Tuesday, October 20, 2009

HBase on Cloudera Training Virtual Machine (0.3.1)

You might want to run HBase on Cloudera's Virtual Machine to get a quick start to a prototyping setup. In theory you download the VM, start it and you are ready to go. There are a few issues though, the worst being that the current Hadoop Training VM does not include HBase at all. Also, Cloudera is using a specific version of Hadoop that it deems stable and maintains it own release cycle. So Cloudera's version of Hadoop is 0.18.3. HBase though needs Hadoop 0.20 - but we are in luck as Andrew Purtell of TrendMicro maintains a special branch of HBase 0.20 that works with Cloudera's release.

Here are the steps to get HBase running on Cloudera's VM:
  1. Download VM

    Get it from Cloudera's website.
  2. Start VM

    As the above page states: "To launch the VMWare image, you will either need VMware Player for windows and linux, or VMware Fusion for Mac."

    Note: I have Parallels for Mac and wanted to use that. I used Parallels Transporter to convert the "cloudera-training-0.3.1.vmx" to a new "cloudera-training-0.2-cl3-000002.hdd", create a new VM in Parallels selecting Ubuntu Linux as the OS and the newly created .hdd as the disk image. Boot up the VM and you are up and running. I gave it a bit more memory for the graphics to be able to switch the VM to 1440x900 which is native to my MacBook Pro I am using.

    Finally follow the steps explained on the page above, i.e. open a Terminal and issue:
    $ cd ~/git
    $ ./update-exercises --workspace
  3. Pull HBase branch

    Open a new Terminal (or issue a $ cd .. in the open one), then:
    $ sudo -u hadoop git clone http://git.apache.org/hbase.git /home/hadoop/hbase
    $ sudo -u hadoop sh -c "cd /home/hadoop/hbase ; git checkout origin/0.20_on_hadoop-0.18.3"
    HEAD is now at c050f68... pull up to release

    First we clone the repository, then switch to the actual branch. You will notice that I am using sudo -u hadoop because Hadoop itself is started under that account and so I wanted it to match. Also, the default "training" account does not have SSH set up as explained in Hadoop's quick-start guide. When sudo is asking for a password use the default set to "training".
  4. Build Branch

    Continue in Terminal:
    $ sudo -u hadoop sh -c "cd /home/hadoop/hbase/ ; export PATH=$PATH:/usr/share/apache-ant-1.7.1/bin ; ant package"
  5. Configure HBase

    There are a few edits to be made to get HBase running.
    $ sudo -u hadoop vim /home/hadoop/hbase/build/conf/hbase-site.xml
    $ sudo -u hadoop vim /home/hadoop/hbase/build/conf/hbase-env.sh 
    # The java implementation to use.  Java 1.6 required.
    # export JAVA_HOME=/usr/java/jdk1.6.0/
    export JAVA_HOME=/usr/lib/jvm/java-6-sun

    Note: There is a small glitch in the revision 826669 of that Cloudera specific HBase branch. The master UI (on port 60010 on localhost) will not start because a path is different and Jetty packages are missing because of it. You can fix it by editing the start up script and changing the path scanned:
    $ sudo -u hadoop vim /home/hadoop/hbase/build/bin/hbase

    for f in $HBASE_HOME/lib/jsp-2.1/*.jar; do
    for f in $HBASE_HOME/lib/jetty-ext/*.jar; do

    This is only until the developers have fixed this in the branch (compare the revision I used r813052 with what you get). Or if you do not want the UI you can ignore this and the error in the logs too. HBase will still run, just not its web based interface.
  6. Rev up the Engine!

    The final thing is to start HBase:
    $ sudo -u hadoop /home/hadoop/hbase/build/bin/start-hbase.sh
    $ sudo -u hadoop /home/hadoop/hbase/build/bin/hbase shell
    HBase Shell; enter 'help<RETURN>' for list of supported commands.
    Version: 0.20.0-0.18.3, r813052, Mon Oct 19 06:51:57 PDT 2009
    hbase(main):001:0> list
    0 row(s) in 0.2320 seconds


This sums it up. I hope you give HBase on the Cloudera Training VM a whirl as it also has Eclipse installed and therefore provides a quick start into Hadoop and HBase.

Just keep in mind that this is for prototyping only! With such a setup you will only be able to insert a handful of rows. If you overdo it you will bring it to its knees very quickly. But you can safely use it to play around with the shell to create tables or use the API to get used to it and test changes in your code etc.

