Getting started with Query Profiler¶

This starter notebook will help you get up and running with the Query Profiler (QueryProfiler) tool in VerticaPyLab and demonstrate functionality through various examples and use cases.

This tool is a work in progress and the VerticaPy team is continuously adding new features.

VerticaPyLab¶

The easiest way to use the QueryProfiler tool is through VerticaPyLab. For installation instructions, see Getting Started .

Before using the QueryProfiler tool, confirm that you are connected to a Vertica database. If not, follow the connection instructions in a Jupyter notebook or connect using the Connect option on the VerticaPyLab homepage.

QueryProfiler¶

The QueryProfiler object is a python object that includes many built-in methods for analyzing queries and their performance. There are a few different ways to create a QueryProfiler object.

Create and save a QueryProfiler object¶

First, import the verticapy package and load the datasets:

import verticapy as vp

from verticapy.datasets import load_titanic, load_amazon

# load datasets
titanic = load_titanic()

amazon = load_amazon()

Create `QueryProfiler` object from transaction id and statement id¶

Before creating the QueryProfiler object, take a look at the data used by the query:

amazon.head(100)

	📅 date Date 100%	...	Abc state Varchar(32) 100%	123 number Int 100%
1	1998-01-01	...	AMAPÁ	0
2	1998-01-01	...	AMAZONAS	0
3	1998-01-01	...	DISTRITO FEDERAL	0
4	1998-01-01	...	ESPÍRITO SANTO	0
5	1998-01-01	...	MARANHÃO	0
6	1998-01-01	...	PARANÁ	0
7	1998-01-01	...	PIAUÍ	0
8	1998-01-01	...	RORAIMA	0
9	1998-01-01	...	SERGIPE	0
10	1998-01-01	...	SÃO PAULO	0
11	1998-02-01	...	GOIÁS	0
12	1998-02-01	...	MATO GROSSO DO SUL	0
13	1998-02-01	...	MINAS GERAIS	0
14	1998-02-01	...	PARAÍBA	0
15	1998-02-01	...	SANTA CATARINA	0
16	1998-02-01	...	SÃO PAULO	0
17	1998-03-01	...	AMAPÁ	0
18	1998-03-01	...	BAHIA	0
19	1998-03-01	...	MATO GROSSO DO SUL	0
20	1998-03-01	...	PARÁ	0
21	1998-03-01	...	PERNAMBUCO	0
22	1998-03-01	...	RIO GRANDE DO SUL	0
23	1998-04-01	...	CEARÁ	0
24	1998-04-01	...	PARANÁ	0
25	1998-04-01	...	PARAÍBA	0
26	1998-04-01	...	PARÁ	0
27	1998-05-01	...	ALAGOAS	0
28	1998-05-01	...	AMAPÁ	0
29	1998-05-01	...	MARANHÃO	0
30	1998-05-01	...	MATO GROSSO DO SUL	0
31	1998-05-01	...	RIO GRANDE DO SUL	0
32	1998-05-01	...	TOCANTINS	0
33	1998-06-01	...	ESPÍRITO SANTO	6
34	1998-06-01	...	RIO DE JANEIRO	3
35	1998-06-01	...	RIO GRANDE DO NORTE	1
36	1998-06-01	...	SÃO PAULO	451
37	1998-07-01	...	ESPÍRITO SANTO	37
38	1998-07-01	...	MARANHÃO	274
39	1998-07-01	...	MATO GROSSO	360
40	1998-07-01	...	MATO GROSSO DO SUL	3712
41	1998-07-01	...	PARAÍBA	0
42	1998-07-01	...	PARÁ	638
43	1998-07-01	...	RONDÔNIA	365
44	1998-07-01	...	SÃO PAULO	596
45	1998-08-01	...	ALAGOAS	1
46	1998-08-01	...	BAHIA	815
47	1998-08-01	...	DISTRITO FEDERAL	48
48	1998-08-01	...	ESPÍRITO SANTO	38
49	1998-08-01	...	MARANHÃO	1176
50	1998-08-01	...	MATO GROSSO	228
51	1998-08-01	...	MINAS GERAIS	875
52	1998-08-01	...	PIAUÍ	711
53	1998-08-01	...	RIO GRANDE DO SUL	9
54	1998-08-01	...	RORAIMA	0
55	1998-08-01	...	SERGIPE	0
56	1998-09-01	...	AMAPÁ	20
57	1998-09-01	...	DISTRITO FEDERAL	33
58	1998-09-01	...	PIAUÍ	1991
59	1998-09-01	...	RORAIMA	2
60	1998-10-01	...	AMAZONAS	83
61	1998-10-01	...	GOIÁS	1034
62	1998-10-01	...	MATO GROSSO	576
63	1998-10-01	...	PARAÍBA	179
64	1998-10-01	...	PARÁ	3665
65	1998-10-01	...	PIAUÍ	2586
66	1998-10-01	...	SERGIPE	0
67	1998-11-01	...	ALAGOAS	19
68	1998-11-01	...	AMAPÁ	131
69	1998-11-01	...	CEARÁ	575
70	1998-11-01	...	DISTRITO FEDERAL	0
71	1998-11-01	...	MARANHÃO	2237
72	1998-11-01	...	RIO DE JANEIRO	6
73	1998-11-01	...	RIO GRANDE DO SUL	28
74	1998-11-01	...	SÃO PAULO	488
75	1998-12-01	...	BAHIA	82
76	1998-12-01	...	MARANHÃO	1399
77	1998-12-01	...	MATO GROSSO	100
78	1998-12-01	...	PARAÍBA	51
79	1998-12-01	...	PERNAMBUCO	59
80	1998-12-01	...	RIO DE JANEIRO	1
81	1998-12-01	...	RONDÔNIA	33
82	1998-12-01	...	TOCANTINS	9
83	1999-01-01	...	ALAGOAS	58
84	1999-01-01	...	GOIÁS	14
85	1999-01-01	...	MATO GROSSO	239
86	1999-01-01	...	MINAS GERAIS	36
87	1999-01-01	...	PARÁ	87
88	1999-01-01	...	PERNAMBUCO	102
89	1999-01-01	...	RONDÔNIA	1
90	1999-01-01	...	SÃO PAULO	7
91	1999-01-01	...	TOCANTINS	36
92	1999-02-01	...	ACRE	0
93	1999-02-01	...	CEARÁ	16
94	1999-02-01	...	MATO GROSSO	69
95	1999-02-01	...	MATO GROSSO DO SUL	28
96	1999-02-01	...	PERNAMBUCO	13
97	1999-02-01	...	RONDÔNIA	1
98	1999-02-01	...	SANTA CATARINA	2
99	1999-02-01	...	TOCANTINS	1
100	1999-03-01	...	AMAPÁ	2

