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redshift_views.view.lkml
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redshift_views.view.lkml
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view: redshift_db_space {
derived_table: {
sql: select name as table
, trim(pgn.nspname) as schema
, sum(b.mbytes) as megabytes
, sum(a.rows) as rows
from (select db_id
, id
, name
, sum(rows) as rows
from stv_tbl_perm a
group by 1,2,3) as a
join pg_class as pgc
on pgc.oid = a.id
join pg_namespace as pgn
on pgn.oid = pgc.relnamespace
join pg_database as pgdb
on pgdb.oid = a.db_id
join (select tbl
, count(*) as mbytes
from stv_blocklist
group by 1) as b
on a.id = b.tbl
group by 1,2
;;
}
dimension: table {
type: string
sql: ${TABLE}.table ;;
}
dimension: schema {
type: string
sql: ${TABLE}.schema ;;
}
dimension: megabytes {
type: number
sql: ${TABLE}.megabytes ;;
}
dimension: rows {
type: number
sql: ${TABLE}.rows ;;
}
dimension: table_stem {
sql: case
when (${table} ~ '(lr|lc)\\$[a-zA-Z0-9]+_.*')
then ltrim(regexp_substr(${table}, '_.*'), '_') || ' - Looker PDT'
else ${table}
end
;;
}
measure: total_megabytes {
type: sum
sql: ${megabytes} ;;
}
measure: total_rows {
type: sum
sql: ${rows} ;;
}
measure: total_tables {
type: count_distinct
sql: ${table} ;;
}
}
view: redshift_etl_errors {
derived_table: {
sql: select starttime as error_time
, filename as file_name
, colname as column_name
, type as column_data_type
, position as error_position
, raw_field_value as error_field_value
, err_reason as error_reason
, raw_line
from stl_load_errors
;;
}
dimension_group: error {
type: time
timeframes: [time, date]
sql: ${TABLE}.error_time ;;
}
dimension: file_name {
type: string
sql: ${TABLE}.file_name ;;
}
dimension: column_name {
type: string
sql: ${TABLE}.column_name ;;
}
dimension: column_data_type {
type: string
sql: ${TABLE}.column_data_type ;;
}
dimension: error_position {
type: string
sql: ${TABLE}.error_position ;;
}
dimension: error_field_value {
type: string
sql: ${TABLE}.error_field_value ;;
}
dimension: error_reason {
type: string
sql: ${TABLE}.error_reason ;;
}
dimension: raw_line {
type: string
sql: ${TABLE}.raw_line ;;
}
}
view: redshift_data_loads {
derived_table: {
sql: select replace(regexp_substr(filename, '//[a-zA-Z0-9\-]+/'), '/', '') as root_bucket
, replace(filename, split_part(filename, '/', regexp_count(filename, '/') + 1), '') as s3_path
, regexp_replace(replace(filename, split_part(filename, '/', regexp_count(filename, '/') + 1), ''), '([\\d]{5,}|[\\d\-]{5,}/)', '') as s3_path_clean
, split_part(filename, '/', regexp_count(filename, '/') + 1) as file_name
, regexp_replace(split_part(filename, '/', regexp_count(filename, '/') + 1), '([\\d]{7,}|[\\d\-]{7,})', '') as file_stem
, curtime as load_time
from stl_load_commits
;;
}
dimension: root_bucket {
type: string
sql: ${TABLE}.root_bucket ;;
}
dimension: s3_path {
type: string
sql: ${TABLE}.s3_path ;;
}
dimension: s3_path_clean {
type: string
sql: ${TABLE}.s3_path_clean ;;
}
dimension: file_name {
type: string
sql: ${TABLE}.file_name ;;
}
dimension: file_stem {
type: string
sql: ${TABLE}.file_stem ;;
}
dimension_group: load {
type: time
timeframes: [raw, time, date]
sql: ${TABLE}.load_time ;;
}
measure: most_recent_load {
type: string
sql: max(${load_raw}) ;;
}
measure: hours_since_last_load {
type: number
value_format_name: id
sql: datediff('hour', ${most_recent_load}, getdate()) ;;
html: {% if value < 24 %}
<div style="color:#B40404; background-color:#22CE7E; font-size:100%; text-align:center">{{ rendered_value }}</div>
