HDFS: All content tagged as HDFS in NoSQL databases and polyglot persistence
In my post about in-memory databases vs Aster Data and Greenplum vs Hadoop market share, I’ve proposed a scenario in which Aster Data and Greenplum could expand into the space of in-memory databases by employing hybrid storage.
What I haven’t covered in that post is the possibility of Hadoop, actually HDFS, expanding into hybrid storage.
But that’s happening already and Hortonworks is already working on introducing support for heterogeneous storages in HDFS:
We plan to introduce the idea of Storage Preferences for files. A Storage Preference is a hint to HDFS specifying how the application would like block replicas for the given file to be placed. Initially the Storage Preference will include:
- The desired number of file replicas (also called the replication factor) and;
- The target storage type for the replicas.
Even if the costs of memory will continue to decrease at the same rate as before 2012, when they flat-lined, a cost effective architecture will almost always rely on hybrid storage.
Original title and link: Heterogeneous storages in HDFS ( ©myNoSQL)
We abstracted out an HDFS layer but underneath that it is actually talking to lustre.
This is not the first project based on the principle “we already have this distributed system, file system or database, so why not reusing it for Hadoop?”. What would be the first step of such a project? Provide a HDFS API compatible layer on top of your existing system. But how about the other assumptions in HDFS: large block, sequential, local access, etc? How do you guarantee that your integration addressed all of them?
If this trends continues, I could see one of the companies behind the open source Hadoop, Cloudera or Hortonworks or both, coming up with a TCK sold to any company that claims HDFS compatibility.
Original title and link: Hadoop on top of… Intel adds Lustre support to Hadoop ( ©myNoSQL)
Great retrospective with many architecture details of the improvements added to HDFS in 2012 and what is planned for this year by Todd Lipcon.
For a quick overview:
- 2012: HDFS 2.0
- HA (in 2 phases)
- Performance improvements:
- for Impala: faster libhdfs, APIs for spindle-based scheduling
- for HBase and Accumulo: direct reads from block files in secure environments, application level checksums and IOPS elimintation
- on-the-wire encryption
- rolling upgrades and wire compatibility
- HDFS snapshots
- better storage density and file formats
- caching and hierarchical storage management
Original title and link: What’s New and Upcoming in HDFS ( ©myNoSQL)
A paper authored by a team from Universities of Wisconsin and Chicago:
We harden the Hadoop Distributed File System (HDFS) against fail- silent (non fail-stop) behaviors that result from memory corruption and software bugs using a new approach: selective and lightweight versioning (SLEEVE). With this approach, actions performed by important subsystems of HDFS (e.g., namespace management) are checked by a second implementation of the subsystem that uses lightweight, approximate data structures. We show that HARDFS detects and recovers from a wide range of fail-silent behaviors caused by random bit flips, targeted corruptions, and real software bugs. In particular, HARDFS handles 90% of the fail-silent faults that result from random memory corruption and correctly detects and recovers from 100% of 78 targeted corruptions and 5 real-world bugs. Moreover, it recov- ers orders of magnitude faster than full reboot by using micro-recovery. The extra protection in HARDFS incurs minimal performance and space overheads.
At very large scale, failures that we consider to be very rare can occur more frequently. HDFS already deals with handling machine and disk failure. This paper is about handling memory corruptions.
You can download it from here.
Original title and link: HDFS Paper: HARDFS - Hardening HDFS With Selective and Lightweight Versioning ( ©myNoSQL)
Quantcast released a new Hadoop file system QFS:
- fully compatible with HDFS
- licensed under Apache 2.0 license
- written in C++
- while HDFS replicates data 3 times, QFS requires only 1.5x raw capacity
- QFS supports two types of fault tolerance: chunk replication and Reed-Solomon encoding
QFS components (more details here):
QFS performance comparison to HDFS:
Now I’m looking forward to hear comments from HDFS experts about QFS.
Original title and link: Quantcast File System for Hadoop ( ©myNoSQL)
It’s unfortunate that the post focuses mostly on the usage of Spring and RabitMQ and the slidedeck doesn’t dive deeper into the architecture, data flows, and data stores, but the diagrams below should give you an idea of this truly polyglot persistentency architecture:
The slide deck presenting architecture principles and numbers about the platform after the break.