titanic.head(100)

	123 pclass Int 100%	...	123 body Int 9%	Abc Varchar(100) 57%
1	1	...	22
2	1	...	[null]
3	1	...	[null]
4	1	...	[null]
5	1	...	[null]
6	1	...	[null]
7	1	...	[null]
8	1	...	62
9	1	...	[null]
10	1	...	[null]
11	1	...	[null]
12	1	...	[null]
13	1	...	110
14	1	...	[null]
15	1	...	[null]
16	1	...	38
17	1	...	[null]
18	1	...	126
19	1	...	292
20	1	...	175
21	1	...	[null]
22	1	...	122
23	1	...	166
24	1	...	[null]
25	1	...	207
26	1	...	[null]
27	1	...	232
28	1	...	[null]
29	1	...	[null]
30	1	...	[null]
31	1	...	[null]
32	1	...	[null]
33	1	...	46
34	1	...	169
35	1	...	[null]
36	1	...	[null]
37	1	...	[null]
38	1	...	[null]
39	1	...	[null]
40	1	...	[null]
41	1	...	[null]
42	1	...	[null]
43	1	...	[null]
44	1	...	[null]
45	1	...	[null]
46	1	...	[null]
47	1	...	[null]
48	1	...	[null]
49	1	...	[null]
50	1	...	[null]
51	1	...	[null]
52	1	...	[null]
53	1	...	[null]
54	1	...	[null]
55	1	...	[null]
56	1	...	[null]
57	1	...	[null]
58	1	...	[null]
59	1	...	[null]
60	1	...	[null]
61	1	...	[null]
62	1	...	[null]
63	1	...	[null]
64	1	...	[null]
65	1	...	[null]
66	1	...	[null]
67	1	...	[null]
68	1	...	[null]
69	1	...	[null]
70	1	...	[null]
71	1	...	[null]
72	1	...	[null]
73	1	...	[null]
74	2	...	[null]
75	2	...	[null]
76	2	...	[null]
77	2	...	[null]
78	2	...	[null]
79	2	...	[null]
80	2	...	[null]
81	2	...	[null]
82	2	...	[null]
83	2	...	236
84	2	...	[null]
85	2	...	[null]
86	2	...	[null]
87	2	...	155
88	2	...	[null]
89	2	...	75
90	2	...	35
91	2	...	[null]
92	2	...	[null]
93	2	...	[null]
94	2	...	[null]
95	2	...	[null]
96	2	...	[null]
97	2	...	[null]
98	2	...	165
99	2	...	[null]
100	2	...	[null]

