You should double-check your data, you might find that it's null padded
or something like that which would screw up the splits. You can do a
scan from the shell which might give you hints.
On Tue, Jul 29, 2014 at 3:53 PM, Pelton, Aaron A.
<[email protected] <mailto:[email protected]>> wrote:
I agree with the idea of pooling the writers.
As for the discussion of the keys. I get what you are saying with
choosing better keys for distribution based on frequency of the
chars in the English language. But, for this test I'm just using
apache RandomStringUtils to create a 2 char random alpha sequence to
prepend, so it should be a moderately distributed sampling of chars.
However, let me emphasize that I mean I'm seeing 1 tablet getting
millions of entries in it, compared to the remaining 35 tablets
having no entries or just like 1k. To me that says something isn't
right.
-----Original Message-----
From: Josh Elser [mailto:[email protected]
<mailto:[email protected]>]
Sent: Tuesday, July 29, 2014 4:20 PM
To: [email protected] <mailto:[email protected]>
Subject: Re: Request for Configuration Help for basic test. Tservers
dying and only one tablet being used
On 7/29/14, 3:20 PM, Pelton, Aaron A. wrote:
> To followup to two of your statements/questions:
>
> 1. Good, pre-splitting your table should help with random data,
but if you're only writing data to one tablet, you're stuck (very
similar to hot-spotting reducers in MapReduce jobs).
>
> - OK so its good that the data is presplitting, but maybe this is
conceptually something that I'm not grasping about accumulo yet, but
I thought specifying the pre-splits is what causes the table to span
multiple tablets on the various tserver initially. However, the
core of the data appears to be in one specific tablet on on tserver.
Each tserver appears to have a few tablets allocated to it for the
table I'm working out of. So, I'm confused as to how to get the data
to write to more than just the one tablet/partition. I would almost
think my keys I specified aren't being matched correctly against
incoming data then?
No, it sounds like you have the idea correctly. Many tablets make up
a table, the split points for a table are what defines those tablet
boundaries. Consider you have a table where the rowID are English
words
(http://en.wikipedia.org/wiki/Letter_frequency#Relative_frequencies_of_the_first_letters_of_a_word_in_the_English_language).
If you split your table on each letter (a-z), you would still see
much more activity to the tablets which host words starting with
'a', 't', and 's' because you have significantly more data being
ingested into those tablets.
When designing a table (specifically the rowID of the key), it's
desirable to try to make the rowID as distributed as possible across
the entire table. This helps ensure even processing across all of
your nodes. Does that make sense?
> 2. What do you actually do when you receive an HTTP request to
write to Accumulo. It sounds like you're reading data and then
writing? Is each HTTP request creating its own BatchWriter? More
insight to what a "write" looks like in your system (in terms of
Accumulo API calls) would help us make recommendations about more
efficient things you can do.
>
> Yes each http request gets its own reference to a writer or
scanner, which is closed when thre result is returned from the http
request. There are two rest services. One transforms the data and
preforms some indexes based on it and then sends both data and index
to a BatchWriter. The sample code for the data being written is
below. The indexes being written are similar but use different
family and qualifier values.
>
> Text rowId = new Text(id + ":" + time);
> Text fam = new Text(COLUMN_FAMILY_KLV);
> Text qual = new Text("");
> Value val = new Value(data.getBytes());
>
> Mutation mut = new Mutation(rowId);
> mut.put(fam, qual, val);
>
> long memBuf = 1_000_000L;
> long timeout = 1000L;
> int numThreads = 10;
>
> BatchWriter writer = null;
> try
> {
> writer = conn.createBatchWriter(TABLE_NAME, memBuf,
timeout, numThreads);
> writer.addMutation(mut);
> }
> catch (Exception x)
> {
> // x.printStackTrace();
> logger.error(x.toString(), x);
> result = "ERROR";
> }
> finally
> {
> try
> {
> if (writer != null)
> {
> writer.close();
> }
> }
> catch (Exception x)
> {
> // x.printStackTrace();
> logger.error(x.toString(), x);
> result = "ERROR";
> }
> }
You could try to make a threadpool for BatchWriters instead of
creating a new one for each HTTP thread. This might help amortize
the RPC cost by sending more than one mutation at a time (the
BatchWriter should be thread safe in this regard). You then just
want to call flush() instead of closing the BatchWriter.
