September 2011
Intermediate to advanced
552 pages
16h 31m
English
Content preview from HBase: The Definitive GuideBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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Appendix F. HBase Versus Bigtable
Overall, HBase implements close to all of the features described in Chapter 1. Where it differs, it may have to because either the Bigtable paper was not very clear to begin with, or it relies on other open source projects to provide various services and those simply work differently.
HBase stores timestamps in milliseconds—as opposed to Bigtable, which uses microseconds. This is not much of an issue and can possibly be attributed to C and Java having different preferred timer resolutions.
While we have not yet addressed the specific details, it should be pointed out that both also use different compression algorithms. HBase uses those supplied in Java, but can also use LZO (with a bit of work; we will look into this later).[132] Bigtable has a two-phase compression using BMDiff and Zippy.
HBase has coprocessors that are different from what Sawzall, the scripting language used in Bigtable to filter or aggregate data, or the Bigtable Coprocessor framework,[133] provides. The details on Google’s coprocessor implementation are rather sketchy, so if there are more differences, they are unknown. On the other hand, HBase has support for server-side filters that help reduce the amount of data being moved from the server to the client.
HBase does primarily work with the
Hadoop Distributed File System (HDFS), while Bigtable
uses GFS. But HBase can also work on other filesystems thanks to the
pluggable FileSystem class provided by Hadoop. There are implementations ...
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