We can now run some queries to create a QueryProfiler object. One way to do so is by using the queries statement_id and transaction_id.

To allow for SQL execution in Jupyter cells, load the sql extension:

%load_ext verticapy.sql
The verticapy.sql extension is already loaded. To reload it, use:
  %reload_ext verticapy.sql

Next, let us run the queries:

%%sql
SELECT
    date,
    MONTH(date) as month,
    AVG(number) as avg_number_test
FROM public.amazon
GROUP BY date
ORDER BY avg_number_test DESC;

	📅 date Date 100%	...	123 month Integer 100%	123 avg_number_test Float(22) 100%
1	2007-09-01	...	9	3500.96296296296
2	2010-09-01	...	9	3163.25925925926
3	2003-09-01	...	9	2826.85185185185
4	2015-09-01	...	9	2669.74074074074
5	2002-08-01	...	8	2116.7037037037
6	2005-08-01	...	8	1925.22222222222
7	2002-10-01	...	10	1767.48148148148
8	2003-10-01	...	10	1603.51851851852
9	2004-10-01	...	10	1493.74074074074
10	2008-09-01	...	9	1460.92592592593
11	2012-10-01	...	10	1267.14814814815
12	2013-09-01	...	9	1169.81481481481
13	2016-10-01	...	10	1118.88888888889
14	2009-09-01	...	9	1090.0
15	2002-11-01	...	11	1043.66666666667
16	2015-11-01	...	11	1019.59259259259
17	2000-10-01	...	10	1012.44444444444
18	1998-10-01	...	10	870.185185185185
19	2000-09-01	...	9	862.62962962963
20	2017-07-01	...	7	848.555555555556

%%sql
SELECT
    a.date,
    MONTH(a.date) AS month,
    AVG(a.number) AS avg_number_test,
    b.max_number
FROM
    public.amazon AS a
JOIN (
    SELECT
        date,
        MAX(number) AS max_number
    FROM
        public.amazon
    GROUP BY
        date
) AS b
ON
    a.date = b.date
GROUP BY
    a.date, b.max_number
ORDER BY
    avg_number_test DESC;

	📅 date Date 100%	...	123 month Integer 100%	123 max_number Integer 100%
1	2017-09-01	...	9	25004
2	2002-09-01	...	9	11034
3	2011-09-01	...	9	7086
4	2015-10-01	...	10	7781
5	2005-10-01	...	10	8405
6	2003-08-01	...	8	11132
7	2014-08-01	...	8	8555
8	1998-09-01	...	9	10363
9	1999-08-01	...	8	18566
10	2015-08-01	...	8	7913
11	2008-10-01	...	10	7691
12	1999-09-01	...	9	11729
13	1998-08-01	...	8	15406
14	2001-10-01	...	10	6661
15	2009-10-01	...	10	6342
16	2004-07-01	...	7	11517
17	2003-11-01	...	11	6195
18	2003-12-01	...	12	4787
19	2004-06-01	...	6	9813
20	2013-11-01	...	11	3688