I remember seeing that there are some optimizations within the
BatchWriter to write a single Mutation, but if you're really trying
to saturate your system, using fewer BatchWriters would likely help
you realize more throughput.
> At the beginning of the test, a known subset of control data
range is created and uploaded. For the duration of the heart of the
test while ongoing writes occur, queries upon data in that control
range are performed. The rest service that handles the read
eventually hits this:
>
> ArrayList<String> latlons = new ArrayList<String>();
> Authorizations auths = new Authorizations();
>
> Scanner scan = null;
> try
> {
> scan = conn.createScanner(TABLE_NAME, auths);
> scan.setRange(new Range(id + ":0", id + "::")); //
all times
> scan.fetchColumnFamily(new Text(COLUMN_FAMILY_KLV));
>
> for (Map.Entry<Key, Value> e : scan)
> {
> // do stuff with e
> }
> }
> catch (TableNotFoundException x)
> {
> LOGGER.fatal("The table " + TABLE_NAME + " could not
be found.", x);
> }
> finally
> {
> if (scan != null)
> {
> scan.close();
> }
> }
>
> -----Original Message-----
> From: Josh Elser [mailto:[email protected]
<mailto:[email protected]>]
> Sent: Tuesday, July 29, 2014 1:43 PM
> To: [email protected] <mailto:[email protected]>
> Subject: Re: Request for Configuration Help for basic test. Tservers
> dying and only one tablet being used
>
> Some comments inline
>
> On 7/29/14, 1:07 PM, Pelton, Aaron A. wrote:
>> Hi All,
>>
>> I am new to Accumulo and I apologize if the answers to my questions
>> are already posted somewhere. I've done a fair amount of
googling and
>> poking around the manuals etc.
>>
>> I am just doing a simple test with two machines, one producing about
>> 600 threads on the network to stream simultaneous writes to a rest
>> service, and the other producing about 300 threads on the network to
>> perform simultaneous queries to a rest service. The rest service has
>> Accumulo API calls in it to write out and query data.
>>
>> I have inherited the following configuration
>>
>> -Squirrel Bundle distribution of Accumulo 1.5.0
>>
>> -1 Master machine to start and stop Accumulo services on
>>
>> -12 data nodes running tservers. The first three of these also
>> running the zookeeper instances. And, nodes 4-6 running tracers.
>>
>> I have noticed the following issues with configuration and changed
>> them as follows
>>
>> -Changed swapiness to 0 on all nodes
>>
>> -Was getting OutOfMemoryExceptions after the above still, and after
>> running test for long duration. Thus, increased Java Heap size from
>> 1g to 4g, which is still far below the physical ram on the nodes.
>>
>> -Increased java heap from 1g to 2g on master node
>>
>> -I also increased the following properties
>>
>> o <property>
>>
>> o <name>tserver.memory.maps.max</name>
>>
>> o <value>2G</value>
>>
>> o </property>
>>
>> o
>>
>> o <property>
>>
>> o <name>tserver.cache.data.size</name>
>>
>> o <value>512M</value>
>>
>> o </property>
>>
>> o
>>
>> o <property>
>>
>> o <name>tserver.cache.index.size</name>
>>
>> o <value>512M</value>
>>
>> o </property>
>>
>> -Changed the ulimit for virtual memory to unlimited
>>
>> -Changed the ulimit for files opened to 65536
>>
>> -Changed the ulimit for max user processes to 1024
>
> These all look good. Just keep in mind that tserver.cache.data.size
> and tserver.cache.index.size will be on the JVM heap while
> tserver.memory.maps.max is off heap (assuming you're using the native
> maps which you very well should be -- I assume Sqrrl's distro set
this
> up for you)
>
>> -A tomcat instance with a server socket accepting up to 1,000
threads
>> / user connections to a rest service that eventually makes a read /
>> write out to an Accumulo connector instance.
>>
>> -Changed the zookeeper connection limit max to 0 since this is
just a
>> test environment
>>
>> -Noticed that code I had inherited didn't have close calls on the
>> scanner objects in the rest service b/c it was originally designed
>> for Accumulo 1.4 in which there wasn't such an API.
>
> Scanners can clean up after themselves, whereas BatchScanners
don't. A
> close method was added to ScannerBase (the parent class of
Scanner and
> BatchScanner) to let you seamlessly swap out a Scanner with a
BatchScanner (and vice versa) while not leaking any resources. In
short, you can call Scanner#close, but it's just a no-op.
