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Viewing query plans
You can obtain query plans in two ways:.
You can obtain query plans in two ways:
-
The
EXPLAIN statement outputs query plans in various text formats (see below).
-
Management Console provides a graphical interface for viewing query plans. For detailed information, see Working with query plans in MC.
You can also observe the real-time flow of data through a query plan by querying the system table
QUERY_PLAN_PROFILES. For more information, see Profiling query plans.
EXPLAIN output options
By default, EXPLAIN output represents the query plan as a hierarchy, where each level, or path, represents a single database operation that the optimizer uses to execute a query. EXPLAIN output also appends DOT language source so you can display this output graphically with open source Graphviz tools.
EXPLAIN supports options for producing verbose and JSON output. You can also show the local query plans that are assigned to each node, which together comprise the total (global) query plan.
EXPLAIN also supports an ANNOTATED option. EXPLAIN ANNOTATED returns a query with embedded optimizer hints, which encapsulate the query plan for this query. For an example of usage, see Using optimizer-generated and custom directed queries together.
1 - EXPLAIN-Generated query plans
EXPLAIN returns the optimizer's query plan for executing a specified query.
EXPLAIN returns the optimizer's query plan for executing a specified query. For example:
QUERY PLAN DESCRIPTION:
------------------------------
EXPLAIN SELECT customer_name, customer_state FROM customer_dimension WHERE customer_state IN ('MA','NH') AND customer_gender='Male' ORDER BY customer_name LIMIT 10;
Access Path:
+-SELECT LIMIT 10 [Cost: 365, Rows: 10] (PATH ID: 0)
| Output Only: 10 tuples
| Execute on: Query Initiator
| +---> SORT [TOPK] [Cost: 365, Rows: 544] (PATH ID: 1)
| | Order: customer_dimension.customer_name ASC
| | Output Only: 10 tuples
| | Execute on: Query Initiator
| | +---> STORAGE ACCESS for customer_dimension [Cost: 326, Rows: 544] (PATH ID: 2)
| | | Projection: public.customer_dimension_DBD_1_rep_VMartDesign_node0001
| | | Materialize: customer_dimension.customer_state, customer_dimension.customer_name
| | | Filter: (customer_dimension.customer_gender = 'Male')
| | | Filter: (customer_dimension.customer_state = ANY (ARRAY['MA', 'NH']))
| | | Execute on: Query Initiator
| | | Runtime Filter: (SIP1(TopK): customer_dimension.customer_name)
You can use EXPLAIN to evaluate choices that the optimizer makes with respect to a given query. If you think query performance is less than optimal, run it through the Database Designer. For more information, see Incremental design and Reducing Run-time of Queries.
2 - JSON-Formatted query plans
EXPLAIN JSON returns a query plan in JSON format.
EXPLAIN JSON returns a query plan in JSON format. For example:
=> EXPLAIN JSON SELECT customer_name, customer_state FROM customer_dimension
WHERE customer_state IN ('MA','NH') AND customer_gender='Male' ORDER BY customer_name LIMIT 10;
------------------------------
{
"PARAMETERS" : {
"QUERY_STRING" : "EXPLAIN JSON SELECT customer_name, customer_state FROM customer_dimension \n
WHERE customer_state IN ('MA','NH') AND customer_gender='Male' ORDER BY customer_name LIMIT 10;"
},
"PLAN" : {
"PATH_ID" : 0,
"PATH_NAME" : "SELECT",
"EXTRA" : " LIMIT 10",
"COST" : 2114.000000,
"ROWS" : 10.000000,
"COST_STATUS" : "NO_STATISTICS",
"TUPLE_LIMIT" : 10,
"EXECUTE_NODE" : "Query Initiator",
"INPUT" : {
"PATH_ID" : 1,
"PATH_NAME" : "SORT",
"EXTRA" : "[TOPK]",
"COST" : 2114.000000,
"ROWS" : 49998.000000,
"COST_STATUS" : "NO_STATISTICS",
"ORDER" : ["customer_dimension.customer_name", "customer_dimension.customer_state"],
"TUPLE_LIMIT" : 10,
"EXECUTE_NODE" : "All Nodes",
"INPUT" : {
"PATH_ID" : 2,
"PATH_NAME" : "STORAGE ACCESS",
"EXTRA" : "for customer_dimension",
"COST" : 252.000000,
"ROWS" : 49998.000000,
"COST_STATUS" : "NO_STATISTICS",
"TABLE" : "public.customer_dimension",
"PROJECTION" : "public.customer_dimension_b0",
"MATERIALIZE" : ["customer_dimension.customer_name", "customer_dimension.customer_state"],
"FILTER" : ["(customer_dimension.customer_state = ANY (ARRAY['MA', 'NH']))", "(customer_dimension.customer_gender = 'Male')"],
"EXECUTE_NODE" : "All Nodes"
"SIP" : "Runtime Filter: (SIP1(TopK): customer_dimension.customer_name)"
}
}
}
}
(40 rows)
3 - Verbose query plans
You can qualify EXPLAIN with the VERBOSE option.
