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@wzhfy wzhfy commented Jan 16, 2017

What changes were proposed in this pull request?

Currently we can only check the estimated stats in logical plans by debugging. We need to provide an easier and more efficient way for developers/users.

In this pr, we add EXPLAIN COST command to show stats in the optimized logical plan.
E.g.

spark-sql> EXPLAIN COST select count(1) from store_returns;

...
== Optimized Logical Plan ==
Aggregate [count(1) AS count(1)#24L], Statistics(sizeInBytes=16.0 B, rowCount=1, isBroadcastable=false)
+- Project, Statistics(sizeInBytes=4.3 GB, rowCount=5.76E+8, isBroadcastable=false)
   +- Relation[sr_returned_date_sk#3,sr_return_time_sk#4,sr_item_sk#5,sr_customer_sk#6,sr_cdemo_sk#7,sr_hdemo_sk#8,sr_addr_sk#9,sr_store_sk#10,sr_reason_sk#11,sr_ticket_number#12,sr_return_quantity#13,sr_return_amt#14,sr_return_tax#15,sr_return_amt_inc_tax#16,sr_fee#17,sr_return_ship_cost#18,sr_refunded_cash#19,sr_reversed_charge#20,sr_store_credit#21,sr_net_loss#22] parquet, Statistics(sizeInBytes=28.6 GB, rowCount=5.76E+8, isBroadcastable=false)
...

How was this patch tested?

Add test cases.

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wzhfy commented Jan 16, 2017

cc @rxin @cloud-fan

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SparkQA commented Jan 16, 2017

Test build #71424 has started for PR 16594 at commit c3489fc.

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wzhfy commented Jan 16, 2017

retest this please

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SparkQA commented Jan 16, 2017

Test build #71430 has finished for PR 16594 at commit c3489fc.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds no public classes.

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SparkQA commented Jan 17, 2017

Test build #71508 has finished for PR 16594 at commit 3d66df9.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds no public classes.

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A general style suggestion. Normally, the SQL keywords are using upper case in the test cases.

explain select * from src -> EXPLAIN SELECT * FROM src

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thanks, fixed

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Why not doing this by default? Do we need an extra flag?

If needed, the name should be SHOW_TABLE_STATS_IN_EXPLAIN

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If we do it by default, it can simplify this PR a lot.

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SHOW_TABLE_STATS_IN_EXPLAIN could be misleading, because we are not only showing stats for table, but also for all logical plans.

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It's invalidated by default because stats info can be inaccurate (and in some cases very inaccurate), and can confuse regular users. At current stage it's better to be a feature for administrators and developers to see how cbo behaves in estimation. So I make the flag "internal".

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Then, when the stats are not accurate, will it be the cause of an inefficient plan? If so, why not showing them the number?

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I'm not sure. e.g., after joins of many tables, if sizeInBytes is computed by the simple way (non-cbo way), we just multiply all the sizes of these tables, then sizeInBytes becomes a ridiculously large value. I think this will harm user experience.
I agree removing the flag can simplify code a lot, but I'm hesitated to expose such information to all users.

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If the sizeInBytes affects the plan decision, I think it makes sense to let users see it.

When the plan is not expected and the number is super large, they might turn on/off CBO or trigger the command to re-analyze the tables. Hiding it looks not right to me, even if the number is ugly. : )

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OK. But since it changes user interface, let's double check with others. @rxin @hvanhovell @cloud-fan Shall we show stats of LogicalPlan directly in explain command ?

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SparkQA commented Jan 18, 2017

Test build #71588 has finished for PR 16594 at commit 6af640d.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds no public classes.

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@wzhfy could you add an example of this to the PR description? I am a bit worried that the explain plans will become (much) harder to read. I am also interested to see if this new explain output is understandable for an end user.

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wzhfy commented Jan 23, 2017

@hvanhovell I've updated the description which shows a simple example.

The explained plan will become hard to read when joining many tables and sizeInBytes is computed by the simple way (non-cbo way), i.e. we just multiply all the sizes of these tables, then sizeInBytes becomes a super large value (could be more than a hundred digits).
e.g. part of the explained plan of tpcds q31 looks like this (not using cbo):

== Optimized Logical Plan ==
...
+- Aggregate [ca_county#559, d_qoy#480, d_year#476], [ca_county#559, MakeDecimal(sum(UnscaledValue(ss_ext_sales_price#24)),17,2) AS store_sales#387]: sizeInBytes=54,810,794,086,252,700,000,000, isBroadcastable=false
   +- Project [ss_ext_sales_price#24, d_year#476, d_qoy#480, ca_county#559]: sizeInBytes=66,990,970,549,864,410,000,000, isBroadcastable=false
      +- Join Inner, (ss_addr_sk#15 = ca_address_sk#552): sizeInBytes=79,171,147,013,476,130,000,000, isBroadcastable=false
         :- Project [ss_addr_sk#15, ss_ext_sales_price#24, d_year#476, d_qoy#480]: sizeInBytes=3,963,069,503,456,967, isBroadcastable=false
         :  +- Join Inner, (ss_sold_date_sk#9 = d_date_sk#470): sizeInBytes=5,095,375,075,873,244, isBroadcastable=false
         :     :- Project [ss_sold_date_sk#9, ss_addr_sk#15, ss_ext_sales_price#24]: sizeInBytes=39,847,153,628, isBroadcastable=false
         :     :  +- Filter (isnotnull(ss_sold_date_sk#9) && isnotnull(ss_addr_sk#15)): sizeInBytes=245,724,114,045, isBroadcastable=false
         :     :     +- Relation[ss_sold_date_sk#9,ss_sold_time_sk#10,ss_item_sk#11,ss_customer_sk#12,ss_cdemo_sk#13,ss_hdemo_sk#14,ss_addr_sk#15,ss_store_sk#16,ss_promo_sk#17,ss_ticket_number#18,ss_quantity#19,ss_wholesale_cost#20,ss_list_price#21,ss_sales_price#22,ss_ext_discount_amt#23,ss_ext_sales_price#24,ss_ext_wholesale_cost#25,ss_ext_list_price#26,ss_ext_tax#27,ss_coupon_amt#28,ss_net_paid#29,ss_net_paid_inc_tax#30,ss_net_profit#31] parquet: sizeInBytes=245,724,114,045, rowCount=5,759,954,874, isBroadcastable=false
...

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rxin commented Jan 23, 2017

sorry this explain plan makes no sense -- it is impossible to read.

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wzhfy commented Jan 23, 2017

@rxin Can we add a flag to enable or disable it? Currently there's no other way to see size and row count except debugging.

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Let us do some research how the other RDBMSs are doing it? For example, Oracle

SQL> explain plan for select * from product;
Explained.

SQL> select * from table(dbms_xplan.display);

PLAN_TABLE_OUTPUT
-----------------------------------------------------
Plan hash value: 3917577207
-----------------------------------------------------
| Id  | Operation          | Name    | Rows  | Bytes |
-----------------------------------------------------
|   0 | SELECT STATEMENT   |         | 15856 |  1254K|
|   1 |  TABLE ACCESS FULL | PRODUCT | 15856 |  1254K|
-----------------------------------------------------

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DB2 has a tool to format the contents of the EXPLAIN tables. Below is an example of the output with explanation:

screenshot 2017-01-22 21 05 45

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PostgreSQL has a few different options in the EXPLAIN command:

EXPLAIN SELECT * FROM foo WHERE i = 4;

                         QUERY PLAN
--------------------------------------------------------------
 Index Scan using fi on foo  (cost=0.00..5.98 rows=1 width=4)
   Index Cond: (i = 4)
(2 rows)

The same plan with cost estimates suppressed:

EXPLAIN (COSTS FALSE) SELECT * FROM foo WHERE i = 4;

        QUERY PLAN
----------------------------
 Index Scan using fi on foo
   Index Cond: (i = 4)
(2 rows)

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gatorsmile commented Jan 23, 2017

MySQL also outputs the statistics in the command of EXPLAIN. See the link: https://dev.mysql.com/doc/refman/5.7/en/explain-extended.html