In order to create a QueryProfiler object from a query, we need the queries statement_id and transaction_id, both of which are found in the QUERY_REQUESTS system table:

from verticapy.performance.vertica import QueryProfiler, QueryProfilerInterface

qprof = QueryProfiler((45035996273780927,76))

To create a QueryProfiler object w/ multiple queries, provide a list of tuples

qprof = QueryProfilerInterface([(45035996273780927,74), (45035996273780075,6)])

Once the QueryProfiler object is created, you can run the get_queries() method to view the queries contained in the QueryProfiler object:

qprof.get_queries()

	...	📅 start_timestamp Timestamp(29) 100%	📅 end_timestamp Timestamp(29) 100%
0	...	2024-10-31 15:34:08.680296	2024-10-31 15:34:08.744459
1	...	2024-10-31 15:34:14.053410	2024-10-31 15:34:14.206118

To visualize the query plan, run get_qplan_tree(), which is customizable, allowing you to specify certain metrics or focus on a specified tree path:

_images/user_guide_performance_qprof_get_qplan_tree.png

Create a `QueryProfiler` object directly from a query¶

You can also create the QueryProfiler Object directly from an SQL Command:

qprof = QueryProfiler("""
    select transaction_id, statement_id, request, request_duration
    from query_requests where start_timestamp > (now() - interval'1 hour')
    order by request_duration desc limit 10;
    """
)

Save the QueryProfiler object in a target schema¶

After you create a QueryProfiler object, you can save it to a target schema.

In this example, we will save the object to the sc_demo schema:

vp.create_schema("sc_demo")
Out[6]: False

To save the QueryProfiler object, specify the target_schema and, optionally, a key_id (it is a unique key which is used to search for the stored Qprof object) when creating the QueryProfiler object:

# Save it to your schema
qprof = QueryProfiler(
    (45035996273780927, 76),
    target_schema = "sc_demo",
    key_id = "unique_xx1",
    overwrite = True,
)

Load a `QueryProfiler` object¶

To load a previously saved QueryProfiler, simply provide its target_schema and key_id:

from verticapy.performance.vertica import QueryProfiler, QueryProfilerInterface

# Someone else can now connect to my DB and use the object.
qprof = QueryProfiler(
    target_schema = "sc_demo",
    key_id = "unique_xx1",
)

Export and import¶

You can export and import QueryProfiler objects as .tar files.

Export¶

To export a QueryProfiler object, use the export_profile() method:

qprof.export_profile(filename="test_export_1.tar")

Note

There is also a shell script which helps you export qprof data without python. See qprof_export .

Import¶

To import a QueryProfiler object, use the import_profile() method and provide the target_schema and key_id.

Make sure the key_id is unique/unused. Let us create a new schema to load this into:

vp.create_schema("sc_demo_1")

qprof = QueryProfiler.import_profile(
    target_schema = "sc_demo_1",
    key_id = "unique_load_xx1",
    filename = "test_export_1.tar",
    auto_initialize = True,
)

Methods & attributes¶

The QueryProfiler object includes many useful methods and attributes to aid in the analysis of query performence.

Access performance tables¶

With the QueryProfiler object, you can access any of the following tables:

qprof.get_table()
Out[7]: 
['dc_requests_issued',
 'dc_explain_plans',
 'dc_query_executions',
 'dc_plan_activities',
 'dc_scan_events',
 'dc_slow_events',
 'execution_engine_profiles',
 'host_resources',
 'query_events',
 'query_plan_profiles',
 'query_profiles',
 'projection_storage',
 'projection_usage',
 'resource_pool_status',
 'storage_containers',
 'dc_lock_attempts',
 'dc_plan_resources',
 'configuration_parameters',
 'query_consumption',
 'projections',
 'projection_columns',
 'resource_acquisitions',
 'resource_pools']

For example, view the QUERY_EVENTS table:

qprof.get_table("query_events")

Or the DC_EXPLAIN_PLANS table:

qprof.get_table('dc_explain_plans')

Or the QUERY_CONSUMPTION table:

qprof.get_table("query_consumption")

Get query information¶

You can retrieve the query information, such as transaction_id and statement_id, from the QueryProfiler object:

qprof = QueryProfiler("""
    select transaction_id, statement_id, request, request_duration
    from query_requests where start_timestamp > (now() - interval'1 hour')
    order by request_duration desc limit 10;
    """
)

Searching the performance tables...
Setting the requests and queries durations...
Checking all the tables consistency using a single SQL query...
Checking all the tables data types...