Update: Updated title to include version number, fixed XML

Monday, October 12, 2009

HBase Architecture 101 - Storage

One of the more hidden aspects of HBase is how data is actually stored. While the majority of users may never have to bother about it you may have to get up to speed when you want to learn what the various advanced configuration options you have at your disposal mean. "How can I tune HBase to my needs?", and other similar questions are certainly interesting once you get over the (at times steep) learning curve of setting up a basic system. Another reason wanting to know more is if for whatever reason disaster strikes and you have to recover a HBase installation.

In my own efforts getting to know the respective classes that handle the various files I started to sketch a picture in my head illustrating the storage architecture of HBase. But while the ingenious and blessed committers of HBase easily navigate back and forth through that maze I find it much more difficult to keep a coherent image. So I decided to put that sketch to paper. Here it is.

Please note that this is not a UML or call graph but a merged picture of classes and the files they handle and by no means complete though focuses on the topic of this post. I will discuss the details below and also look at the configuration options and how they affect the low-level storage files.

The Big Picture

So what does my sketch of the HBase innards really say? You can see that HBase handles basically two kinds of file types. One is used for the write-ahead log and the other for the actual data storage. The files are primarily handled by the HRegionServer's. But in certain scenarios even the HMaster will have to perform low-level file operations. You may also notice that the actual files are in fact divided up into smaller blocks when stored within the Hadoop Distributed Filesystem (HDFS). This is also one of the areas where you can configure the system to handle larger or smaller data better. More on that later.

The general flow is that a new client contacts the Zookeeper quorum (a separate cluster of Zookeeper nodes) first to find a particular row key. It does so by retrieving the server name (i.e. host name) that hosts the -ROOT- region from Zookeeper. With that information it can query that server to get the server that hosts the .META. table. Both of these two details are cached and only looked up once. Lastly it can query the .META. server and retrieve the server that has the row the client is looking for.

Once it has been told where the row resides, i.e. in what region, it caches this information as well and contacts the HRegionServer hosting that region directly. So over time the client has a pretty complete picture of where to get rows from without needing to query the .META. server again.

Note: The HMaster is responsible to assign the regions to each HRegionServer when you start HBase. This also includes the "special" -ROOT- and .META. tables.

Next the HRegionServer opens the region it creates a corresponding HRegion object. When the HRegion is "opened" it sets up a Store instance for each HColumnFamily for every table as defined by the user beforehand. Each of the Store instances can in turn have one or more StoreFile instances, which are lightweight wrappers around the actual storage file called HFile. A HRegion also has a MemStore and a HLog instance. We will now have a look at how they work together but also where there are exceptions to the rule.

Stay Put

So how is data written to the actual storage? The client issues a HTable.put(Put) request to the HRegionServer which hands the details to the matching HRegion instance. The first step is now to decide if the data should be first written to the "Write-Ahead-Log" (WAL) represented by the HLog class. The decision is based on the flag set by the client using Put.writeToWAL(boolean) method. The WAL is a standard Hadoop SequenceFile (although it is currently discussed if that should not be changed to a more HBase suitable file format) and it stores HLogKey's. These keys contain a sequential number as well as the actual data and are used to replay not yet persisted data after a server crash.

Once the data is written (or not) to the WAL it is placed in the MemStore. At the same time it is checked if the MemStore is full and in that case a flush to disk is requested. When the request is served by a separate thread in the HRegionServer it writes the data to an HFile located in the HDFS. It also saves the last written sequence number so the system knows what was persisted so far. Let"s have a look at the files now.


HBase has a configurable root directory in the HDFS but the default is /hbase. You can simply use the DFS tool of the Hadoop command line tool to look at the various files HBase stores.

