Scaling HDFS with a Strongly Consistent Relational Model for Metadata

Hakimzadeh, Kamal and Peiro Sajjad, Hooman and Dowling, Jim (2014) Scaling HDFS with a Strongly Consistent Relational Model for Metadata. In: DAIS 2014, 3-6 June 2014, Berlin, Germany.

PDF (Scaling HDFS with a Strongly Consistent Relational Model for Metadata) - Published Version

Official URL:


The Hadoop Distributed File System (HDFS) scales to store tens of petabytes of data despite the fact that the entire file system's metadata must fit on the heap of a single Java virtual machine. The size of HDFS' metadata is limited to under 100 GB in production, as garbage collection events in bigger clusters result in heartbeats timing out to the metadata server(NameNode). In this paper, we address the problem of how to migrate the HDFS' metadata to a relational model, so that we can support larger amounts of storage on a shared nothing, in-memory, distributed database. Our main contribution is that we show how to provide at least as strong consistency semantics as HDFS while adding support for a multiple-writer, multiple-reader concurrency model. We guarantee freedom from deadlocks by logically organizing inodes (and their constituent blocks and replicas) into a hierarchy and having all metadata operations agree on a global order for acquiring both explicit locks and implicit locks on subtrees in the hierarchy. We use transactions with pessimistic concurrency control to ensure the safety and progress of metadata operations. Finally, we show how to improve performance of our solution by introducing a snapshotting mechanism at NameNodes that minimizes the number of roundtrips to the database

Item Type:Conference or Workshop Item (Speech)
ID Code:5724
Deposited By:Kamal Hakimzadeh
Deposited On:04 Nov 2014 09:05
Last Modified:04 Nov 2014 09:05

Repository Staff Only: item control page