The hardest part about learning Hbase (the open source implementation of Google's BigTable), is just wrapping your mind around the concept of what it actually is.
I find it rather unfortunate that these two great systems contain the words table and base in their names, which tend to cause confusion among RDBMS indoctrinated individuals (like myself).
This article aims to describe these distributed data storage systems from a conceptual standpoint. After reading it, you should be better able to make an educated decision regarding when you might want to use Hbase vs when you'd be better off with a "traditional" database.
It's all in the terminology
Fortunately, Google's BigTable Paper clearly explains what BigTable actually is. Here is the first sentence of the "Data Model" section:
A Bigtable is a sparse, distributed, persistent multidimensional sorted map.
Note: At this juncture I like to give readers the opportunity to collect any brain matter which may have left their skulls upon reading that last line.
The BigTable paper continues, explaining that:
The map is indexed by a row key, column key, and a timestamp; each value in the map is an uninterpreted array of bytes.
Along those lines, the HbaseArchitecture page of the Hadoop wiki posits that:
HBase uses a data model very similar to that of Bigtable. Users store data rows in labelled tables. A data row has a sortable key and an arbitrary number of columns. The table is stored sparsely, so that rows in the same table can have crazily-varying columns, if the user likes.
Although all of that may seem rather cryptic, it makes sense once you break it down a word at a time. I like to discuss them in this sequence: map, persistent, distributed, sorted, multidimensional, and sparse.
Rather than trying to picture a complete system all at once, I find it easier to build up a mental framework piecemeal, to ease into it...
map
At its core, Hbase/BigTable is a map. Depending on your programming language background, you may be more familiar with the terms associative array (PHP), dictionary (Python), Hash (Ruby), or Object (JavaScript).
From the wikipedia article, a map is "an abstract data type composed of a collection of keys and a collection of values, where each key is associated with one value."
Using JavaScript Object Notation, here's an example of a simple map where all the values are just strings:
1.{
2."zzzzz" : "woot",
3."xyz" : "hello",
4."aaaab" : "world",
5."1" : "x",
6."aaaaa" : "y"
7.}
persistent
Persistence merely means that the data you put in this special map "persists" after the program that created or accessed it is finished. This is no different in concept than any other kind of persistent storage such as a file on a filesystem. Moving along...
distributed
Hbase and BigTable are built upon distributed filesystems so that the underlying file storage can be spread out among an array of independent machines.
Hbase sits atop either Hadoop's Distributed File System (HDFS) or Amazon's Simple Storage Service(S3), while a BigTable makes use of the Google File System (GFS).
Data is replicated across a number of participating nodes in an analogous manner to how data is striped across discs in a RAID system.
For the purpose of this article, we don't really care which distributed filesystem implementation is being used. The important thing to understand is that it is distributed, which provides a layer of protection against, say, a node within the cluster failing.
sorted
Unlike most map implementations, in Hbase/BigTable the key/value pairs are kept in strict alphabetical order. That is to say that the row for the key "aaaaa" should be right next to the row with key "aaaab" and very far from the row with key "zzzzz".
Continuing our JSON example, the sorted version looks like this:
1.{
2."1" : "x",
3."aaaaa" : "y",
4."aaaab" : "world",
5."xyz" : "hello",
6."zzzzz" : "woot"
7.}
Because these systems tend to be so huge and distributed, this sorting feature is actually very important. The spacial propinquity of rows with like keys ensures that when you must scan the table, the items of greatest interest to you are near each other.
This is important when choosing a row key convention. For example, consider a table whose keys are domain names. It makes the most sense to list them in reverse notation (so "com.jimbojw.www" rather than "www.jimbojw.com") so that rows about a subdomain will be near the parent domain row.
Continuing the domain example, the row for the domain "mail.jimbojw.com" would be right next to the row for "www.jimbojw.com" rather than say "mail.xyz.com" which would happen if the keys were regular domain notation.
It's important to note that the term "sorted" when applied to Hbase/BigTable does not mean that "values" are sorted. There is no automatic indexing of anything other than the keys, just as it would be in a plain-old map implementation.
multidimensional
multidimensional
Up to this point, we haven't mentioned any concept of "columns", treating the "table" instead as a regular-old hash/map in concept. This is entirely intentional. The word "column" is another loaded word like "table" and "base" which carries the emotional baggage of years of RDBMS experience.
Instead, I find it easier to think about this like a multidimensional map - a map of maps if you will. Adding one dimension to our running JSON example gives us this:
01.{
02."1" : {
03."A" : "x",
04."B" : "z"
05.},
06."aaaaa" : {
07."A" : "y",
08."B" : "w"
09.},
10."aaaab" : {
11."A" : "world",
12."B" : "ocean"
13.},
14."xyz" : {
15."A" : "hello",
16."B" : "there"
17.},
18."zzzzz" : {
19."A" : "woot",
20."B" : "1337"
21.}
22.}
In the above example, you'll notice now that each key points to a map with exactly two keys: "A" and "B". From here forward, we'll refer to the top-level key/map pair as a "row". Also, in BigTable/Hbase nomenclature, the "A" and "B" mappings would be called "Column Families".
A table's column families are specified when the table is created, and are difficult or impossible to modify later. It can also be expensive to add new column families, so it's a good idea to specify all the ones you'll need up front.
Fortunately, a column family may have any number of columns, denoted by a column "qualifier" or "label". Here's a subset of our JSON example again, this time with the column qualifier dimension built in:
01.{
02.// ...
03."aaaaa" : {
04."A" : {
05."foo" : "y",
06."bar" : "d"
07.},
08."B" : {
09."" :