$ hadoop dfs -lsr /hbase/docs
drwxr-xr-x - hadoop supergroup 0 2009-09-28 14:22 /hbase/.logs
drwxr-xr-x - hadoop supergroup 0 2009-10-15 14:33 /hbase/.logs/srv1.foo.bar,60020,1254172960891
-rw-r--r-- 3 hadoop supergroup 14980 2009-10-14 01:32 /hbase/.logs/srv1.foo.bar,60020,1254172960891/hlog.dat.1255509179458
-rw-r--r-- 3 hadoop supergroup 1773 2009-10-14 02:33 /hbase/.logs/srv1.foo.bar,60020,1254172960891/hlog.dat.1255512781014
-rw-r--r-- 3 hadoop supergroup 37902 2009-10-14 03:33 /hbase/.logs/srv1.foo.bar,60020,1254172960891/hlog.dat.1255516382506
-rw-r--r-- 3 hadoop supergroup 137648437 2009-09-28 14:20 /hbase/docs/1905740638/oldlogfile.log
drwxr-xr-x - hadoop supergroup 0 2009-09-27 18:03 /hbase/docs/999041123
-rw-r--r-- 3 hadoop supergroup 2323 2009-09-01 23:16 /hbase/docs/999041123/.regioninfo
drwxr-xr-x - hadoop supergroup 0 2009-10-13 01:36 /hbase/docs/999041123/cache
-rw-r--r-- 3 hadoop supergroup 91540404 2009-10-13 01:36 /hbase/docs/999041123/cache/5151973105100598304
drwxr-xr-x - hadoop supergroup 0 2009-09-27 18:03 /hbase/docs/999041123/contents
-rw-r--r-- 3 hadoop supergroup 333470401 2009-09-27 18:02 /hbase/docs/999041123/contents/4397485149704042145
drwxr-xr-x - hadoop supergroup 0 2009-09-04 01:16 /hbase/docs/999041123/language
-rw-r--r-- 3 hadoop supergroup 39499 2009-09-04 01:16 /hbase/docs/999041123/language/8466543386566168248
drwxr-xr-x - hadoop supergroup 0 2009-09-04 01:16 /hbase/docs/999041123/mimetype
-rw-r--r-- 3 hadoop supergroup 134729 2009-09-04 01:16 /hbase/docs/999041123/mimetype/786163868456226374
drwxr-xr-x - hadoop supergroup 0 2009-10-08 22:45 /hbase/docs/999882558
-rw-r--r-- 3 hadoop supergroup 2867 2009-10-08 22:45 /hbase/docs/999882558/.regioninfo
drwxr-xr-x - hadoop supergroup 0 2009-10-09 23:01 /hbase/docs/999882558/cache
-rw-r--r-- 3 hadoop supergroup 45473255 2009-10-09 23:01 /hbase/docs/999882558/cache/974303626218211126
drwxr-xr-x - hadoop supergroup 0 2009-10-12 00:37 /hbase/docs/999882558/contents
-rw-r--r-- 3 hadoop supergroup 467410053 2009-10-12 00:36 /hbase/docs/999882558/contents/2507607731379043001
drwxr-xr-x - hadoop supergroup 0 2009-10-09 23:02 /hbase/docs/999882558/language
-rw-r--r-- 3 hadoop supergroup 541 2009-10-09 23:02 /hbase/docs/999882558/language/5662037059920609304
drwxr-xr-x - hadoop supergroup 0 2009-10-09 23:02 /hbase/docs/999882558/mimetype
-rw-r--r-- 3 hadoop supergroup 84447 2009-10-09 23:02 /hbase/docs/999882558/mimetype/2642281535820134018
drwxr-xr-x - hadoop supergroup 0 2009-10-14 10:58 /hbase/docs/compaction.dir

The first set of files are the log files handled by the HLog instances and which are created in a directory called .logs underneath the HBase root directory. Then there is another subdirectory for each HRegionServer and then a log for each HRegion.

Next there is a file called oldlogfile.log which you may not even see on your cluster. They are created by one of the exceptions I mentioned earlier as far as file access is concerned. They are a result of so called "log splits". When the HMaster starts and finds that there is a log file that is not handled by a HRegionServer anymore it splits the log copying the HLogKey's to the new regions they should be in. It places them directly in the region's directory in a file named oldlogfile.log. Now when the respective HRegion is instantiated it reads these files and inserts the contained data into its local MemStore and starts a flush to persist the data right away and delete the file.

Note: Sometimes you may see left-over oldlogfile.log.old (yes, there is another .old at the end) which are caused by the HMaster trying repeatedly to split the log and found there was already another split log in place. At that point you have to consult with the HRegionServer or HMaster logs to see what is going on and if you can remove those files. I found at times that they were empty and therefore could safely be removed.

The next set of files are the actual regions. Each region name is encoded using a Jenkins Hash function and a directory created for it. The reason to hash the region name is because it may contain characters that cannot be used in a path name in DFS. The Jenkins Hash always returns legal characters, as simple as that. So you get the following path structure:


In the root of the region directory there is also a .regioninfo holding meta data about the region. This will be used in the future by an HBase fsck utility (see HBASE-7) to be able to rebuild a broken .META. table. For a first usage of the region info can be seen in HBASE-1867.