{% elsif value >= 24 and value < 48 %}
<div style="color:#868A08; background-color:#95F047; font-size:100%; text-align:center">{{ rendered_value }}</div>
{% elsif value >= 48 %}
<div style="color:#868A08; background-color:#C64646; font-size:100%; text-align:center">{{ rendered_value }}</div>
{% endif %}
;;
}
}
view: redshift_plan_steps {
#description: "Steps from the query planner for recent queries to Redshift"
derived_table: {
# Insert into PDT because redshift won't allow joining certain system tables/views onto others (presumably because they are located only on the leader node)
datagroup_trigger: nightly
distribution: "query"
sortkeys: ["query"]
sql:
WITH redshift_plan_steps AS
(SELECT
query, nodeid, parentid,
CASE WHEN plannode='SubPlan' THEN 'SubPlan'
ELSE substring(regexp_substr(plannode, 'XN( [A-Z][a-z]+)+'),4) END as operation,
substring(regexp_substr(plannode, 'DS_[A-Z_]+'),0) as network_distribution_type,
REGEXP_REPLACE(REGEXP_REPLACE(
substring(info from 1 for 240)
, '\'(\\\\\.|[^\'\\\\\])+(\'|$|\\\\\$)', '[Redacted String]'), '[0-9][0-9]+', '[Redacted Int]')
as operation_argument,
CASE
WHEN plannode NOT LIKE '% on %' THEN NULL
WHEN plannode LIKE '% on "%' THEN substring(regexp_substr(plannode,' on "[^"]+'),6)
ELSE substring(regexp_substr(plannode,' on [\._a-zA-Z0-9]+'),5)
END as "table",
RIGHT('0'||COALESCE(substring(regexp_substr(plannode,' rows=[0-9]+'),7),''),32)::decimal(38,0) as "rows",
RIGHT('0'||COALESCE(substring(regexp_substr(plannode,' width=[0-9]+'),8),''),32)::decimal(38,0) as width,
substring(regexp_substr(plannode,'\\(cost=[0-9]+'),7) as cost_lo_raw,
substring(regexp_substr(plannode,'\\.\\.[0-9]+'),3) as cost_hi_raw,
CASE
WHEN cost_hi_raw != '' THEN
( CASE WHEN LEN(cost_hi_raw) > 18 THEN 999999999999999999::DECIMAL(38,0) ELSE cost_hi_raw::DECIMAL(38,0) END )
ELSE NULL END as cost_hi_numeric,
CASE
WHEN COALESCE(parentid,0)=0 THEN 'root'
WHEN nodeid = MAX(nodeid) OVER (PARTITION BY query,parentid) THEN 'inner'
ELSE 'outer' END::CHAR(5) as inner_outer,
SUM(cost_hi_numeric) OVER (PARTITION by query,parentid) as sum_children_cost
FROM stl_explain
WHERE query>=(SELECT min(query) FROM ${redshift_queries.SQL_TABLE_NAME})
AND query<=(SELECT max(query) FROM ${redshift_queries.SQL_TABLE_NAME})
GROUP BY 1,2,3,4,5,6,7,8,9,10,11,12)
SELECT
redshift_plan_steps.*,
redshift_plan_steps.cost_hi_numeric - COALESCE(x.sum_children_cost,0) AS incremental_step_cost
FROM redshift_plan_steps
LEFT JOIN (SELECT query, parentid, sum_children_cost FROM redshift_plan_steps GROUP BY 1,2,3) AS x
ON redshift_plan_steps.query = x.query AND redshift_plan_steps.nodeid = x.parentid
ORDER BY 1,3
;;
#TODO?: Currently not extracting the sequential scan column, but I'm not sure if this is useful to extract. What's more useful as far as I can tell are the fields in the filter (operation argument)
}
dimension: query {
sql: ${TABLE}.query;;
type: number
value_format_name: id
drill_fields: [redshift_plan_steps.step]
}
dimension: step {
sql: ${TABLE}.nodeid ;;
type: number
value_format_name: id
}
dimension: query_step {
sql: ${query}||'.'||${step} ;;
#primary_key: yes #Unfortunately not, because all CTE plans are labeled as step 0
hidden: yes
}
dimension: parent_step {
type: number
sql: ${TABLE}.parentid;;
hidden: yes
}
dimension: step_description {
description: "Concatenation of 'operation - network distribution type - table'"
sql: CASE WHEN COALESCE(${operation},'') = '' THEN '' ELSE ${operation} END ||
