looking at the different steps involved in more depth. 

Injection is obviously fastest in N1 (17 minutes), while, N2H takes almost double the amount of time for the same task (27 minutes), but this is still exceeded by N2C, where Injection takes a staggeringly 85 minutes. 

However, it makes up for it during the iterations, where on average it is about 10 minutes faster than N2H. So if we were to run the crawl with more iterations, the longer time taken for injection (as this is only done once) would take less weight in the total. Within the iteration and except for the updating step, N2C is usually faster than N2H. 

The distribution of Mappers and Reducers for each task also stays constant over all iterations with N2C, while with N2Hbase data seems to be partitioned differently in each iteration and more Mappers are required  as the crawl goes on. This results in a longer processing time as our Hadoop setup allows only up to 2 mappers to be used at the same time. Curiously, this increase in the number of mappers was for the same number of entries as input. 

The number of mappers used by N2H and N2C is the main explanation for the differences between them. To give an example, the generation step in the first iteration took 11.6 minutes with N2C whereas N2H required 20 minutes. The latter had its input represented by 3 Mappers whereas the former required only 2 mappers. The mapping part would have certainly taken a lot less time if it had been forced into 2 mappers with a larger input (or if our cluster allowed more than 2 mappers / reducers) .

Besides the way data are stored, Nutch 1 and 2 differ by their storage strategies. Nutch 1.x has a concept of segments corresponding to a fetchlist, i.e. one round of crawling, separate from the data structure containing the status of the URLs (crawldb) whereas Nutch 2.x stores everything in a single, table-like structure. 

The implications of this is that the fetching and parsing steps of Nutch 1.x have the segments as input (i.e 5K URLs in our tests), whereas in Nutch 2.x the whole table (3M URLs) is the input. The way GORA currently operates is that all the entries are given by the backends then filtered on the client side before being submitted as input to the MapReduce Job. When the number of URLs in the table is large, a substantial amount of time is spent getting the content from the backends, filtering them as a preamble to the MapReduce job and discarding most of them in the process as they are not in the current fetchlist.

There is a JIRA issue in GORA  about filtering the content on the backend side which would certainly improve things but it does not seem to have been worked on for quite some time. 

Conclusions

Although more flexibility in terms of storage is attractive, at the moment this still seems to come at the price of a much lower performance compared to Nutch 1.x, which is also simpler to setup as it does not required to configure GORA and the backends (and the corresponding knowledge and skills).

This also has an impact on the hardware that can be used, as running HBase or Cassandra has an impact on the RAM required. We initially ran this test on a slightly dated laptop (3GB RAM) and could not get it to work successfully with HBase nor Cassandra. The same crawl with Nutch 1.7 ran fine.

Nutch 1.x also has the advantage of having been around for much longer and as a result is a lot more reliable. It also has some features currently missing from 2.x (e.g. pluggable indexing backends).

We can expect the performance in Nutch 2.x to improve a lot as GORA gets more features such as the one mentioned above.

We ran this test on a single server in pseudo distributed mode, but it would be interesting to see what happens on a properly distributed setup. 

Free your Nutch crawls with pluggable indexers

I have just committed what should be a very important new feature of the next 1.x release of Apache Nutch, namely the possibility to implement indexing backends via plugins. This is currently on the trunk only but should hopefully be ported to 2.x at some point. The Nutch-1047 JIRA issue contains a history of patches and discussions for this feature.

As you'll see by reading the explanations below, this is not the same thing as the indexing filters or the storage backends in Nutch 2.x.

Historically, Nutch used to manage its own Lucene indices itself and provide a web interface for querying them. Support for SOLR was added much later in the 1.0 release (NUTCH-442) and users had two separate commands for indexing directly with Lucene or sending the documents to SOLR, in which case the search could be done outside the Nutch search servers and directly with SOLR. We then decided to drop the Nutch search servers and the Lucene-based indexing altogether in Nutch 1.3 (NUTCH-837) and let the SOLR indexer become the only option. This was an excellent move as it greatly reduced the amount of code we had to look after and meant that we could focus on the crawling while benefiting from the advances in SOLR.

One of the nice things about Nutch is that most of its components are based on plugins. The actual plugin mechanism was borrowed from Eclipse and allows to have endpoints and extensions. Nutch has extension points for URLFilters, URLNormalizers, Parsers, Protocols, etc... The full list of Nutch extensions can be found here. Basically pretty much everything in Nutch is done via plugins and I found that most customisations of Nutch I do for my clients are usually implemented via plugins only.