You can qualify EXPLAIN with the VERBOSE option. This option, valid for default and JSON output, increases the amount of detail in the rendered query plan
For example, the following EXPLAIN statement specifies to produce verbose output. Added information is set off in bold:
=> EXPLAIN VERBOSE SELECT customer_name, customer_state FROM customer_dimension
WHERE customer_state IN ('MA','NH') AND customer_gender='Male' ORDER BY customer_name LIMIT 10;
QUERY PLAN DESCRIPTION:
------------------------------
Opt Vertica Options
--------------------
PLAN_OUTPUT_SUPER_VERBOSE
EXPLAIN VERBOSE SELECT customer_name, customer_state FROM customer_dimension
WHERE customer_state IN ('MA','NH') AND customer_gender='Male'
ORDER BY customer_name LIMIT 10;
Access Path:
+-SELECT LIMIT 10 [Cost: 756.000000, Rows: 10.000000 Disk(B): 0.000000 CPU(B): 0.000000 Memory(B): 0.000000 Netwrk(B): 0.000000 Parallelism: 1.000000] [OutRowSz (B): 274](PATH ID: 0)
| Output Only: 10 tuples
| Execute on: Query Initiator
| Sort Key: (customer_dimension.customer_name)
| LDISTRIB_UNSEGMENTED
| +---> SORT [TOPK] [Cost: 756.000000, Rows: 9998.000000 Disk(B): 0.000000 CPU(B): 34274697.123457 Memory(B): 2739452.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 274] (PATH ID: 1)
| | Order: customer_dimension.customer_name ASC
| | Output Only: 10 tuples
| | Execute on: Query Initiator
| | Sort Key: (customer_dimension.customer_name)
| | LDISTRIB_UNSEGMENTED
| | +---> STORAGE ACCESS for customer_dimension [Cost: 513.000000, Rows: 9998.000000 Disk(B): 0.000000 CPU(B): 0.000000 Memory(B): 0.000000 Netwrk(B): 0.000000 Parallelism: 4.000000 (NO STATISTICS)] [OutRowSz (B): 274] (PATH ID: 2)
| | | Column Cost Aspects: [ Disk(B): 7371817.156569 CPU(B): 4914708.578284 Memory(B): 2659466.004399 Netwrk(B): 0.000000 Parallelism: 4.000000 ]
| | | Projection: public.customer_dimension_P1
| | | Materialize: customer_dimension.customer_state, customer_dimension.customer_name
| | | Filter: (customer_dimension.customer_gender = 'Male')/* sel=0.999800 ndv= 500 */
| | | Filter: (customer_dimension.customer_state = ANY (ARRAY['MA', 'NH']))/* sel=0.999800 ndv= 500 */
| | | Execute on: All Nodes
| | | Runtime Filter: (SIP1(TopK): customer_dimension.customer_name)
| | | Sort Key: (customer_dimension.household_id, customer_dimension.customer_key, customer_dimension.store_membership_card, customer_dimension.customer_type, customer_dimension.customer_region, customer_dimension.title, customer_dimension.number_of_children)
| | | LDISTRIB_SEGMENTED
4 - Local query plans
EXPLAIN LOCAL (on a multi-node database) shows the local query plans assigned to each node, which together comprise the total (global) query plan.
EXPLAIN LOCAL (on a multi-node database) shows the local query plans assigned to each node, which together comprise the total (global) query plan. If you omit this option, Vertica shows only the global query plan. Local query plans are shown only in DOT language source, which can be rendered in Graphviz.
For example, the following EXPLAIN statement includes the LOCAL option:
=> EXPLAIN LOCAL SELECT store_name, store_city, store_state
FROM store.store_dimension ORDER BY store_state ASC, store_city ASC;
The output includes GraphViz source, which describes the local query plans assigned to each node. For example, output for this statement on a three-node database includes a GraphViz description of the following query plan for one node (v_vmart_node0003):
-----------------------------------------------
PLAN: v_vmart_node0003 (GraphViz Format)
-----------------------------------------------
digraph G {
graph [rankdir=BT, label = "v_vmart_node0003\n", labelloc=t, labeljust=l ordering=out]
0[label = "NewEENode \nOutBlk=[UncTuple(3)]", color = "green", shape = "box"];
1[label = "Send\nSend to: v_vmart_node0001\nNet id: 1000\nMerge\n\nUnc: Char(2)\nUnc: Varchar(64)\nUnc: Varchar(64)", color = "green", shape = "box"];
2[label = "Sort: (keys = A,A,N)\nUnc: Char(2)\nUnc: Varchar(64)\nUnc: Varchar(64)", color = "green", shape = "box"];
3[label = "ExprEval: \n store_dimension.store_state\n store_dimension.store_city\n store_dimension.store_name\nUnc: Char(2)\nUnc: Varchar(64)\nUnc: Varchar(64)
", color = "green", shape = "box"];
4[label = "StorageUnionStep: store_dimension_p_b0\nUnc: Varchar(64)\nUnc: Varchar(64)\nUnc: Char(2)", color = "purple", shape = "box"];
5[label = "ScanStep: store_dimension_p_b0\nstore_key (not emitted)\nstore_name\nstore_city\nstore_state\nUnc: Varchar(64)\nUnc: Varchar(64)\nUnc: Char(2)", color
= "brown", shape = "box"];
1->0 [label = "0",color = "blue"];
2->1 [label = "0",color = "blue"];
3->2 [label = "0",color = "blue"];
4->3 [label = "0",color = "blue"];
5->4 [label = "0",color = "blue"];
}
GraphViz renders this output as follows:
