Sudarshan,
Below are the results that Mujtaba put together. He put together two
version of your schema: one with the ATTRIBID as part of the row key
and one with it as a key value. He also benchmarked the query time both
when all of the data was in the cache versus when all of the data was
read off of disk.
Let us know if you have any questions/follow up.
Thanks,
James (& Mujtaba)
Compute Average over 250K random rows in 1B row table
ATTRIBID in row key
Data from HBase cache Data loaded from disk
Phoenix Skip Scan 1.4 sec 31 sec
HBase Batched Gets 3.8 sec 58 sec
HBase Range Scan - 10+ min
ATTRIBID as key value
Data from HBase cache Data loaded from disk
Phoenix Skip Scan 1.7 sec 37 sec
HBase Batched Gets 4.0 sec 82 sec
HBase Range Scan - 10+ min
Details
-------
HBase 0.94.7 Hadoop 1.04
Total number of regions: 30 spread on 4 Region Servers (6 core W3680 Xeon
3.3GHz) with 8GB heap.
Data:
20 FIELDTYPE, 50M OBJECTID for each FIELDTYPE, 10 ATTRIBID. VAL is random
integer.
Query:
SELECT AVG(VAL) FROM T1
WHERE OBJECTID IN (250K RANDOM OBJECTIDs) AND FIELDTYPE = 'F1' AND ATTRIBID =
'1'
Create table DML:
1. CREATE TABLE IF NOT EXISTS T1 (
OBJECTID INTEGER NOT NULL,
FIELDTYPE CHAR(2) NOT NULL,
ATTRIBID INTEGER NOT NULL,
CF.VAL INTEGER
CONSTRAINT PK PRIMARY KEY (OBJECTID,FIELDTYPE,ATTRIBID))
COMPRESSION='GZ', BLOCKSIZE='4096'
2. CREATE TABLE IF NOT EXISTS T2 (
OBJECTID INTEGER NOT NULL,
FIELDTYPE CHAR(2) NOT NULL,
CF.ATTRIBID INTEGER,
CF.VAL INTEGER
CONSTRAINT PK PRIMARY KEY (OBJECTID,FIELDTYPE))
COMPRESSION='GZ', BLOCKSIZE='4096'
On 04/25/2013 04:19 PM, Sudarshan Kadambi (BLOOMBERG/ 731 LEXIN) wrote:
James: First of all, this looks quite promising.
The table schema outlined in your other message is correct except that
attrib_id will not be in the primary key. Will that be a problem with respect
to the skip-scan filter's performance? (it doesn't seem like it...)
Could you share any sort of benchmark numbers? I want to try this out right
away, but I've to wait for my cluster administrator to upgrade us from HBase
0.92 first!
----- Original Message -----
From: [email protected]
To: [email protected]
At: Apr 25 2013 18:45:14
On 04/25/2013 03:35 PM, Gary Helmling wrote:
I'm looking to write a service that runs alongside the region servers and
acts a proxy b/w my application and the region servers.
I plan to use the logic in HBase client's HConnectionManager, to segment
my request of 1M rowkeys into sub-requests per region-server. These are
sent over to the proxy which fetches the data from the region server,
aggregates locally and sends data back. Does this sound reasonable or even
a useful thing to pursue?
This is essentially what coprocessor endpoints (called through
HTable.coprocessorExec()) basically do. (One difference is that there is a
parallel request per-region, not per-region server, though that is a
potential optimization that could be made as well).
The tricky part I see for the case you describe is splitting your full set
of row keys up correctly per region. You could send the full set of row
keys to each endpoint invocation, and have the endpoint implementation
filter down to only those keys present in the current region. But that
would be a lot of overhead on the request side. You could split the row
keys into per-region sets on the client side, but I'm not sure we provide
sufficient context for the Batch.Callable instance you provide to
coprocessorExec() to determine which region it is being invoked against.
Sudarshan,
In our head branch of Phoenix (we're targeting this for a 1.2 release in
two weeks), we've implemented a skip scan filter that functions similar
to a batched get, except:
1) it's more flexible in that it can jump not only from a single key to
another single key, but also from range to range
2) it's faster, about 3-4x.
3) you can use it in combination with aggregation, since it's a filter
The scan is chunked up by region and only the keys in each region are
sent, along the lines as you and Gary have described. Then the results
are merged together by the client automatically.
How would you decompose your row key into columns? Is there a time
component? Let me walk you through an example where you might have a
LONG id value plus perhaps a timestamp (it work equally well if you only
had a single column in your PK). If you provide a bit more info on your
use case, I can tailor it more exactly.
Create a schema:
CREATE TABLE t (key BIGINT NOT NULL, ts DATE NOT NULL, data VARCHAR
CONSTRAINT pk PRIMARY KEY (key, ts));
Populate your data using our UPSERT statement.
Aggregate over a set of keys like this:
SELECT count(*) FROM t WHERE key IN (?,?,?) AND ts > ? AND ts < ?
where you bind the ? at runtime (probably building the statement
programmatically based on how many keys you're binding.
Then Phoenix would jump around the key space of your table using the
skip next hint feature provided by filters. You'd just use the regular
JDBC ResultSet to get your count back.
If you want more info and/or a benchmark of seeking over 250K keys in a
billion row table, let me know.
Thanks,
James
