Statistics tool options

The statistics tool lets you access the history of microbatches that your scheduler has run. This tool outputs the log of the microbatches in JSON format to the standard output. You can use its options to filter the list of microbatches to get just the microbatches that interest you.

Syntax

vkconfig statistics [options]

--cluster "cluster"[,"cluster2"...]

Only return microbatches that retrieved data from a cluster whose name matches one in the list you supply.

--dump

Instead of returning microbatch data, return the SQL query that vkconfig would execute to extract the data from the scheduler tables. You can use this option if you want use a Vertica client application to get the microbatch log instead of using vkconfig's JSON output.

--from-timestamp "timestamp"

Only return microbatches that began after timestamp. The timestamp value is in yyyy-[m]m-[d]d hh:mm:ss format.

Cannot be used in conjunction with --last.

--last number

Returns the number most recent microbatches that meet all other filters. Cannot be used in conjunction with --from-timestamp or --to-timestamp.

--microbatch "name"[,"name2"...]

Only return microbatches whose name matches one of the names in the comma-separated list.

--partition partition#[,partition#2...]

Only return microbatches that accessed data from the topic partition that matches ones of the values in the partition list.

--source "source"[,"source2"...]

Only return microbatches that accessed data from a source whose name matches one of the names in the list you supply to this argument.

--target-schema "schema"[,"schema2"...]

Only return microbatches that wrote data to the Vertica schemas whose name matches one of the names in the target schema list argument.

--target-table "table"[,"table2"...]

Only return microbatches that wrote data to Vertica tables whose name match one of the names in the target schema list argument.

--to-timestamp "timestamp"

Only return microbatches that began before timestamp. The timestamp value is in yyyy-[m]m-[d]d hh:mm:ss format.

Cannot be used in conjunction with --last.

See Common vkconfig script options for options that are available in all of the vkconfig tools.

Usage considerations

• You can use LIKE wildcards in the values you supply to the --cluster, --microbatch, --source, --target-schema, and --target-table arguments. This feature lets you match partial strings in the microbatch data. See LIKE predicate for more information about using wildcards.

• The string comparisons for the --cluster, --microbatch, --source, --target-schema, and --target-table arguments are case-insensitive.

• The date and time values you supply to the --from-timestamp and --to-timestamp arguments use the java.sql.timestamp format for parsing the value. This format's parsing can accept values that you may consider invalid and would expect it to reject. For example, if you supply a timestamp of 01-01-2018 24:99:99, the Java timestamp parser silently converts the date to 2018-01-02 01:40:39 instead of returning an error.

Examples

This example gets the last microbatch that the scheduler defined in the weblog.conf file ran:

$ /opt/vertica/packages/kafka/bin/vkconfig statistics --last 1 --conf weblog.conf
{"microbatch":"weblog", "target_schema":"public", "target_table":"web_hits",
"source_name":"web_hits", "source_cluster":"kafka_weblog", "source_partition":0,
"start_offset":80000, "end_offset":79999, "end_reason":"END_OF_STREAM",
"end_reason_message":null, "partition_bytes":0, "partition_messages":0,
"timeslice":"00:00:09.793000", "batch_start":"2018-11-06 09:42:00.176747",
"batch_end":"2018-11-06 09:42:00.437787", "source_duration":"00:00:00.214314",
"consecutive_error_count":null, "transaction_id":45035996274513069,
"frame_start":"2018-11-06 09:41:59.949", "frame_end":null}

If your scheduler is reading from more than partition, the --last 1 option lists the last microbatch from each partition:

$ /opt/vertica/packages/kafka/bin/vkconfig statistics --last 1 --conf iot.conf
{"microbatch":"iotlog", "target_schema":"public", "target_table":"iot_data",
"source_name":"iot_data", "source_cluster":"kafka_iot", "source_partition":0,
"start_offset":-2, "end_offset":-2, "end_reason":"DEADLINE",
"end_reason_message":null, "partition_bytes":0, "partition_messages":0,
"timeslice":"00:00:09.842000", "batch_start":"2018-11-06 12:52:49.387567",
"batch_end":"2018-11-06 12:52:59.400219", "source_duration":"00:00:09.950127",
"consecutive_error_count":null, "transaction_id":45035996274537015,
"frame_start":"2018-11-06 12:52:49.213", "frame_end":null}
{"microbatch":"iotlog", "target_schema":"public", "target_table":"iot_data",
"source_name":"iot_data", "source_cluster":"kafka_iot", "source_partition":1,
"start_offset":1604, "end_offset":1653, "end_reason":"END_OF_STREAM",
"end_reason_message":null, "partition_bytes":4387, "partition_messages":50,
"timeslice":"00:00:09.842000", "batch_start":"2018-11-06 12:52:49.387567",
"batch_end":"2018-11-06 12:52:59.400219", "source_duration":"00:00:00.220329",
"consecutive_error_count":null, "transaction_id":45035996274537015,
"frame_start":"2018-11-06 12:52:49.213", "frame_end":null}
{"microbatch":"iotlog", "target_schema":"public", "target_table":"iot_data",
"source_name":"iot_data", "source_cluster":"kafka_iot", "source_partition":2,
"start_offset":1603, "end_offset":1652, "end_reason":"END_OF_STREAM",
"end_reason_message":null, "partition_bytes":4383, "partition_messages":50,
"timeslice":"00:00:09.842000", "batch_start":"2018-11-06 12:52:49.387567",
"batch_end":"2018-11-06 12:52:59.400219", "source_duration":"00:00:00.318997",
"consecutive_error_count":null, "transaction_id":45035996274537015,
"frame_start":"2018-11-06 12:52:49.213", "frame_end":null}
{"microbatch":"iotlog", "target_schema":"public", "target_table":"iot_data",
"source_name":"iot_data", "source_cluster":"kafka_iot", "source_partition":3,
"start_offset":1604, "end_offset":1653, "end_reason":"END_OF_STREAM",
"end_reason_message":null, "partition_bytes":4375, "partition_messages":50,
"timeslice":"00:00:09.842000", "batch_start":"2018-11-06 12:52:49.387567",
"batch_end":"2018-11-06 12:52:59.400219", "source_duration":"00:00:00.219543",
"consecutive_error_count":null, "transaction_id":45035996274537015,
"frame_start":"2018-11-06 12:52:49.213", "frame_end":null}

You can use the --partition argument to get just the partitions you want:

$ /opt/vertica/packages/kafka/bin/vkconfig statistics --last 1 --partition 2 --conf iot.conf
{"microbatch":"iotlog", "target_schema":"public", "target_table":"iot_data",
"source_name":"iot_data", "source_cluster":"kafka_iot", "source_partition":2,
"start_offset":1603, "end_offset":1652, "end_reason":"END_OF_STREAM",
"end_reason_message":null, "partition_bytes":4383, "partition_messages":50,
"timeslice":"00:00:09.842000", "batch_start":"2018-11-06 12:52:49.387567",
"batch_end":"2018-11-06 12:52:59.400219", "source_duration":"00:00:00.318997",
"consecutive_error_count":null, "transaction_id":45035996274537015,
"frame_start":"2018-11-06 12:52:49.213", "frame_end":null}

If your scheduler reads from more than one source, the --last 1 option outputs the last microbatch from each source:

$ /opt/vertica/packages/kafka/bin/vkconfig statistics --last 1 --conf weblog.conf
{"microbatch":"weberrors", "target_schema":"public", "target_table":"web_errors",
"source_name":"web_errors", "source_cluster":"kafka_weblog",
"source_partition":0, "start_offset":10000, "end_offset":9999,
"end_reason":"END_OF_STREAM", "end_reason_message":null,
"partition_bytes":0, "partition_messages":0, "timeslice":"00:00:04.909000",
"batch_start":"2018-11-06 10:58:02.632624",
"batch_end":"2018-11-06 10:58:03.058663", "source_duration":"00:00:00.220618",
"consecutive_error_count":null, "transaction_id":45035996274523991,
"frame_start":"2018-11-06 10:58:02.394", "frame_end":null}
{"microbatch":"weblog", "target_schema":"public", "target_table":"web_hits",
"source_name":"web_hits", "source_cluster":"kafka_weblog", "source_partition":0,
"start_offset":80000, "end_offset":79999, "end_reason":"END_OF_STREAM",
"end_reason_message":null, "partition_bytes":0, "partition_messages":0,
"timeslice":"00:00:09.128000", "batch_start":"2018-11-06 10:58:03.322852",
"batch_end":"2018-11-06 10:58:03.63047", "source_duration":"00:00:00.226493",
"consecutive_error_count":null, "transaction_id":45035996274524004,
"frame_start":"2018-11-06 10:58:02.394", "frame_end":null}

You can use wildcards to enable partial matches. This example demonstrates getting the last microbatch for all microbatches whose names end with "log":

~$ /opt/vertica/packages/kafka/bin/vkconfig statistics --microbatch "%log" \
                                            --last 1 --conf weblog.conf
{"microbatch":"weblog", "target_schema":"public", "target_table":"web_hits",
"source_name":"web_hits", "source_cluster":"kafka_weblog", "source_partition":0,
"start_offset":80000, "end_offset":79999, "end_reason":"END_OF_STREAM",
"end_reason_message":null, "partition_bytes":0, "partition_messages":0,
"timeslice":"00:00:04.874000", "batch_start":"2018-11-06 11:37:16.17198",
"batch_end":"2018-11-06 11:37:16.460844", "source_duration":"00:00:00.213129",
"consecutive_error_count":null, "transaction_id":45035996274529932,
"frame_start":"2018-11-06 11:37:15.877", "frame_end":null}

To get microbatches from a specific period of time, use the --from-timestamp and --to-timestamp arguments. This example gets the microbatches that read from partition #2 between 12:52:30 and 12:53:00 on 2018-11-06 for the scheduler defined in iot.conf.

$ /opt/vertica/packages/kafka/bin/vkconfig statistics  --partition 1 \
                        --from-timestamp "2018-11-06 12:52:30" \
                        --to-timestamp "2018-11-06 12:53:00" --conf iot.conf
{"microbatch":"iotlog", "target_schema":"public", "target_table":"iot_data",
"source_name":"iot_data", "source_cluster":"kafka_iot", "source_partition":1,
"start_offset":1604, "end_offset":1653, "end_reason":"END_OF_STREAM",
"end_reason_message":null, "partition_bytes":4387, "partition_messages":50,
"timeslice":"00:00:09.842000", "batch_start":"2018-11-06 12:52:49.387567",
"batch_end":"2018-11-06 12:52:59.400219", "source_duration":"00:00:00.220329",
"consecutive_error_count":null, "transaction_id":45035996274537015,
"frame_start":"2018-11-06 12:52:49.213", "frame_end":null}
{"microbatch":"iotlog", "target_schema":"public", "target_table":"iot_data",
"source_name":"iot_data", "source_cluster":"kafka_iot", "source_partition":1,
"start_offset":1554, "end_offset":1603, "end_reason":"END_OF_STREAM",
"end_reason_message":null, "partition_bytes":4371, "partition_messages":50,
"timeslice":"00:00:09.788000", "batch_start":"2018-11-06 12:52:38.930428",
"batch_end":"2018-11-06 12:52:48.932604", "source_duration":"00:00:00.231709",
"consecutive_error_count":null, "transaction_id":45035996274536981,
"frame_start":"2018-11-06 12:52:38.685", "frame_end":null}

This example demonstrates using the --dump argument to get the SQL statement vkconfig executed to retrieve the output from the previous example:

$ /opt/vertica/packages/kafka/bin/vkconfig statistics  --dump --partition 1 \
                       --from-timestamp "2018-11-06 12:52:30" \
                       --to-timestamp "2018-11-06 12:53:00" --conf iot.conf
SELECT microbatch, target_schema, target_table, source_name, source_cluster,
source_partition, start_offset, end_offset, end_reason, end_reason_message,
partition_bytes, partition_messages, timeslice, batch_start, batch_end,
last_batch_duration AS source_duration, consecutive_error_count, transaction_id,
frame_start, frame_end FROM "iot_sched".stream_microbatch_history WHERE
(source_partition = '1') AND (frame_start >= '2018-11-06 12:52:30.0') AND
(frame_start < '2018-11-06 12:53:00.0') ORDER BY frame_start DESC, microbatch,
source_cluster, source_name, source_partition;