As of MySQL 5.7.3, the EXPLAIN statement is changed so that the effect of the EXTENDED keyword is always enabled.

mysql> EXPLAIN EXTENDED
    -> SELECT t1.a, t1.a IN (SELECT t2.a FROM t2) FROM t1\G
*************************** 1. row ***************************
           id: 1
  select_type: PRIMARY
        table: t1
         type: index
possible_keys: NULL
          key: PRIMARY
      key_len: 4
          ref: NULL
         rows: 4
     filtered: 100.00
        Extra: Using index
*************************** 2. row ***************************
           id: 2
  select_type: SUBQUERY
        table: t2
         type: index
possible_keys: a
          key: a
      key_len: 5
          ref: NULL
         rows: 3
     filtered: 100.00
        Extra: Using index
2 rows in set, 1 warning (0.00 sec)

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SQLServer has three ways to show the plan: graphical plans, text plans, and XML plans. Actually, it is pretty advanced. When using the text plans, users can set the output formats:

  1. SHOWPLAN_ALL – A reasonably complete set of data showing the estimated execution
    plan for the query.
  2. SHOWPLAN_TEXT – Provides a very limited set of data for use with tools like osql.exe.
    It, too, only shows the estimated execution plan
  3. STATISTICS PROFILE – Similar to SHOWPLAN_ALL except it represents the data for
    the actual execution plan.

I found a 300-pages book SQL Server Execution Plans. For details, you can download and read it.

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:- ) No perfect solution, but we should use the metric prefix when the number is huge.

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SparkQA commented Jan 23, 2017

Test build #71847 has finished for PR 16594 at commit ddd5936.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds no public classes.

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ron8hu commented Jan 24, 2017

To show a very large Long number, there is no need to print out every digit in the number. We can use exponent. For example, a number 120,000,000,005,123 can be printed as 1.2*10E14, where 10E14 means 10 to the power 14.

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wzhfy commented Jan 24, 2017

@ron8hu Yes, I've already updated this pr. I'll present an example.

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wzhfy commented Jan 24, 2017

@rxin @gatorsmile @hvanhovell I've updated this pr and make stats much more readable:

SizeInBytes is shown in units of B, KB, MB ... PB, e.g. sizeInBytes=228.8 GB,
and if it's too large to represent in TB, it's shown in scientific notation, e.g. sizeInBytes=5.48E+22 B.
For row count, it doesn't have units, so it's always shown in scientific notation, e.g. rowCount=7.31E+4.

Now the above example looks like this:

...
+- Aggregate [ca_county#1696, d_qoy#1617, d_year#1613], [ca_county#1696, MakeDecimal(sum(UnscaledValue(ss_ext_sales_price#1259)),17,2) AS store_sales#1524], Statistics(sizeInBytes=5.481E+22 B, isBroadcastable=false)
         +- Project [ss_ext_sales_price#1259, d_year#1613, d_qoy#1617, ca_county#1696], Statistics(sizeInBytes=6.699E+22 B, isBroadcastable=false)
            +- Join Inner, (ss_addr_sk#1250 = ca_address_sk#1689), Statistics(sizeInBytes=7.917E+22 B, isBroadcastable=false)
               :- Project [ss_addr_sk#1250, ss_ext_sales_price#1259, d_year#1613, d_qoy#1617], Statistics(sizeInBytes=3.520 PB, isBroadcastable=false)
               :  +- Join Inner, (ss_sold_date_sk#1244 = d_date_sk#1607), Statistics(sizeInBytes=4.525 PB, isBroadcastable=false)
               :     :- Project [ss_sold_date_sk#1244, ss_addr_sk#1250, ss_ext_sales_price#1259], Statistics(sizeInBytes=37.11 GB, isBroadcastable=false)
               :     :  +- Filter (isnotnull(ss_sold_date_sk#1244) && isnotnull(ss_addr_sk#1250)), Statistics(sizeInBytes=228.8 GB, isBroadcastable=false)
               :     :     +- Relation[ss_sold_date_sk#1244,ss_sold_time_sk#1245,ss_item_sk#1246,ss_customer_sk#1247,ss_cdemo_sk#1248,ss_hdemo_sk#1249,ss_addr_sk#1250,ss_store_sk#1251,ss_promo_sk#1252,ss_ticket_number#1253,ss_quantity#1254,ss_wholesale_cost#1255,ss_list_price#1256,ss_sales_price#1257,ss_ext_discount_amt#1258,ss_ext_sales_price#1259,ss_ext_wholesale_cost#1260,ss_ext_list_price#1261,ss_ext_tax#1262,ss_coupon_amt#1263,ss_net_paid#1264,ss_net_paid_inc_tax#1265,ss_net_profit#1266] parquet, Statistics(sizeInBytes=228.8 GB, rowCount=5.760E+9, isBroadcastable=false)
               :     +- Project [d_date_sk#1607, d_year#1613, d_qoy#1617], Statistics(sizeInBytes=124.9 KB, isBroadcastable=false)
               :        +- Filter ((((isnotnull(d_date_sk#1607) && isnotnull(d_year#1613)) && isnotnull(d_qoy#1617)) && (d_qoy#1617 = 2)) && (d_year#1613 = 2000)), Statistics(sizeInBytes=1.805 MB, isBroadcastable=false)
               :           +- Relation[d_date_sk#1607,d_date_id#1608,d_date#1609,d_month_seq#1610,d_week_seq#1611,d_quarter_seq#1612,d_year#1613,d_dow#1614,d_moy#1615,d_dom#1616,d_qoy#1617,d_fy_year#1618,d_fy_quarter_seq#1619,d_fy_week_seq#1620,d_day_name#1621,d_quarter_name#1622,d_holiday#1623,d_weekend#1624,d_following_holiday#1625,d_first_dom#1626,d_last_dom#1627,d_same_day_ly#1628,d_same_day_lq#1629,d_current_day#1630,... 4 more fields] parquet, Statistics(sizeInBytes=1.805 MB, rowCount=7.305E+4, isBroadcastable=false)
...

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SparkQA commented Jan 24, 2017

Test build #71906 has finished for PR 16594 at commit 0af8d7f.

  • This patch fails Spark unit tests.
  • This patch merges cleanly.
  • This patch adds no public classes.

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We are having bytesToString in Utils.scala

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That method only accepts Long parameter, and estimated stats can still be unreadable even when using TB as unit.

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I'll try to use that method in combination with current logic, thanks for reminding

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gatorsmile commented Jan 24, 2017

I still do not think using an internal configuration is a user friendly way to show the plan costs. Basically, using this way, we are not wanting users to see it. When CBO is introduced, we should let users see it. The traditional RDBMS admins might not expect such a design

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wzhfy commented Jan 24, 2017

@gatorsmile I just did a quick fix to show how the improved stats look like. If @rxin @hvanhovell accept the change proposed in this pr, I'll update to remove the flag :)

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SparkQA commented Jan 24, 2017

Test build #71921 has started for PR 16594 at commit bd45854.

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rxin commented Feb 7, 2017

ok here is an idea

how about

explain stats xxx

as the way to add stats?

we already have explain codegen.

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I like the idea proposed by rxin

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me 2.

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wzhfy commented Feb 7, 2017

ok I'll modify it with this new command.

decimalValue.toString() + " B"
}
} else {
decimalValue.toString()
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Always represent it using scientific notation? Or only do it when the number is too large?

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https://en.wikipedia.org/wiki/Metric_prefix

Even if we do not have a unit, we still can use K, M, G, T, P, E?

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I'm not sure, will that be more readable than scientific notation if no unit?

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With or without units, the readability is the same, right? If we make them consistent, the impl of def format(number: BigInt) will look much cleaner.