As you've guessed, NUTCH-1047 is about having generic commands for indexing and handling the backend implementations via plugins. Instead of piggybacking the SOLR indexer code to send the documents to a different backend, one can now use the brand new generic IndexingJob and isolate the logic of how the documents are sent to the backend via an extension of the new IndexWriter endpoint in a custom plugin.

The IndexWriter interface is pretty straightforward :

public String describe();

public void open(JobConf job, String name) throws IOException;

public void write(NutchDocument doc) throws IOException;

public void delete(String key) throws IOException;

public void update(NutchDocument doc) throws IOException;

public void commit() throws IOException;

public void close() throws IOException;

Having this mechanism allows us to move most of the SOLR-specific code to the new indexer-solr plugin (and hopefully all of it as soon as we have a generic de-duplicator which could use the IndexWriter plugins) but more importantly will facilitate the implementation of popular indexing backends such as ElasticSearch or Amazon's CloudSearch service without making the core code of Nutch more complex. We frequently get people on the mailing list asking how to store the Nutch documents on such or such database and being able to do that in a plugin will definitely make it easier. It will also be a good way of storing Nutch documents as files, etc...

This is quite a big change to the architecture of Nutch but we tried to make it as transparent as possible for end users. The only indexer plugin currently available is a port of the existing code for SOLR and is activated by default. We left the old solr* commands and modified them so that they use the generic commands with the indexing plugins in the background so from a user point of view there should be no difference at all. 

There is already a JIRA for a text-based CSV indexing plugin and I expect that the ElasticSearch one will get rapid adoption.

I had been willing to find the time to work on this for quite some time and I'm very pleased it is now committed, thanks to the comments and reviews I got from my fellow Nutch developers. I look forward to getting more feedback and seeing it being used, extended, improved, etc... 

Nutch 2.0 is out (at last!)

Like pretty much any 2.0 release, Nutch 2.0 marks a radical change from the 1.x branch. I've mentioned 2.0 in previous posts but let's do a bit of history first. Nutch was initially started by Doug Cutting (Lucene's creator) and Mike Caffarella around 2002, then came the MapReduce paper from Google and in 2005 MapReduce was implemented as part of Nutch then became a sub project of Lucene at Apache. You know what happened to Hadoop after that : open source super-stardom, millions of dollars in investment, fierce competition between commercial distributions but also a myriad of related projects (HBase, ZooKeeper, Pig, Hive, Mahout etc...) with in the background the emergence of new concepts such as Big Data or NoSQL.

Meanwhile Nutch tagged along following the various releases of Hadoop but was based on the same architecture. It simply started relying on other projects more and more instead of implementing its own stuff, mainly Apache Tika (another offspring of Nutch) for parsing and extracting metadata from various document formats and Apache SOLR for indexing and searching documents. This made the code much lighter, easier to maintain and also up to date with all sorts of functionalities provided by these projects. However the way we stored and access data in Nutch remained the same since the beginning of Hadoop i.e. SequenceFiles and MapFiles.

Nutch 2.0 (a.k.a NutchGora) started in earnest 2 years ago when one of our clients decided to  invest in the development of a NoSQL-based version of Nutch. There had been a preliminary version called NutchBase developed by Dogacan Guney which was used as a basis except that instead of relying exclusively on HBase, we decided to implement our own Backend-Neutral-MapReduce-friendly-ORM which is now an Apache Top Level Project known as Apache GORA and serializes data with Apache AVRO. GORA provides us with a unified  access to various backends, NoSQL or not, an object-to-datastore mapping mechanism and utilities for MapReduce. This means that Nutch 2.0 can run on HBase, Cassandra, Accumulo or MySQL with just a few configuration files to modify.

One major change in 2.0 is that, instead of having a separation between the status of the URLs (crawlDB) separated from the data for these URLs (content and text in segments) and the webgraph (linkDB), we have a single table-like representation of the data where each entry contains everything we know about a URL, even the links that point to it or the various versions of its content (depending on the backend used). Not having separate segments is definitely good news. One of the side effects is that a fetch or parse step can be resumed. 