>
>> -This may be wrong, but in an effort to see my ~900 connections
>> simultaneously get as much access to db writes/reads for
servicing, I
>> up'd some thread counts for
>>
>> o <property>
>>
>> o <name>tserver.server.threads.minimum</name>
>>
>> o <value>75</value>
>>
>> o </property>
>>
>> o
>>
>> o <property>
>>
>> o <name>master.server.threads.minimum</name>
>>
>> o <value>300</value>
>>
>> o </property>
>>
>> I have a couple of problems to note:
>>
>> 1.Ingest speeds seem kinda slow. I would anticipate network overhead
>> but not enough to reduce writes to 125 records / sec when each
record
>> is only a few kB.
>
> What do you actually do when you receive an HTTP request to write
to Accumulo. It sounds like you're reading data and then writing? Is
each HTTP request creating its own BatchWriter? More insight to what
a "write" looks like in your system (in terms of Accumulo API calls)
would help us make recommendations about more efficient things you
can do.
>
>> a.I believe this is due to the fact that I'm only seeing one tserver
>> primarily active at ingesting, with one tbalet in particular for the
>> table receiving the bulk of the data.
>>
>> b.I have added pre-splits upon table creation for each letter of the
>> alphabet, plus the digits 0-9. As this is a test with a simple loop
>> creating ID values, I throw 2 alpha chars randomly in front of the
>> generated number in my loop and use that as the ID to distribute
>> hopefully the IDs across tablets for this table. A record ID
>> ingested might look like "bk1234:8876", whereby it has random 2
>> chars, orig ID value, colon, and a timestamp. Sample pre-splitting:
>> (Granted the array could be constructed more gracefully, but for
a quick test, meh).
>>
>> *try*
>>
>> {
>>
>> conn.tableOperations().create(/TABLE_NAME/);
>>
>> *final*SortedSet<Text> sortedSplits = *new*TreeSet<Text>();
>>
>> *for*(String binPrefix : *new*String[] { "a", "b", "c", "d", "e",
>> "f", "g", "h", "i", "j", "k", "l", "m",
>>
>> "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z",
"1",
>> "2", "3", "4", "5", "6", "7",
>>
>> "8", "9", "0"})
>>
>> {
>>
>> sortedSplits.add(*new*Text(binPrefix));
>>
>> }
>>
>> conn.tableOperations().addSplits(/TABLE_NAME/, sortedSplits);
>>
>> }
>>
>> *catch*(TableExistsException | TableNotFoundException exception)
>>
>> {
>>
>> /LOGGER/.warn("Could not create table or sorted splits", exception);
>>
>> }
>
> Good, pre-splitting your table should help with random data, but
if you're only writing data to one tablet, you're stuck (very
similar to hot-spotting reducers in MapReduce jobs).
>
>> 2.Tservers running on the data node halt after about 4 hours in of
>> processing. I'm attempting to ingest into the billions, hopefully
>> trillions of records range. Generally it is the ones that aren't
>> under load in the beginning, until finally the one that is handling
>> the bulk of the load crashes typically last. In the beginning, I
>> noticed in the tserver logs the OutOfMemoryException, but haven't
>> seen that in the past few runs after the memory adjustments. In fact
>> the tserver log doesn't say anything about why it stopped. Also
>> didn't notice anything unusual in the zookeeper log other than
the occasional CancelledKeyException.
>
> Make sure you check both the tserver_hostname.debug.log,
tserver_hostname.out and tserver_hostname.err files. OOMEs sometimes
don't make it to the log file because of the JVM tearing down. You
should be able to find something as to why the tserver stopped.
>
>> 3.Lastly can anyone approximate with the 12 nodes that I have, what
>> kind of ingest speed should I see if things were configured
correctly
>> in number of records per second based on small record sizes of a
few kB.
>> And, is anything obviously wrong with the configurations mentioned
>> above that would improve throughput?
>
> Generally, a "normal" machine will be able to do ingest of about
200k records at 150bytes for ~30MB/s.
>
> You might also want to try increasing tserver.mutation.queue.max
to 1M in accumulo-site.xml (restart required). You can find some
extra information about that on the releases notes:
> http://accumulo.apache.org/release_notes/1.5.1.html#known-issues.
Not sure if Sqrrl's distribution has done this already for you.
>
>
>> ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>>
>> --
>>
>> Sincerely,
>>
>> Aaron Pelton
>>
