A database table can store different types of data and sometimes we need to transform row-level data into column-level data. This problem can be solved by using the PIVOT() function. This function is used to rotate rows of a table into column values.
Some databases like Microsoft SQL Server or Oracle come with inbuilt functionality to create a pivot table using the inbuilt pivot() function. However, this function is not available in some databases such as MySQL and MariaDB.
I'm going to add a somewhat longer and more detailed explanation of the steps to take to solve this problem. I apologize if it's too long.
I'll start out with the base you've given and use it to define a couple of terms that I'll use for the rest of this post. This will be the base table:
select * from history;
+--------+----------+-----------+
| hostid | itemname | itemvalue |
+--------+----------+-----------+
|      1 | A        |        10 |
|      1 | B        |         3 |
|      2 | A        |         9 |
|      2 | C        |        40 |
+--------+----------+-----------+
This will be our goal, the pretty pivot table:
select * from history_itemvalue_pivot;
+--------+------+------+------+
| hostid | A    | B    | C    |
+--------+------+------+------+
|      1 |   10 |    3 |    0 |
|      2 |    9 |    0 |   40 |
+--------+------+------+------+
Values in the history.hostid column will become y-values in the pivot table.  Values in the history.itemname column will become x-values (for obvious reasons).
When I have to solve the problem of creating a pivot table, I tackle it using a three-step process (with an optional fourth step):
Let's apply these steps to your problem and see what we get:
Step 1: select columns of interest.  In the desired result, hostid provides the y-values and itemname provides the x-values.
Step 2: extend the base table with extra columns.  We typically need one column per x-value.  Recall that our x-value column is itemname: 
create view history_extended as (
  select
    history.*,
    case when itemname = "A" then itemvalue end as A,
    case when itemname = "B" then itemvalue end as B,
    case when itemname = "C" then itemvalue end as C
  from history
);
select * from history_extended;
+--------+----------+-----------+------+------+------+
| hostid | itemname | itemvalue | A    | B    | C    |
+--------+----------+-----------+------+------+------+
|      1 | A        |        10 |   10 | NULL | NULL |
|      1 | B        |         3 | NULL |    3 | NULL |
|      2 | A        |         9 |    9 | NULL | NULL |
|      2 | C        |        40 | NULL | NULL |   40 |
+--------+----------+-----------+------+------+------+
Note that we didn't change the number of rows -- we just added extra columns.  Also note the pattern of NULLs -- a row with itemname = "A" has a non-null value for new column A, and null values for the other new columns.
Step 3: group and aggregate the extended table.  We need to group by hostid, since it provides the y-values:
create view history_itemvalue_pivot as (
  select
    hostid,
    sum(A) as A,
    sum(B) as B,
    sum(C) as C
  from history_extended
  group by hostid
);
select * from history_itemvalue_pivot;
+--------+------+------+------+
| hostid | A    | B    | C    |
+--------+------+------+------+
|      1 |   10 |    3 | NULL |
|      2 |    9 | NULL |   40 |
+--------+------+------+------+
(Note that we now have one row per y-value.)  Okay, we're almost there!  We just need to get rid of those ugly NULLs.
Step 4: prettify. We're just going to replace any null values with zeroes so the result set is nicer to look at:
create view history_itemvalue_pivot_pretty as (
  select 
    hostid, 
    coalesce(A, 0) as A, 
    coalesce(B, 0) as B, 
    coalesce(C, 0) as C 
  from history_itemvalue_pivot 
);
select * from history_itemvalue_pivot_pretty;
+--------+------+------+------+
| hostid | A    | B    | C    |
+--------+------+------+------+
|      1 |   10 |    3 |    0 |
|      2 |    9 |    0 |   40 |
+--------+------+------+------+
And we're done -- we've built a nice, pretty pivot table using MySQL.
Considerations when applying this procedure:
itemvalue in this exampleNULL, but it could also be 0 or "", depending on your exact situationsum, but count and max are also often used (max is often used when building one-row "objects" that had been spread across many rows)group by clause (and don't forget to select them)Known limitations:
SELECT 
    hostid, 
    sum( if( itemname = 'A', itemvalue, 0 ) ) AS A,  
    sum( if( itemname = 'B', itemvalue, 0 ) ) AS B, 
    sum( if( itemname = 'C', itemvalue, 0 ) ) AS C 
FROM 
    bob 
GROUP BY 
    hostid;
    Another option,especially useful if you have many items you need to pivot is to let mysql build the query for you:
SELECT
  GROUP_CONCAT(DISTINCT
    CONCAT(
      'ifnull(SUM(case when itemname = ''',
      itemname,
      ''' then itemvalue end),0) AS `',
      itemname, '`'
    )
  ) INTO @sql
FROM
  history;
SET @sql = CONCAT('SELECT hostid, ', @sql, ' 
                  FROM history 
                   GROUP BY hostid');
PREPARE stmt FROM @sql;
EXECUTE stmt;
DEALLOCATE PREPARE stmt;
FIDDLE Added some extra values to see it working
GROUP_CONCAT has a default value of 1000 so if you have a really big query change this parameter before running it
SET SESSION group_concat_max_len = 1000000;
Test:
DROP TABLE IF EXISTS history;
CREATE TABLE history
(hostid INT,
itemname VARCHAR(5),
itemvalue INT);
INSERT INTO history VALUES(1,'A',10),(1,'B',3),(2,'A',9),
(2,'C',40),(2,'D',5),
(3,'A',14),(3,'B',67),(3,'D',8);
  hostid    A     B     C      D
    1     10      3     0      0
    2     9       0    40      5
    3     14     67     0      8
    Taking advantage of Matt Fenwick's idea that helped me to solve the problem (a lot of thanks), let's reduce it to only one query:
select
    history.*,
    coalesce(sum(case when itemname = "A" then itemvalue end), 0) as A,
    coalesce(sum(case when itemname = "B" then itemvalue end), 0) as B,
    coalesce(sum(case when itemname = "C" then itemvalue end), 0) as C
from history
group by hostid
    I edit Agung Sagita's answer from subquery to join. I'm not sure about how much difference between this 2 way, but just for another reference.
SELECT  hostid, T2.VALUE AS A, T3.VALUE AS B, T4.VALUE AS C
FROM TableTest AS T1
LEFT JOIN TableTest T2 ON T2.hostid=T1.hostid AND T2.ITEMNAME='A'
LEFT JOIN TableTest T3 ON T3.hostid=T1.hostid AND T3.ITEMNAME='B'
LEFT JOIN TableTest T4 ON T4.hostid=T1.hostid AND T4.ITEMNAME='C'
    
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