CASE WHEN COALESCE(${operation_argument},'') = '' THEN '' ELSE ' - ' || ${operation_argument} END ||
CASE WHEN COALESCE(${network_distribution_type},'') = '' THEN '' ELSE ' - ' || ${network_distribution_type} END ||
CASE WHEN COALESCE(${table},'') = '' THEN '' ELSE ' - ' || ${table} END ;;
type: "string"
hidden: yes
}
dimension: operation {
label: "Operation"
sql: ${TABLE}.operation ;;
type: "string"
html:
{% if value contains 'Nested' %}
<span style="color: darkred">{{ rendered_value }}</span>
{% else %}
{{ rendered_value }}
{% endif %}
;;
}
dimension: operation_join_algorithm {
type: "string"
sql: CASE WHEN ${operation} ILIKE '%Join%'
THEN regexp_substr(${operation},'^[A-Za-z]+')
ELSE 'Not a Join' END
;;
html:
{% if value == 'Nested' %}
<span style="color: darkred">{{ rendered_value }}</span>
{% else %}
{{ rendered_value }}
{% endif %}
;;
}
dimension: network_distribution_type {
label: "Network Redistribution"
description: "AWS Docs http://docs.aws.amazon.com/redshift/latest/dg/c_data_redistribution.html"
sql: ${TABLE}.network_distribution_type ;;
type: "string"
html: <span style="color: {% if
value == 'DS_DIST_NONE' %} #37ce12 {% elsif
value == 'DS_DIST_ALL_NONE' %} #17470c {% elsif
value == 'DS_DIST_INNER' %} #5f7c58 {% elsif
value == 'DS_DIST_OUTER' %} #ff8828 {% elsif
value == 'DS_DIST_BOTH' %} #c13c07 {% elsif
value == 'DS_BCAST_INNER' %} #d6a400 {% elsif
value == 'DS_DIST_ALL_INNER' %} #9e0f62 {% else
%} black {% endif %}">{{ rendered_value }}</span>
;;
}
dimension: network_distribution_bytes {
#TODO: Multiply by number of nodes if BCAST?
description: "Bytes from inner and outer children needing to be distributed or broadcast. (For broadcast, this value does not multiply by the number of nodes broadcast to.)"
sql: CASE
WHEN ${network_distribution_type} ILIKE '%INNER%' THEN ${inner_child.bytes}
WHEN ${network_distribution_type} ILIKE '%OUTER%' THEN ${outer_child.bytes}
WHEN ${network_distribution_type} ILIKE '%BOTH%' THEN ${inner_child.bytes} + ${outer_child.bytes}
ELSE 0
END ;;
}
dimension: table {
sql: ${TABLE}."table" ;;
type: "string"
}
dimension: operation_argument {
label: "Operation argument"
sql: ${TABLE}.operation_argument ;;
type: "string"
}
dimension: rows {
label: "Rows out"
sql: ${TABLE}.rows;;
description: "Number of rows returned from this step"
type: "number"
}
dimension: width {
label: "Width out"
sql: ${TABLE}.width;;
description: "The estimated width of the average row, in bytes, that is returned from this step"
type: "number"
}
dimension:bytes{
label: "Bytes out"
description: "Estimated bytes out from this step (rows * width)"
sql: ${rows} * ${width} ;;
type: "number"
}
dimension: inner_outer {
label: "Child Inner/Outer"
description: "If the step is a child of another step, whether it is the inner or outer child of the parent, e.g. for network redistribution in joins"
type: "string"
sql: ${TABLE}.inner_outer ;;
}
dimension: cost_lo_raw {
description: "Cumulative relative cost of returning the first row for this step"
type: string
sql: ${TABLE}.cost_lo_raw ;;
hidden: yes
}
dimension: cost_hi_raw {
description: "Cumulative relative cost of completing this step"
type: string
sql: ${TABLE}.cost_hi_raw ;;
hidden: yes
}
dimension: incremental_step_cost {
description: "Incremental relative cost of completing this step"
type: number
sql: ${TABLE}.incremental_step_cost ;;
}
measure: count {
type: count
drill_fields: [query, parent_step, step, operation, operation_argument, network_distribution_type]
}
measure: step_cost {
type: sum
sql: ${incremental_step_cost} ;;