In each column-family directory you can see the actual data files, which I explain in the following section in detail.

Something that I have not shown above are split regions with their initial daughter reference files. When a data file within a region grows larger than the configured hbase.hregion.max.filesize then the region is split in two. This is done initially very quickly because the system simply creates two reference files in the new regions now supposed to host each half. The name of the reference file is an ID with the hashed name of the referenced region as a postfix, e.g. 1278437856009925445.3323223323. The reference files only hold little information: the key the original region was split at and wether it is the top or bottom reference. Of note is that these references are then used by the HalfHFileReader class (which I also omitted from the big picture above as it is only used temporarily) to read the original region data files. Only upon a compaction the original files are rewritten into separate files in the new region directory. This also removes the small reference files as well as the original data file in the original region.

And this also concludes the file dump here, the last thing you see is a compaction.dir directory in each table directory. They are used when splitting or compacting regions as noted above. They are usually empty and are used as a scratch area to stage the new data files before swapping them into place.


So we are now at a very low level of HBase's architecture. HFile's (kudos to Ryan Rawson) are the actual storage files, specifically created to serve one purpose: store HBase's data fast and efficiently. They are apparently based on Hadoop's TFile (see HADOOP-3315) and mimic the SSTable format used in Googles BigTable architecture. The previous use of Hadoop's MapFile's in HBase proved to be not good enough performance wise. So how do the files look like?

The files have a variable length, the only fixed blocks are the FileInfo and Trailer block. As the picture shows it is the Trailer that has the pointers to the other blocks and it is written at the end of persisting the data to the file, finalizing the now immutable data store. The Index blocks record the offsets of the Data and Meta blocks. Both the Data and the Meta blocks are actually optional. But you most likely you would always find data in a data store file.

How is the block size configured? It is driven solely by the HColumnDescriptor which in turn is specified at table creation time by the user or defaults to reasonable standard values. Here is an example as shown in the master web based interface:

{NAME => 'docs', FAMILIES => [{NAME => 'cache', COMPRESSION => 'NONE', VERSIONS => '3', TTL => '2147483647', BLOCKSIZE => '65536', IN_MEMORY => 'false', BLOCKCACHE => 'false'}, {NAME => 'contents', COMPRESSION => 'NONE', VERSIONS => '3', TTL => '2147483647', BLOCKSIZE => '65536', IN_MEMORY => 'false', BLOCKCACHE => 'false'}, ...

The default is "64KB" (or 65535 bytes). Here is what the HFile JavaDoc explains:

"Minimum block size. We recommend a setting of minimum block size between 8KB to 1MB for general usage. Larger block size is preferred if files are primarily for sequential access. However, it would lead to inefficient random access (because there are more data to decompress). Smaller blocks are good for random access, but require more memory to hold the block index, and may be slower to create (because we must flush the compressor stream at the conclusion of each data block, which leads to an FS I/O flush). Further, due to the internal caching in Compression codec, the smallest possible block size would be around 20KB-30KB."

So each block with its prefixed "magic" header contains either plain or compressed data. How that looks like we will have a look at in the next section.

One thing you may notice is that the default block size for files in DFS is 64MB, which is 1024 times what the HFile default block size is. So the HBase storage files blocks do not match the Hadoop blocks. Therefore you have to think about both parameters separately and find the sweet spot in terms of performance for your particular setup.

One option in the HBase configuration you may see is hfile.min.blocksize.size. It seems to be only used during migration from earlier versions of HBase (since it had no block file format) and when directly creating HFile during bulk imports for example.

So far so good, but how can you see if a HFile is OK or what data it contains? There is an App for that!

The HFile.main() method provides the tools to dump a data file:

$ hbase org.apache.hadoop.hbase.io.hfile.HFile
usage: HFile [-f ] [-v] [-r ] [-a] [-p] [-m] [-k]
-a,--checkfamily Enable family check
-f,--file File to scan. Pass full-path; e.g.
-k,--checkrow Enable row order check; looks for out-of-order keys
-m,--printmeta Print meta data of file
-p,--printkv Print key/value pairs
-r,--region Region to scan. Pass region name; e.g. '.META.,,1'
-v,--verbose Verbose output; emits file and meta data delimiters

Here is an example of what the output will look like (shortened here):