View the transaction_id and statement_id:

tid = qprof.transaction_id

sid = qprof.statement_id

print(tid, sid)
45035996277028508 78

Display the query request:

# Pretty, Formatted, Results
qprof.get_request()
<IPython.core.display.Markdown object>
Out[12]: "PROFILE \n   SELECT\n     transaction_id,\n     statement_id,\n     request,\n     request_duration \n   FROM\n query_requests \n   WHERE start_timestamp > (now() - INTERVAL'1 hour') \n   ORDER BY request_duration DESC \n   LIMIT 10"

View the number of query steps in a bar graph:

qprof.get_qsteps(kind="bar")

Loading....

qprof.get_qplan()
+-SELECT  LIMIT 10 [Cost: 62K, Rows: 10 (NO STATISTICS)] (PATH ID: 0)
|  Output Only: 10 tuples
|  Execute on: Query Initiator
| +---> SORT [TOPK] [Cost: 62K, Rows: 10K (NO STATISTICS)] (PATH ID: 1)
| |      Order: query_requests.request_duration DESC
| |      Output Only: 10 tuples
| |      Execute on: All Nodes
| |      Execute on: All Nodes
| | +---> JOIN HASH [LeftOuter] [Cost: 2K, Rows: 10K (NO STATISTICS)] (PATH ID: 3)
| | |      Join Cond: (ri.node_name = dc_requests_completed.node_name) AND (ri.session_id = dc_requests_completed.session_id) AND (ri.request_id = dc_requests_completed.request_id)
| | |      Materialize at Output: ri."time", ri.transaction_id, ri.statement_id, ri.request
| | |      Execute on: All Nodes
| | | +-- Outer -> STORAGE ACCESS for ri [Cost: 649, Rows: 10K (NO STATISTICS)] (PATH ID: 4)
| | | |      Projection: v_internal.dc_requests_issued_p
| | | |      Materialize: ri.node_name, ri.session_id, ri.request_id
| | | |      Filter: (ri."time" > '2024-10-31 14:34:55.082575-04'::timestamptz)
| | | |      Execute on: All Nodes
| | | +-- Inner -> STORAGE ACCESS for dc_requests_completed [Cost: 965, Rows: 10K (NO STATISTICS)] (PATH ID: 5)
| | | |      Projection: v_internal.dc_requests_completed_p
| | | |      Materialize: dc_requests_completed.request_id, dc_requests_completed."time", dc_requests_completed.node_name, dc_requests_completed.session_id
| | | |      Filter: (dc_requests_completed.node_name IS NOT NULL)
| | | |      Filter: (dc_requests_completed.session_id IS NOT NULL)
| | | |      Filter: (dc_requests_completed.request_id IS NOT NULL)
| | | |      Execute on: All Nodes
Out[13]: '+-SELECT  LIMIT 10 [Cost: 62K, Rows: 10 (NO STATISTICS)] (PATH ID: 0)\n|  Output Only: 10 tuples\n|  Execute on: Query Initiator\n| +---> SORT [TOPK] [Cost: 62K, Rows: 10K (NO STATISTICS)] (PATH ID: 1)\n| |      Order: query_requests.request_duration DESC\n| |      Output Only: 10 tuples\n| |      Execute on: All Nodes\n| |      Execute on: All Nodes\n| | +---> JOIN HASH [LeftOuter] [Cost: 2K, Rows: 10K (NO STATISTICS)] (PATH ID: 3)\n| | |      Join Cond: (ri.node_name = dc_requests_completed.node_name) AND (ri.session_id = dc_requests_completed.session_id) AND (ri.request_id = dc_requests_completed.request_id)\n| | |      Materialize at Output: ri."time", ri.transaction_id, ri.statement_id, ri.request\n| | |      Execute on: All Nodes\n| | | +-- Outer -> STORAGE ACCESS for ri [Cost: 649, Rows: 10K (NO STATISTICS)] (PATH ID: 4)\n| | | |      Projection: v_internal.dc_requests_issued_p\n| | | |      Materialize: ri.node_name, ri.session_id, ri.request_id\n| | | |      Filter: (ri."time" > \'2024-10-31 14:34:55.082575-04\'::timestamptz)\n| | | |      Execute on: All Nodes\n| | | +-- Inner -> STORAGE ACCESS for dc_requests_completed [Cost: 965, Rows: 10K (NO STATISTICS)] (PATH ID: 5)\n| | | |      Projection: v_internal.dc_requests_completed_p\n| | | |      Materialize: dc_requests_completed.request_id, dc_requests_completed."time", dc_requests_completed.node_name, dc_requests_completed.session_id\n| | | |      Filter: (dc_requests_completed.node_name IS NOT NULL)\n| | | |      Filter: (dc_requests_completed.session_id IS NOT NULL)\n| | | |      Filter: (dc_requests_completed.request_id IS NOT NULL)\n| | | |      Execute on: All Nodes'