description: "Relative cost of completing steps"
}
measure: total_rows{
label: "Total rows out"
type: "sum"
sql: ${rows} ;;
description: "Sum of rows returned across steps"
}
measure: total_bytes {
label: "Total bytes out"
type: "sum"
sql: ${bytes} ;;
}
measure: total_network_distribution_bytes {
type: sum
sql: ${network_distribution_bytes} ;;
}
set: steps_drill {
fields: [
redshift_plan_steps.query,
redshift_plan_steps.parent_step,
redshift_plan_steps.step,
redshift_plan_steps.operation,
redshift_plan_steps.operation_argument,
redshift_plan_steps.network_distribution_type,
redshift_plan_steps.rows,
redshift_plan_steps.width,
redshift_plan_steps.bytes
]
}
}
view: redshift_queries {
# Limited to last 24 hours of queries
derived_table: {
datagroup_trigger: nightly
distribution: "query"
sortkeys: ["query"]
sql: SELECT
wlm.query,
-- To expose unredacted query text, use:
-- COALESCE(qlong.querytxt,q.substring)::varchar as text
-- instead of the below
REGEXP_REPLACE(REGEXP_REPLACE(
COALESCE(qlong.querytxt,q.substring)::varchar
, '\'(\\\\\.|[^\'\\\\\])+(\'|$|\\\\\$)', '[Redacted String]'), '[0-9][0-9]+', '[Redacted Int]')
as text,
REGEXP_REPLACE(REGEXP_REPLACE(
SUBSTRING(
REGEXP_REPLACE(COALESCE(qlong.querytxt,q.substring)::varchar,'^\\s*-- ([A-Za-z ]*''\\{[^}]*\\}''.|Building [^ ]+( in dev mode)? on instance [0-9a-f]+.)','')
,1,100)
, '\'(\\\\\.|[^\'\\\\\])+(\'|$|\\\\\$)', '[Redacted String]'), '[0-9][0-9]+', '[Redacted Int]')
as snippet,
CASE
WHEN COALESCE(qlong.querytxt,q.substring)::varchar NOT ILIKE '-- Building %'
THEN 'No'
WHEN COALESCE(qlong.querytxt,q.substring)::varchar ILIKE '% in dev mode on instance %'
THEN 'Dev'
ELSE 'Prod'
END as pdt,
REGEXP_SUBSTR(COALESCE(qlong.querytxt,q.substring)::varchar,'^\\s*-- [A-Za-z ]*''\\{[^}]*"user_id":(\\d+)',1,1,'e') as looker_user_id,
REGEXP_SUBSTR(COALESCE(qlong.querytxt,q.substring)::varchar,'^\\s*-- [A-Za-z ]*''\\{[^}]*"history_id":(\\d+)',1,1,'e') as looker_history_id,
REGEXP_SUBSTR(COALESCE(qlong.querytxt,q.substring)::varchar,'^\\s*-- [^}]*instance_?s?l?u?g?"?:?"? ?([0-9a-f]+)',1,1,'e') as looker_instance_slug,
sc.name as service_class,
--wlm.service_class as service_class, --Use if connection was not given access to STV_WLM_SERVICE_CLASS_CONFIG
wlm.service_class_start_time as start_time,
wlm.total_queue_time,
wlm.total_exec_time,
q.elapsed --Hmm.. this measure seems to be greater than queue_time+exec_time
FROM STL_WLM_QUERY wlm
LEFT JOIN STV_WLM_SERVICE_CLASS_CONFIG sc ON sc.service_class=wlm.service_class -- Remove this line if access was not granted
LEFT JOIN SVL_QLOG q on q.query=wlm.query
LEFT JOIN STL_QUERY qlong on qlong.query=q.query
WHERE wlm.service_class_start_time >= dateadd(day,-1,GETDATE())
AND wlm.service_class_start_time <= GETDATE()
;;
#STL_QUERY vs SVL_QLOG. STL_QUERY has more characters of query text (4000), but is only retained for "2 to 5 days"
# STL_WLM_QUERY or SVL_QUERY_QUEUE_INFO? http://docs.aws.amazon.com/redshift/latest/dg/r_SVL_QUERY_QUEUE_INFO.html
}
#Looker Query Context '{"user_id":711,"history_id":38026310,"instance_slug":"186fb89f0c23199fffd36f1cdfb6152b"}
dimension: query {
description: "Redshift's Query ID"
type: number
primary_key: yes
link: {
label: "Inspect"
url: "/dashboards/redshift_model::redshift_query_inspection?query={{value}}"
}
}
dimension: text {
alias: [querytxt]
}
dimension: snippet {
alias: [substring]
}
dimension: looker_user_id {
group_label: "Looker Query Context"
type: number
link: {
label: "View in Looker Admin"
url: "/admin/users/{{value}}/edit"
# ^ Note that in scenarios with multiple Looker instances, this may not be the right link!
}
}
dimension: looker_history_id {
group_label: "Looker Query Context"
type: number
link: {
label: "View in Looker Admin"
url: "/admin/queries/{{value}}"
# ^ Note that in scenarios with multiple Looker instances, this may not be the right link!
}
}
dimension: looker_instance_slug {
group_label: "Looker Query Context"
}
dimension: pdt {
label: "Is PDT?"
group_label: "Looker Query Context"
description: "Either Prod, Dev, or No"
}
dimension_group: start {
type: time
timeframes: [raw, minute,second, minute15, hour, hour_of_day, day_of_week, date]
sql: ${TABLE}.start_time ;;
}
dimension: service_class {
type: string
sql: ${TABLE}.service_class ;;
}
dimension: time_in_queue {
type: number
description: "Amount of time that a query was queued before running, in seconds"
sql: ${TABLE}.total_queue_time /1000000;;
}
dimension: time_executing {
type: number
description: "Amount of time that a query was executing, in seconds"
sql: ${TABLE}.total_exec_time::float /1000000;;
}
dimension: time_executing_roundup1 {
description: "Time executing, rounded up to the nearest 1 second"
group_label: "Time Executing Buckets"
label: "01 second"
type: number
sql: CEILING(${TABLE}.total_exec_time::float/1000000) ;;
value_format_name: decimal_0
}
dimension: time_executing_roundup5 {
description: "Time executing, rounded up to the nearest 5 seconds"
group_label: "Time Executing Buckets"
label: "05 seconds"
type: number
sql: CEILING(${TABLE}.total_exec_time::float/1000000 / 5)*5 ;;
value_format_name: decimal_0
}
dimension: time_executing_roundup10 {
description: "Time executing, rounded up to the nearest 10 seconds"
group_label: "Time Executing Buckets"
label: "10 seconds"
type: number
sql: CEILING(${TABLE}.total_exec_time::float/1000000 / 10)*10 ;;
value_format_name: decimal_0
}
dimension: time_executing_roundup15 {
description: "Time executing, rounded up to the nearest 15 seconds"
group_label: "Time Executing Buckets"
label: "15 seconds"
type: number
sql: ${TABLE}.total_exec_time::float/1000000 / 15)*15 ;;
value_format_name: decimal_0
}
dimension: time_overall {
type: number
description: "Amount of time that a query took (both queued and executing), in seconds"
sql: ${time_in_queue} + ${time_executing} ;;
}
dimension: time_elapsed {
type: number
description: "Amount of time (from another table, for comparison...)"
sql: ${TABLE}.elapsed / 1000000 ;;
}
dimension: was_queued {
type: yesno
sql: ${TABLE}.total_queue_time > 0;;
}
measure: count {
type: count
drill_fields: [query, start_date, time_executing, pdt, looker_history_id, snippet ]
}
measure: count_of_queued {
type: sum
sql: ${was_queued}::int ;;
}
measure: percent_queued {
type: number
value_format: "0.## \%"
sql: 100 * ${count_of_queued} / ${count} ;;
}
measure: total_time_in_queue {
type: sum
description: "Sum of time that queries were queued before running, in seconds"
sql: ${time_in_queue};;
}
measure: total_time_executing {
type: sum
description: "Sum of time that queries were executing, in seconds"
sql: ${time_executing};;
}
measure: total_time_overall {
type: sum
description: "Sum of time that queries took (both queued and executing), in seconds"
sql: ${time_in_queue} + ${time_executing} ;;
}
# measure: total_time_elapsed {
# type: sum
# description: "Sum of time from another table, for comparison"
# sql: ${time_elapsed} ;;
# }
measure: time_executing_per_query {
type: number
sql: CASE WHEN ${count}<>0 THEN ${total_time_executing} / ${count} ELSE NULL END ;;
value_format_name: decimal_1
}
}
view: redshift_slices {
# http://docs.aws.amazon.com/redshift/latest/dg/r_STV_SLICES.html
# Use the STV_SLICES table to view the current mapping of a slice to a node.
# This table is visible to all users. Superusers can see all rows; regular users can see only their own data.
derived_table: {
datagroup_trigger: nightly
distribution_style: "all"
sortkeys: ["node"]
sql: SELECT slice,node FROM STV_SLICES;;
}
dimension: node{
type: number
value_format_name: id
sql: ${TABLE}.node ;;
}
dimension: slice {
type: number
value_format_name: id
sql: ${TABLE}.slice ;;
}
measure: nodes {
type: count_distinct
sql: ${node} ;;
}
measure: slices {
type: count_distinct
sql: ${slice} ;;
}
}
view: redshift_tables {
derived_table: {
# Insert into PDT because Redshift won't allow joining certain system tables/views onto others (presumably because they are located only on the leader node)
datagroup_trigger: nightly
distribution_style: all
indexes: ["table_id","table"] # "indexes" translates to an interleaved sort key for Redshift
# http://docs.aws.amazon.com/redshift/latest/dg/r_SVV_TABLE_INFO.html
sql: select
"database"::varchar,
"schema"::varchar,
"Table_id"::bigint,
"table"::varchar,
"encoded"::varchar,
"diststyle"::varchar,
REGEXP_REPLACE("sortkey1", '[^ -~]', '?')::varchar as "sortkey1",
"max_varchar"::bigint,
"sortkey1_enc"::varchar,
"sortkey_num"::int,
"size"::bigint,
"pct_used"::numeric,
"unsorted"::numeric,
"stats_off"::numeric,
"tbl_rows"::bigint,
"skew_sortkey1"::numeric,
"skew_rows"::numeric
from svv_table_info
;;
}
# dimensions #
# Identifiers {
dimension: table_id {
group_label: " Identifiers"
description: "The table's internal/numeric ID"
type: number
sql: ${TABLE}.table_id ;;
}
dimension: path {
group_label: " Identifiers"
description: "Database + schema + table name"
sql: ${database}||'.'||${schema}||'.'||${table} ;;
primary_key: yes
}
dimension: database {
group_label: " Identifiers"
type: string
sql: ${TABLE}.database ;;
}
dimension: schema {
group_label: " Identifiers"
type: string
sql: ${TABLE}.schema ;;
}
dimension: table {
group_label: " Identifiers"
label: "Table Name"
description: "Local table name"
type: string
sql: ${TABLE}."table" ;;
}
dimension: table_join_key {
group_label: " Identifiers"
hidden:yes
type:string
sql: CASE WHEN ${schema}='looker_scratch'
THEN 'name:'||${table}
ELSE 'id:'||${table_id}
END ;;
#Because when PDTs get rebuilt, their ID changes, and showing the info about the current PDT is more useful than showing nothing
}
#}
# Size (Table) {
dimension: rows_in_table {
group_label: "Size (Table)"
type: number
sql: ${TABLE}.tbl_rows ;;
}
dimension: size {
group_label: "Size (Table)"
description: "Size of the table, in 1 MB data blocks"
type: number
sql: ${TABLE}.size ;;
}
dimension: pct_used {
group_label: "Size (Table)"
label: "Percentage used"
type: number
description: "Percent of available space that is used by the table"
sql: ${TABLE}.pct_used ;;
}
#}
# Size (Columns) {
dimension: encoded {
group_label: "Size (Columns)"
description: "Whether any column has compression encoding defined"
type: yesno
sql: case ${TABLE}.encoded
when 'Y'
then true
when 'N'
then false
else null
end
;;
}
dimension: max_varchar {
group_label: "Size (Columns)"
description: "Size of the largest column that uses a VARCHAR data type"
type: number
sql: ${TABLE}.max_varchar ;;
}
#}
# Distribution {
dimension: distribution_style {
group_label: "Distribution"
type: string
sql: ${TABLE}.diststyle ;;
html:
{% if value == 'EVEN' %}
<span style="color: darkorange">{{ rendered_value }}</span>
