使用append增加数据插入速度
/*+APPEND*/作用表高水位配空间,再使用表extent空余空间
append 属于direct insert,归档模式append+table nologging量减少志,非归档模式append量减少志,append式插入产少undo
寻找 freelist free block , 直接table HWM 面加入数据！
//最快的方法 10000记录 23MSpublic static void insert() {
// 开时时间
Long begin = new Date().getTime()
// sql前缀
String prefix = "INSERT INTO tb_big_data (count, create_time, random) VALUES "
try {
// 保存sql后缀
StringBuffer suffix = new StringBuffer()
// 设置事务为非自动提交
conn.setAutoCommit(false)
// Statement st = conn.createStatement()
// 比起st，pst会更好些
PreparedStatement pst = conn.prepareStatement("")
// 外层循环，总提交事务次数
for (int i = 1 i <= 100 i++) {
// 第次提交步长
for (int j = 1 j <= 10000 j++) {
// 构建sql后缀
suffix.append("(" + j * i + ", SYSDATE(), " + i * j
* Math.random() + "),")
}
// 构建完整sql
String sql = prefix + suffix.substring(0, suffix.length() - 1)
// 添加执行sql
pst.addBatch(sql)
// 执行 *** 作
pst.executeBatch()
// 提交事务
conn.commit()
// 清空上一次添加的数据
suffix = new StringBuffer()
}
// 头等连接
pst.close()
conn.close()
} catch (SQLException e) {
e.printStackTrace()
}
// 结束时间
Long end = new Date().getTime()
// 耗时
System.out.println("cast : " + (end - begin) / 1000 + " ms")
}
我们知道，JSON是一种轻量级的数据交互的格式，大部分NO SQL数据库的存储都用JSON。MySQL从5.7开始支持JSON格式的数据存储，并且新增了很多JSON相关函数。MySQL 8.0 又带来了一个新的把JSON转换为TABLE的函数JSON_TABLE，实现了JSON到表的转换。
举例一
我们看下简单的例子：
简单定义一个两级JSON 对象
mysql>set @ytt='{"name":[{"a":"ytt","b":"action"}, {"a":"dble","b":"shard"},{"a":"mysql","b":"oracle"}]}'Query OK, 0 rows affected (0.00 sec)
第一级：
mysql>select json_keys(@ytt)+-----------------+| json_keys(@ytt) |+-----------------+| ["name"] |+-----------------+1 row in set (0.00 sec)
第二级：
mysql>select json_keys(@ytt,'$.name[0]')+-----------------------------+| json_keys(@ytt,'$.name[0]') |+-----------------------------+| ["a", "b"] |+-----------------------------+1 row in set (0.00 sec)
我们使用MySQL 8.0 的JSON_TABLE 来转换 @ytt。
mysql>select * from json_table(@ytt,'$.name[*]' columns (f1 varchar(10) path '$.a', f2 varchar(10) path '$.b')) as tt
+-------+--------+
| f1 | f2 |
+-------+--------+
| ytt | action |
| dble | shard |
| mysql | oracle |
+-------+--------+
3 rows in set (0.00 sec)
举例二
再来一个复杂点的例子，用的是EXPLAIN 的JSON结果集。
JSON 串 @json_str1。
set @json_str1 = ' { "query_block": { "select_id": 1, "cost_info": { "query_cost": "1.00" }, "table": { "table_name": "bigtable", "access_type": "const", "possible_keys": [ "id" ], "key": "id", "used_key_parts": [ "id" ], "key_length": "8", "ref": [ "const" ], "rows_examined_per_scan": 1, "rows_produced_per_join": 1, "filtered": "100.00", "cost_info": { "read_cost": "0.00", "eval_cost": "0.20", "prefix_cost": "0.00", "data_read_per_join": "176" }, "used_columns": [ "id", "log_time", "str1", "str2" ] } }}'
第一级：
mysql>select json_keys(@json_str1) as 'first_object'+-----------------+| first_object |+-----------------+| ["query_block"] |+-----------------+1 row in set (0.00 sec)
第二级：
mysql>select json_keys(@json_str1,'$.query_block') as 'second_object'+-------------------------------------+| second_object |+-------------------------------------+| ["table", "cost_info", "select_id"] |+-------------------------------------+1 row in set (0.00 sec)
第三级：
mysql> select json_keys(@json_str1,'$.query_block.table') as 'third_object'\G*************************** 1. row ***************************third_object: ["key","ref","filtered","cost_info","key_length","table_name","access_type","used_columns","possible_keys","used_key_parts","rows_examined_per_scan","rows_produced_per_join"]1 row in set (0.01 sec)
第四级：
mysql>select json_extract(@json_str1,'$.query_block.table.cost_info') as 'forth_object'\G*************************** 1. row ***************************forth_object: {"eval_cost":"0.20","read_cost":"0.00","prefix_cost":"0.00","data_read_per_join":"176"}1 row in set (0.00 sec)
那我们把这个JSON 串转换为表。
SELECT * FROM JSON_TABLE(@json_str1,
"$.query_block"
COLUMNS(
rowid FOR ORDINALITY,
NESTED PATH '$.table'
COLUMNS (
a1_1 varchar(100) PATH '$.key',
a1_2 varchar(100) PATH '$.ref[0]',
a1_3 varchar(100) PATH '$.filtered',
nested path '$.cost_info'
columns (
a2_1 varchar(100) PATH '$.eval_cost' ,
a2_2 varchar(100) PATH '$.read_cost',
a2_3 varchar(100) PATH '$.prefix_cost',
a2_4 varchar(100) PATH '$.data_read_per_join'
),
a3 varchar(100) PATH '$.key_length',
a4 varchar(100) PATH '$.table_name',
a5 varchar(100) PATH '$.access_type',
a6 varchar(100) PATH '$.used_key_parts[0]',
a7 varchar(100) PATH '$.rows_examined_per_scan',
a8 varchar(100) PATH '$.rows_produced_per_join',
a9 varchar(100) PATH '$.key'
),
NESTED PATH '$.cost_info'
columns (
b1_1 varchar(100) path '$.query_cost'
),
c INT path "$.select_id"
)
) AS tt
+-------+------+-------+--------+------+------+------+------+------+----------+-------+------+------+------+------+------+------+
| rowid | a1_1 | a1_2 | a1_3 | a2_1 | a2_2 | a2_3 | a2_4 | a3 | a4 | a5 | a6 | a7 | a8 | a9 | b1_1 | c |
+-------+------+-------+--------+------+------+------+------+------+----------+-------+------+------+------+------+------+------+
| 1 | id | const | 100.00 | 0.20 | 0.00 | 0.00 | 176 | 8 | bigtable | const | id | 1 | 1 | id | NULL | 1 |
| 1 | NULL | NULL | NULL | NULL | NULL | NULL | NULL | NULL | NULL | NULL | NULL | NULL | NULL | NULL | 1.00 | 1 |
+-------+------+-------+--------+------+------+------+------+------+----------+-------+------+------+------+------+------+------+
2 rows in set (0.00 sec)
当然，JSON_table 函数还有其他的用法，我这里不一一列举了，详细的参考手册。
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