qprof.get_qplan_tree()

To view the query plan profile in a pie chart:

qprof.get_qplan_profile(kind = "pie")

To view the cpu time of the query in a bar graph:

qprof.get_cpu_time(kind="bar")

`QueryProfiler` execution report¶

The QueryProfiler object can also generate a report that includes various performence metrics, including which operation took the most amount of time:

qprof.get_qexecution_report().sort({"exec_time_us": "desc"})

	Abc node_name Varchar(128) 100%	...	123 path_id Integer 100%	123 total_rows_read_sort Integer 0%
1	v_mldb_node0001	...	3	[null]
2	v_mldb_node0001	...	5	[null]
3	v_mldb_node0001	...	5	[null]
4	v_mldb_node0001	...	3	[null]
5	v_mldb_node0001	...	2	[null]
6	v_mldb_node0001	...	1	[null]
7	v_mldb_node0001	...	1	[null]
8	v_mldb_node0001	...	4	[null]
9	v_mldb_node0001	...	1	[null]
10	v_mldb_node0001	...	-1	[null]
11	v_mldb_node0003	...	3	[null]
12	v_mldb_node0003	...	5	[null]
13	v_mldb_node0003	...	3	[null]
14	v_mldb_node0001	...	-1	[null]
15	v_mldb_node0002	...	3	[null]
16	v_mldb_node0004	...	3	[null]
17	v_mldb_node0003	...	1	[null]
18	v_mldb_node0001	...	1	[null]
19	v_mldb_node0002	...	5	[null]
20	v_mldb_node0001	...	0	[null]

To view the query execution details:

qprof.get_qexecution()

`QueryProfiler` Summary Report Export¶

You can also easily export the entire report in an HTML format. This report can be read without having any connection to database or a jupyter environment making it very convenient to share and analyze offline.

qprof.to_html("my-report.html") # Where "my-report.html" is the path of the file to save.

	📅 event_timestamp Timestamptz(35) 100%	...	Abc event_severity Varchar(13) 100%	Abc Varchar(64000) 100%
1	2024-08-26 12:02:37.531048-04:00	...	Informational
2	2024-08-26 12:02:37.531966-04:00	...	Informational
3	2024-08-26 12:02:37.538079-04:00	...	Informational
4	2024-10-31 15:24:16.875429-04:00	...	Informational
5	2024-10-31 15:24:16.875502-04:00	...	Informational
6	2024-10-31 15:24:22.142267-04:00	...	Informational
7	2024-10-31 15:24:22.142323-04:00	...	Informational
8	2024-10-31 15:24:22.142794-04:00	...	Critical
9	2024-10-31 15:24:22.142864-04:00	...	Critical
10	2024-10-31 15:24:22.142910-04:00	...	Critical
11	2024-10-31 15:24:22.143009-04:00	...	Critical
12	2024-10-31 15:24:22.143103-04:00	...	Critical
13	2024-10-31 15:24:22.143658-04:00	...	Informational
14	2024-10-31 15:24:22.143732-04:00	...	Warning
15	2024-10-31 15:24:22.144575-04:00	...	Informational
16	2024-10-31 15:24:24.418582-04:00	...	Informational
17	2024-10-31 15:24:24.418645-04:00	...	Informational
18	2024-10-31 15:24:24.419569-04:00	...	Critical
19	2024-10-31 15:24:24.419636-04:00	...	Critical
20	2024-10-31 15:24:24.419673-04:00	...	Critical