$ hbase org.apache.hadoop.hbase.io.hfile.HFile -v -p -m -f \

Scanning -> hdfs://srv1.foo.bar:9000/hbase/docs/999882558/mimetype/2642281535820134018
K: \x00\x04docA\x08mimetype\x00\x00\x01\x23y\x60\xE7\xB5\x04 V: text\x2Fxml
K: \x00\x04docB\x08mimetype\x00\x00\x01\x23x\x8C\x1C\x5E\x04 V: text\x2Fxml
K: \x00\x04docC\x08mimetype\x00\x00\x01\x23xz\xC08\x04 V: text\x2Fxml
K: \x00\x04docD\x08mimetype\x00\x00\x01\x23y\x1EK\x15\x04 V: text\x2Fxml
K: \x00\x04docE\x08mimetype\x00\x00\x01\x23x\xF3\x23n\x04 V: text\x2Fxml
Scanned kv count -> 1554

Block index size as per heapsize: 296
reader=hdfs://srv1.foo.bar:9000/hbase/docs/999882558/mimetype/2642281535820134018, \
compression=none, inMemory=false, \
firstKey=US6683275_20040127/mimetype:/1251853756871/Put, \
lastKey=US6684814_20040203/mimetype:/1251864683374/Put, \
avgKeyLen=37, avgValueLen=8, \
entries=1554, length=84447
fileinfoOffset=84055, dataIndexOffset=84277, dataIndexCount=2, metaIndexOffset=0, \
metaIndexCount=0, totalBytes=84055, entryCount=1554, version=1
MAX_SEQ_ID_KEY = 32041891
hfile.AVG_KEY_LEN = \x00\x00\x00\x25
hfile.AVG_VALUE_LEN = \x00\x00\x00\x08
hfile.COMPARATOR = org.apache.hadoop.hbase.KeyValue\x24KeyComparator
hfile.LASTKEY = \x00\x12US6684814_20040203\x08mimetype\x00\x00\x01\x23x\xF3\x23n\x04

The first part is the actual data stored as KeyValue pairs, explained in detail in the next section. The second part dumps the internal HFile.Reader properties as well as the Trailer block details and finally the FileInfo block values. This is a great way to check if a data file is still healthy.


In essence each KeyValue in the HFile is simply a low-level byte array that allows for "zero-copy" access to the data, even with lazy or custom parsing if necessary. How are the instances arranged?

The structure starts with two fixed length numbers indicating the size of the key and the value part. With that info you can offset into the array to for example get direct access to the value, ignoring the key - if you know what you are doing. Otherwise you can get the required information from the key part. Once parsed into a KeyValue object you have getters to access the details.

Note: One thing to watch out for is the difference between KeyValue.getKey() and KeyValue.getRow(). I think for me the confusion arose from referring to "row keys" as the primary key to get a row out of HBase. That would be the latter of the two methods, i.e. KeyValue.getRow(). The former simply returns the complete byte array part representing the raw "key" as colored and labeled in the diagram.

This concludes my analysis of the HBase storage architecture. I hope it provides a starting point for your own efforts to dig into the grimy details. Have fun!

Update: Slightly updated with more links to JIRA issues. Also added Zookeeper to be more precise about the current mechanisms to look up a region.

Update 2: Added details about region references.

Update 3: Added more details about region lookup as requested.

Hive vs. Pig

While I was looking at Hive and Pig for processing large amounts of data without the need to write MapReduce code I found that there is no easy way to compare them against each other without reading into both in greater detail.

In this post I am trying to give you a 10,000ft view of both and compare some of the more prominent and interesting features. The following table - which is discussed below - compares what I deemed to be such features:








Yes (explicit)

Yes (implicit)





Optional (Thrift)


User Defined Functions (UDF)

Yes (Java)

Yes (Java)

Custom Serializer/Deserializer



DFS Direct Access

Yes (implicit)

Yes (explicit)










Web Interface




Yes (limited)


Let us look now into each of these with a bit more detail.

General Purpose

The question is "What does Hive or Pig solve?". Both - and I think this lucky for us in regards to comparing them - have a very similar goal. They try to ease the complexity of writing MapReduce jobs in a programming language like Java by giving the user a set of tools that they may be more familiar with (more on this below). The raw data is stored in Hadoop's HDFS and can be any format although natively it usually is a TAB separated text file, while internally they also may make use of Hadoop's SequenceFile file format. The idea is to be able to parse the raw data file, for example a web server log file, and use the contained information to slice and dice them into what is needed for business needs. Therefore they provide means to aggregate fields based on specific keys. In the end they both emit the result again in either text or a custom file format. Efforts are also underway to have both use other systems as a source for data, for example HBase.