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We can't make them consistent here, because unit string is added inside Utils.bytesToString.
How about move the logic for size into Utils.bytesToString and make it support BigInt?
Then we can remove def format:

  def simpleString: String = {
    Seq(s"sizeInBytes=${Utils.bytesToString(sizeInBytes)}",
      if (rowCount.isDefined) {
        // Show row count in scientific notation.
        s"rowCount=${BigDecimal(rowCount.get, new MathContext(3, RoundingMode.HALF_UP)).toString()}"
      } else {
        ""
      },
      s"isBroadcastable=$isBroadcastable"
    ).filter(_.nonEmpty).mkString(", ")
  }


def toStringWithStats: String = completeString(appendStats = true)

def completeString(appendStats: Boolean): String = {
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private?

} else if (isExplainableStatement(statement)) {
ExplainCommand(statement, extended = ctx.EXTENDED != null, codegen = ctx.CODEGEN != null)
ExplainCommand(statement, extended = ctx.EXTENDED != null, codegen = ctx.CODEGEN != null,
cost = ctx.COST != null)
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Need to fix the style.

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Can you give a clue on the style?

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      ExplainCommand(
        statement,
        extended = ctx.EXTENDED != null,
        codegen = ctx.CODEGEN != null,
        cost = ctx.COST != null)

extended: Boolean = false,
codegen: Boolean = false)
codegen: Boolean = false,
cost: Boolean = false)
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Please add @parm like the other parameters

def format(number: BigInt, isSize: Boolean): String = {
val decimalValue = BigDecimal(number, new MathContext(3, RoundingMode.HALF_UP))
if (isSize) {
// The largest unit in Utils.bytesToString is TB
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How about improving bytesToString and make it support PB or higher?

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yea, I also think TB is a little small

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SparkQA commented Feb 21, 2017

Test build #73200 has finished for PR 16594 at commit 491ec8f.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds no public classes.

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Test build #73295 has finished for PR 16594 at commit b3457a0.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds no public classes.

FORMAT: 'FORMAT';
LOGICAL: 'LOGICAL';
CODEGEN: 'CODEGEN';
COST: 'COST';
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also put in it nonReserved

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Yes. Also please update the hiveNonReservedKeyword in TableIdentifierParserSuite

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Thanks! Updated.

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LGTM except one comment

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SparkQA commented Feb 24, 2017

Test build #73402 has started for PR 16594 at commit 6e10f84.

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LGTM, pending test

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wzhfy commented Feb 24, 2017

retest this please

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SparkQA commented Feb 24, 2017

Test build #73419 has finished for PR 16594 at commit 6e10f84.

  • This patch passes all tests.
  • This patch merges cleanly.
  • This patch adds no public classes.

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thanks, merging to master!

@asfgit asfgit closed this in 69d0da6 Feb 24, 2017
Yunni pushed a commit to Yunni/spark that referenced this pull request Feb 27, 2017
## What changes were proposed in this pull request?

Currently we can only check the estimated stats in logical plans by debugging. We need to provide an easier and more efficient way for developers/users.

In this pr, we add EXPLAIN COST command to show stats in the optimized logical plan.
E.g.
```
spark-sql> EXPLAIN COST select count(1) from store_returns;

...
== Optimized Logical Plan ==
Aggregate [count(1) AS count(1)#24L], Statistics(sizeInBytes=16.0 B, rowCount=1, isBroadcastable=false)
+- Project, Statistics(sizeInBytes=4.3 GB, rowCount=5.76E+8, isBroadcastable=false)
   +- Relation[sr_returned_date_sk#3,sr_return_time_sk#4,sr_item_sk#5,sr_customer_sk#6,sr_cdemo_sk#7,sr_hdemo_sk#8,sr_addr_sk#9,sr_store_sk#10,sr_reason_sk#11,sr_ticket_number#12,sr_return_quantity#13,sr_return_amt#14,sr_return_tax#15,sr_return_amt_inc_tax#16,sr_fee#17,sr_return_ship_cost#18,sr_refunded_cash#19,sr_reversed_charge#20,sr_store_credit#21,sr_net_loss#22] parquet, Statistics(sizeInBytes=28.6 GB, rowCount=5.76E+8, isBroadcastable=false)
...
```

## How was this patch tested?

Add test cases.

Author: wangzhenhua <[email protected]>
Author: Zhenhua Wang <[email protected]>

Closes apache#16594 from wzhfy/showStats.
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