{% elsif value == 'ALL' or value == 'DS_DIST_NONE'%}
<span style="color: dimgray">{{ rendered_value }}</span>
{% else %}
{{ rendered_value }}
{% endif %}
;;
}
dimension: skew_rows {
group_label: "Distribution"
description: "Ratio of the number of rows in the slice with the most rows to the number of rows in the slice with the fewest rows"
type: number
sql: ${TABLE}.skew_rows ;;
html:
{% if value >= 75 %}
<span style="color:darkred">{{ rendered_value }}</span>
{% elsif value >= 25 %}
<span style="color:darkorange">{{ rendered_value }}</span>
{% else %}
{{ rendered_value }}
{% endif %}
;;
}
#}
# Sorting {
dimension: sortkey {
group_label: "Sorting"
description: "First sort key"
type: string
sql: ${TABLE}.sortkey1 ;;
}
dimension: sortkey_encoding {
group_label: "Sorting"
description: "Compression encoding of the first column in the sort key, if a sort key is defined"
type: string
sql: ${TABLE}.sortkey1_enc ;;
}
dimension: number_of_sortkeys {
group_label: "Sorting"
type: number
sql: ${TABLE}.sortkey_num ;;
}
dimension: unsorted {
group_label: "Sorting"
description: "Percent of unsorted rows in the table"
type: number
sql: ${TABLE}.unsorted ;;
html:
{% if value >= 50 %}
<span style="color: darkred">{{ rendered_value }}</span>
{% elsif value >= 10 %}
<span style="color: darkorange">{{ rendered_value }}</span>
{% elsif value < 10 %}
<span style="color: green">{{ rendered_value }}</span>
{% else %}
{{ rendered_value }}
{% endif %}
;;
}
dimension: skew_sortkey {
group_label: "Sorting"
description: "Ratio of the size of the largest non-sort key column to the size of the first column of the sort key, if a sort key is defined. Use this value to evaluate the effectiveness of the sort key"
type: number
sql: ${TABLE}.skew_sortkey1 ;;
}
#}
dimension: stats_off {
description: "Number that indicates how stale the table's statistics are; 0 is current, 100 is out of date"
type: number
sql: ${TABLE}.stats_off ;;
html:
{% if value >= 50 %}
<span style="color: darkred">{{ rendered_value }}</span>
{% elsif value >= 10 %}
<span style="color: darkorange">{{ rendered_value }}</span>
{% elsif value < 10 %}
<span style="color: green">{{ rendered_value }}</span>
{% else %}
{{ rendered_value }}
{% endif %}
;;
}
measure: count {
type: count
}
measure: total_rows {
type: sum
sql: ${rows_in_table};;
}
measure: total_size {
description: "Size of the table(s), in 1 MB data blocks"
type: sum
sql: ${size} ;;
}
}
view: redshift_query_execution {
#For recent queries based on redshift_queries
#description: "Steps from the query planner for recent queries to Redshift"
derived_table: {
# Insert into PDT because redshift won't allow joining certain system tables/views onto others (presumably because they are located only on the leader node)
datagroup_trigger: nightly
distribution: "query"
sortkeys: ["query"]
sql:
SELECT
query ||'.'|| seg || '.' || step as id,
query, seg, step,
label::varchar,
regexp_substr(label, '^[A-Za-z]+')::varchar as operation,
CASE WHEN label ilike 'scan%name=%' AND label not ilike '%Internal Worktable'
THEN substring(regexp_substr(label, 'name=(.+)$'),6)
ELSE NULL
END::varchar as "table",
CASE WHEN label ilike 'scan%tbl=%'
THEN ('0'+COALESCE(substring(regexp_substr(label, 'tbl=([0-9]+)'),5),''))::int
ELSE NULL
END as "table_id",
CASE WHEN label ilike 'scan%tbl=%'
THEN CASE WHEN label ilike '%name=%LR$%'
THEN 'name:'||substring(regexp_substr(label, 'name=(.+)$'),6)
ELSE 'id:'||COALESCE(substring(regexp_substr(label, 'tbl=([0-9]+)'),5),'')
END
ELSE NULL
END::varchar
as "table_join_key",
MAX(is_diskbased) as is_diskbased,
MAX(is_rrscan) as is_rrscan,
AVG(avgtime) as avgtime,
