How large is the data set you are working with, and your
cluster/nodes?
Just testing with that single 44GB source file currently, and my test
cluster is made from 4 nodes, each with 8 CPU cores, 32GB RAM, a 6TB
Ext4 volume (RAID-10).
Drill defaults left as come in v1.0. I will be adjusting memory and
retrying the CTAS.
I know I can / should assign individual disks to HDFS, but as a test
cluster there are apps that expect data volumes to work on. A dedicated
Hadoop production cluster would have a disk layout specific to the task.
On 28 May 2015, at 12:26, Andries Engelbrecht wrote:
Just check the drillbit.log and drillbit.out files in the log
directory.
Before adjusting memory, see if that is an issue first. It was for me,
but as Jason mentioned there can be other causes as well.
You adjust memory allocation in the drill-env.sh files, and have to
restart the drill bits.
How large is the data set you are working with, and your
cluster/nodes?
—Andries
On May 28, 2015, at 9:17 AM, Matt <bsg...@gmail.com> wrote:
To make sure I am adjusting the correct config, these are heap
parameters within the Drill configure path, not for Hadoop or
Zookeeper?
On May 28, 2015, at 12:08 PM, Jason Altekruse
<altekruseja...@gmail.com> wrote:
There should be no upper limit on the size of the tables you can
create
with Drill. Be advised that Drill does currently operate entirely
optimistically in regards to available resources. If a network
connection
between two drillbits fails during a query, we will not currently
re-schedule the work to make use of remaining nodes and network
connections
that are still live. While we have had a good amount of success
using Drill
for data conversion, be aware that these conditions could cause long
running queries to fail.
That being said, it isn't the only possible cause for such a
failure. In
the case of a network failure we would expect to see a message
returned to
you that part of the query was unsuccessful and that it had been
cancelled.
Andries has a good suggestion in regards to checking the heap
memory, this
should also be detected and reported back to you at the CLI, but we
may be
failing to propagate the error back to the head node for the query.
I
believe writing parquet may still be the most heap-intensive
operation in
Drill, despite our efforts to refactor the write path to use direct
memory
instead of on-heap for large buffers needed in the process of
creating
parquet files.
On Thu, May 28, 2015 at 8:43 AM, Matt <bsg...@gmail.com> wrote:
Is 300MM records too much to do in a single CTAS statement?
After almost 23 hours I killed the query (^c) and it returned:
~~~
+-----------+----------------------------+
| Fragment | Number of records written |
+-----------+----------------------------+
| 1_20 | 13568824 |
| 1_15 | 12411822 |
| 1_7 | 12470329 |
| 1_12 | 13693867 |
| 1_5 | 13292136 |
| 1_18 | 13874321 |
| 1_16 | 13303094 |
| 1_9 | 13639049 |
| 1_10 | 13698380 |
| 1_22 | 13501073 |
| 1_8 | 13533736 |
| 1_2 | 13549402 |
| 1_21 | 13665183 |
| 1_0 | 13544745 |
| 1_4 | 13532957 |
| 1_19 | 12767473 |
| 1_17 | 13670687 |
| 1_13 | 13469515 |
| 1_23 | 12517632 |
| 1_6 | 13634338 |
| 1_14 | 13611322 |
| 1_3 | 13061900 |
| 1_11 | 12760978 |
+-----------+----------------------------+
23 rows selected (82294.854 seconds)
~~~
The sum of those record counts is 306,772,763 which is close to
the
320,843,454 in the source file:
~~~
0: jdbc:drill:zk=es05:2181> select count(*) FROM
root.`sample_201501.dat`;
+------------+
| EXPR$0 |
+------------+
| 320843454 |
+------------+
1 row selected (384.665 seconds)
~~~
It represents one month of data, 4 key columns and 38 numeric
measure
columns, which could also be partitioned daily. The test here was
to create
monthly Parquet files to see how the min/max stats on Parquet
chunks help
with range select performance.
Instead of a small number of large monthly RDBMS tables, I am
attempting
to determine how many Parquet files should be used with Drill /
HDFS.
On 27 May 2015, at 15:17, Matt wrote:
Attempting to create a Parquet backed table with a CTAS from an
44GB tab
delimited file in HDFS. The process seemed to be running, as CPU
and IO was
seen on all 4 nodes in this cluster, and .parquet files being
created in
the expected path.
In however in the last two hours or so, all nodes show near zero
CPU or
IO, and the Last Modified date on the .parquet have not changed.
Same time
delay shown in the Last Progress column in the active fragment
profile.
What approach can I take to determine what is happening (or not)?