From a technical point of view this means that Nutch is not limited to the sequential processing of Hadoop data structures  but can operate at a more atomic level (GET, PUT). Most Nutch tasks are still MapReduce operations though but at least we can get the backends to filter the data and provide only what is needed for a specific task to the MapReduce operations.

The best example of this that I can think of is the update step in a Nutch crawl. Basically what this step does is to merge the information from a round of fetching with the rest of the CrawlDB, typically to change the status of the URLs we have fetched and add the new URLs we have discovered when parsing. With the 1.x branch this is done with a MapReduce operation which takes both the CrawlDB  and the segment as input, reduces on the URLs and updates the status of the CrawlDatum objects in the reduce step. All good. Except that as the crawlDB gets larger and larger, the time taken by the update step gets longer and longer up to a point where it ends up being the slowest part of the crawl. Think about a billion entries in the crawlDB and a single URL to update and you'll get the picture.

There are ways of alleviating this for 1.x (i.e. generate multiple segments in one go and update them with the crawldb at the same time) but the point is that with Nutch 2.0 the equivalent operation would be linear with the number of URLs modified, not the whole crawl dataset.

The change of paradigm between sequential datastructures to a table-like representation is a major change for Nutch which will certainly have many positive side-effects. Being the first release of 2.0, we can expect quite a few fixes to be needed and a massive overhaul of the documentation in the next months but the move seems to be positively welcomed by the Nutch community. Of course 1.x will continue to be the trunk for as long as necessary, i.e. until 2.0 is stable and has all the functionalities that  1.x has.

BTW my slides about 2.0 from last year's Berlin Buzzword are now here.

It is also a symbolic move, with Nutch being at the origin of many successful projects, it was about time it caught up with its famous offspring and the concepts which arose from it.

  

What's new in Nutch 1.5

Apache Nutch 1.5 has been released last week. As with each release, this one contains a lot of changes and I will just comment on a few of them.

The main change is actually not in the list above and has not been documented in the Wiki yet. The binary version of Nutch (apache-nutch-1.5.bin.*) now contains the local runtime only, i.e. what you get in runtime/local when compiling the sources. This should make things a bit more straightforward for beginners as we've seen quite a bit of confusion on the mailing lists about which configuration files should be modified (root/conf vs runtime/local/conf). The src version of Nutch is unchanged and is what you'll need if you want to run Nutch on an existing Hadoop cluster. Of course, the runtime/local directory will be generated too from the source and you'll be able to run Nutch in local mode as well. In a nutshell, if you are not sure about what you're doing, want to use Nutch in local mode without a Hadoop cluster and/or do not need any custom plugins then the binary version is what you're after. I usually recommend to use the distributed version on a pseudo-distributed Hadoop cluster for production as the Hadoop web interfaces provide a wealth of useful information, not mentioning of course that you can have more than one mapper or reducer and harness the full potential of your server.

Apart from the usual dependency updates  (Hadoop 1.0.0, Tika 1.1), this release contains many improvements to the webgraph API, which is a better alternative than the default OPIC scoring in Nutch. In the future, it would be interesting to rely on a library such as Apache Giraph to compute the page ranks as it would simplify the code and also make it more efficient.

As mentioned in a previous post, the Nutch user and dev lists seem to indicate an increasing number of users, which is great. This also mean that we tend to see the same questions and issues coming over and over. One such question was about how to parse and index html metatags (see NUTCH-809) which I had contributed 2 years ago. The parse-metatags plugin is now available in the distribution and the steps are documented in the Wiki. Note that the parsing of the html metatags is not activated by default, this is something for the next release maybe.

An important and related change in Nutch 1.5 is NUTCH-1264 which provides a generic plugin for indexing metadata which is typically used alongside parsing plugins such as parse-metatags above and is based on configuration only. The metadata converted into fields for indexing can come from the crawldb, the parse metadata or the content metadata. More work is needed to delegate the indexing parts of existing plugins to it and this is likely to happen in the next release.

Again, Nutch 1.5 contains loads of improvements and you should definitely consider using it if you are on an older version. The next Nutch release will probably be 2.0 for which a RC is already available. Nutch 2.0, a.k.a NutchGora, is a complete redesign of Nutch based on Apache Gora and uses NoSQL datastores as backends instead of relying on the Hadoop data structures. We will have more releases from the 1.x branch as well as 2.x ones, until the latter gets stable and widely used by the community.

As usual, have a look, give it a try and contribute to Nutch if you can.