MAX(maxtime) as maxtime,
SUM(workmem) as workmem,
SUM(rows_pre_filter) rows_pre_filter,
SUM(bytes) as bytes
FROM svl_query_summary
WHERE query>=(SELECT min(query) FROM ${redshift_queries.SQL_TABLE_NAME})
AND query<=(SELECT max(query) FROM ${redshift_queries.SQL_TABLE_NAME})
GROUP BY query, seg, step, label
;;
}
# or svl_query_report to not aggregate over slices under each step
#using group by because sometimes steps are duplicated.seems to be when some slices are diskbased, others not
dimension: step {
type: string
sql: ${TABLE}.seg || '.' || ${TABLE}.step;;
value_format_name: decimal_2
order_by_field: step_sort
}
dimension: step_sort {
hidden: yes
type: number
sql: ${TABLE}.seg*10000 + ${TABLE}.step;;
}
dimension: query {
type: number
sql: ${TABLE}.query ;;
}
dimension: id {
primary_key: yes
type: string
sql: ${TABLE}.id;;
}
dimension: label {
type: string
sql: ${TABLE}.label ;;
}
dimension: operation {
type: string
sql: ${TABLE}.operation ;;
}
dimension: table {
type: string
sql: ${TABLE}.table ;;
}
dimension: table_id {
type: number
sql: ${TABLE}.table_id ;;
}
dimension: table_join_key {
hidden: yes
type: string
sql: ${TABLE}.table_join_key;;
}
dimension: was_diskbased {
type: string
label: "Was disk-based?"
description: "Whether this step of the query was executed as a disk-based operation on any slice in the cluster"
sql: CASE WHEN ${TABLE}.is_diskbased='t' THEN 'Yes' ELSE 'No' END;;
html:
{% if value == 'Yes' %}
<span style="color: darkred">{{ rendered_value }}</span>
{% else %}
{{ rendered_value }}
{% endif %}
;;
}
dimension: was_restricted_scan {
type: yesno
label: "Was the scan range-restricted?"
description: "Whether this step of the query was executed as a disk-based operation on any slice in the cluster"
sql: CASE WHEN ${TABLE}.is_rrscan='t' THEN 'Yes' WHEN ${operation} = 'scan' THEN 'No' ELSE 'N/A' END;;
html:
{% if value == 'Yes' %}
<span style="color: green">{{ rendered_value }}</span>
{% else %}
{{ rendered_value }}
{% endif %}
;;
}
dimension: step_average_slice_time {
type: number
description: "Average time among slices, in seconds, for this step"
sql: ${TABLE}.avgtime/1000000 ;;
}
dimension: step_max_slice_time {
type: number
description: "Maximum time among slices, in seconds, for this step"
sql: ${TABLE}.maxtime/1000000 ;;
}
dimension: step_skew {
type: number
description: "The ratio of max execution time vs avg execution time for this step among participating slices. (For information on how many slices participated in this step, check svl_query_report)"
sql: CASE WHEN ${TABLE}.avgtime=0 THEN NULL ELSE ${TABLE}.maxtime / ${TABLE}.avgtime END ;;
html:
{% if value > 16 %}
<span style="color: darkred">{{ rendered_value }}</span>
{% elsif value >4 %}
<span style="color: darkorange">{{ rendered_value }}</span>
{% else %}
{{ rendered_value }}
{% endif %}
;;
}
dimension: working_memory {
type: number
description: "Amount of working memory (in bytes) assigned to the query step"
sql: ${TABLE}.workmem ;;
}
dimension: rows_out {
type: number
description: "For scans of permanent tables, the total number of rows emitted (before filtering rows marked for deletion, a.k.a ghost rows). If very different from Query Plan rows, stats should be updated"
sql: ${TABLE}.rows_pre_filter ;;
}
dimension: bytes {
type: number
sql: ${TABLE}.bytes ;;
}
measure: count {
hidden: yes
type: count
}
measure: any_disk_based {
type: string
sql: MAX(${was_diskbased}) ;;
html:
{% if value == 'Yes' %}
<span style="color: darkred; font-weight:bold">{{ rendered_value }}</span>
{% elsif value == 'No' %}
<span style="color: green">{{ rendered_value }}</span>