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)? >>