The features I am comparing are chosen pretty much at random because they stood out when I read into each of these two frameworks. So keep in mind that this is a subjective list.


Hive lends itself to SQL. But since we can only read already existing files in HDFS it is lacking UPDATE or DELETE support for example. It focuses primarily on the query part of SQL. But even there it has its own spin on things to reflect better the underlaying MapReduce process. Overall is seems that someone familiar with SQL can very quickly learn Hive's version of it and get results fast.

Pig on the other hand looks more like a very simplistic scripting language. As with those (and this is a nearly religious topic) some are more intuitive and some are less. As with PigLatin I was able to see what the samples do, but lacking the full knowledge of its syntax I was somewhat finding myself thinking if I really would be able to get what I needed without too many trial-and-error loops. Sure, the Hive SQL needs probably as many iterations to fully grasp - but there is at least a greater understanding of what to expect.


Hive uses once more a specific variation of SQL's Data Definition Language (DDL). It defines the "tables" beforehand and stores the schema in a either shared or local database. Any JDBC offering will do, but it also comes with a built in Derby instance to get you started quickly. If the database is local then only you can run specific Hive commands. If you share the database then others can also run these - or would have to set up their own local database copy. Types are also defined upfront and supported types are INT, BIGINT, BOOLEAN, STRING and so on. There are also array types that lets you handle specific fields in the raw data files as a group.

Pig has no such metadata database. Datatypes and schemas are defined within each script. Types furthermore are usually automatically determined by their use. So if you use a field as an Integer it is handled that way by Pig. You do have the option though to override it and have explicit type definitions, again within the script you need them. Pig has a similar set of types compared to Hive. For example it also has an array type called "bag".


Hive has a notion of partitions. They are basically subdirectories in HDFS. It allows for example processing a subset of the data by alphabet or date. It is up to the user to create these "partitions" as they are not enforced nor required.

Pig does not seem to have such a feature. It may be that filters can achieve the same but it is not immediately obvious to me.


Hive can start an optional server, which is allegedly Thrift based. With the server I presume you can send queries from anywhere to the Hive server which in turn executes them.

Pig does not seem to have such a facility yet.

User Defined Functions

Hive and Pig allow for user functionality by supplying Java code to the query process. These functions can add any additional feature that is required to crunch the numbers as required.

Custom Serializer/Deserializer

Again, both Hive and Pig allow for custom Java classes that can read or write any file format required. I also assume that is how it connects to HBase eventually (just a guess). You can write a parser for Apache log files or, for example, the binary Tokyo Tyrant Ulog format. The same goes for the output, write a database output class and you can write the results back into a database.

DFS Direct Access

Hive is smart about how to access the raw data. A "select * from table limit 10" for example does a direct read from the file. If the query is too complicated it will fall back to use a full MapReduce run to determine the outcome, just as expected.

With Pig I am not sure if it does the same to speed up simple PigLatin scripts. At least it does not seem to be mentioned anywhere as an important feature.


Hive and Pig have support for joining, ordering or sorting data dynamically. They perform the same purpose in both pretty allowing you to aggregate and sort the result as is needed. Pig also has a COGROUP feature that allows you to do OUTER JOIN's and so on. I think this is where you spent most of your time with either package - especially when you start out. But from a cursory look it seems both can do pretty much the same.


Both Hive and Pig have a shell that allows you to query specific things or run the actual queries. Pig also passes on DFS commands such as "cat" to allow you to quickly check what an outcome of a specific PigLatin script was.


Once more, both frameworks seem to provide streaming interfaces so that you can process data with external tools or languages, such as Ruby or Python. How the streaming performs I do not know and if they affect them differently. This is for you to tell me :)

Web Interface

Only Hive has a web interface or UI that can be used to visualize the various schemas and issue queries. This is different to the above mentioned Server as it is an interactive web UI for a human operator. The Hive Server is for use from another programming or scripting language for example.


Another Hive only feature is the availability of a - again limited functionality - JDBC/ODBC driver. It is another way for programmers to use Hive without having to bother with its shell or web interface, or even the Hive Server. Since only a subset of features is available it will require small adjustments on the programmers side of things but otherwise seems like a nice-to-have feature.


Well, it seems to me that both can help you achieve the same goals, while Hive comes more natural to database developers and Pig to "script kiddies" (just kidding). Hive has more features as far as access choices are concerned. They also have reportedly roughly the same amount of committers in each project and are going strong development wise.

This is it from me. Do you have a different opinion or comment on the above then please feel free to reply below. Over and out!