	📅 time Timestamptz(35) 100%	...	123 path_line_index Integer 100%	Abc Varchar(64000) 100%
1	2024-10-31 15:34:18.682896-04:00	...	3
2	2024-10-31 15:34:18.682897-04:00	...	4
3	2024-10-31 15:34:18.682898-04:00	...	5
4	2024-10-31 15:34:18.682899-04:00	...	6
5	2024-10-31 15:34:18.682900-04:00	...	7
6	2024-10-31 15:34:18.682954-04:00	...	1
7	2024-10-31 15:34:18.682957-04:00	...	2
8	2024-10-31 15:34:18.682958-04:00	...	3
9	2024-10-31 15:34:18.682960-04:00	...	4
10	2024-10-31 15:34:18.682961-04:00	...	5
11	2024-10-31 15:34:18.682962-04:00	...	6
12	2024-10-31 15:34:18.683022-04:00	...	1
13	2024-10-31 15:34:18.683028-04:00	...	2
14	2024-10-31 15:34:18.683029-04:00	...	3
15	2024-10-31 15:34:18.683031-04:00	...	4
16	2024-10-31 15:34:18.683032-04:00	...	5
17	2024-10-31 15:34:18.683033-04:00	...	6
18	2024-10-31 15:34:18.683034-04:00	...	7
19	2024-10-31 15:34:18.683074-04:00	...	1
20	2024-10-31 15:34:18.683077-04:00	...	2

	📅 start_time Timestamptz(35) 100%	...	010 is_retry Boolean 59%	010 success Boolean 82%
1	2024-10-30 23:59:28.093856-04:00	...	[null]	✅
2	2024-10-30 23:59:28.465125-04:00	...	[null]	✅
3	2024-10-30 23:59:30.273588-04:00	...	[null]	✅
4	2024-10-30 23:59:35.198877-04:00	...	[null]	✅
5	2024-10-30 23:59:35.948951-04:00	...	[null]	[null]
6	2024-10-30 23:59:36.062933-04:00	...	[null]	✅
7	2024-10-30 23:59:41.358634-04:00	...	[null]	✅
8	2024-10-30 23:59:56.879715-04:00	...	[null]	[null]
9	2024-10-31 00:00:00.683415-04:00	...	[null]	✅
10	2024-10-31 00:00:20.915550-04:00	...	[null]	✅
11	2024-10-31 00:00:21.274346-04:00	...	[null]	✅
12	2024-10-31 00:00:40.441579-04:00	...	[null]	✅
13	2024-10-31 00:00:45.360625-04:00	...	[null]	[null]
14	2024-10-31 00:01:02.417764-04:00	...	[null]	✅
15	2024-10-31 00:01:03.187370-04:00	...	[null]	✅
16	2024-10-31 00:01:03.299935-04:00	...	[null]	[null]
17	2024-10-31 00:01:03.615058-04:00	...	[null]	✅
18	2024-10-31 00:01:04.748955-04:00	...	[null]	✅
19	2024-10-31 00:01:06.115382-04:00	...	[null]	[null]
20	2024-10-31 00:01:09.571069-04:00	...	[null]	✅

Getting started with Query Profiler¶

VerticaPyLab¶

QueryProfiler¶

Create and save a QueryProfiler object¶

Create QueryProfiler object from transaction id and statement id¶

Create a QueryProfiler object directly from a query¶

Save the QueryProfiler object in a target schema¶

Load a QueryProfiler object¶

Export and import¶

Export¶

Import¶

Methods & attributes¶

Access performance tables¶

Get query information¶

QueryProfiler execution report¶

QueryProfiler Summary Report Export¶

Create `QueryProfiler` object from transaction id and statement id¶

Create a `QueryProfiler` object directly from a query¶

Load a `QueryProfiler` object¶

`QueryProfiler` execution report¶

`QueryProfiler` Summary Report Export¶