On Thu, Sep 26, 2013 at 8:04 AM, Slater, David M.
> Awesome, thanks!
> For clarification, when the in memory map is full, if it contains data for
> multiple tablets, how does it prioritize which tablets to do minor
> compactions for?
The memory manager chooses a tablet to minor compact based on the tablet's
memory usage and idle time. It chooses the tablet with the largest (size
in bytes * 2^(minutes idle / 15)). If memory isn't full, but there are
tablets meeting a per-table idleness threshold, it will select one of those
to minor compact. It tries to initiate minor compactions before the memory
is completely full in an attempt to avoid blocking writes during minor
compactions. It uses a dynamic threshold, a percentage of the maximum
memory, to decide when to initiate them. It adjusts the threshold based on
how full the memory gets before a minor compaction is completed
> Is there a separate in memory map for each tablet on a tablet server? When
> the in memory map is full, will it do minor compactions until all of the
> data currently in it is empty, or will it trigger a smaller number of minor
> compactions until it is at a more reasonable size?
> It is unclear to me if you can change the minimum size of rfiles written
> by minor compactions.
> Also, how does the in memory map handle mutations for tablets that are
> currently doing a minor compaction?
> -----Original Message-----
> From: Christopher [mailto:[EMAIL PROTECTED]]
> Sent: Wednesday, September 25, 2013 4:49 PM
> To: Accumulo User List
> Subject: Re: WALOG Design
> Mutations are written to WALOGS when they are inserted into a TServer's
> in-memory map. The TServer's in-memory map gets flushed to disk
> periodically, but there's a risk that the TServer will die after the data
> has been ingested, but before it is flushed to disk. The WALOGS, when
> enabled, protect against this data loss, by first writing out incoming data
> to a WALOG. The WALOG is more efficient than creating RFiles, because it
> does not contain sorted data or indexes.
> It's just a playback file, so that in case of a failure, Mutations that
> the client believed had been ingested, aren't lost.
> Putting the WALOG in memory defeats the purpose of the WALOG, but it can
> be disabled (per-table), if you care more about performance than protection
> against data loss. Don't disable it for the !METADATA table, though...
> You can generate RFiles directly (perhaps using a M/R job), and bypass the
> WALOG, and bulk import them into Accumulo.
> Christopher L Tubbs II
> On Wed, Sep 25, 2013 at 4:39 PM, Slater, David M.
> <[EMAIL PROTECTED]> wrote:
> > First, thank you all for the responses on my BatchWriter question, as
> > I was able to increase my ingestion rate by a large factor. I am now
> > hitting disk i/o limits, which is forcing me to look at reducing file
> > copying. My primary thoughts concerning this are reducing the hadoop
> > replication factor as well as reducing the number of major compactions.
> > However, from what I understand about write ahead logs (in 1.4), even
> > if you remove all major compactions, all data will essentially be
> > written to disk
> > twice: once to the WALOG in the local directory (HDFS is 1.5), then
> > from the WALOG to an RFile on HDFS. Is this understanding correct?
> > I’m trying to understand what the primary reasons are for having the
> > Is there any way to write directly to an RFile from the In-Memory Map
> > (or have the WALOG in memory)?
> > Thanks,
> > David