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fix merge problem
This commit is contained in:
csunny
commit
1b70b5f92b
25
.vscode/launch.json
vendored
Normal file
25
.vscode/launch.json
vendored
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@ -0,0 +1,25 @@
|
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{
|
||||
// Use IntelliSense to learn about possible attributes.
|
||||
// Hover to view descriptions of existing attributes.
|
||||
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
|
||||
"version": "0.2.0",
|
||||
"configurations": [
|
||||
{
|
||||
"name": "Python: Current File",
|
||||
"type": "python",
|
||||
"request": "launch",
|
||||
"program": "${file}",
|
||||
"console": "integratedTerminal",
|
||||
"justMyCode": true,
|
||||
"env": {"PYTHONPATH": "${workspaceFolder}"},
|
||||
"envFile": "${workspaceFolder}/.env"
|
||||
},
|
||||
{
|
||||
"name": "Python: Module",
|
||||
"type": "python",
|
||||
"request": "launch",
|
||||
"module": "pilot",
|
||||
"justMyCode": true,
|
||||
}
|
||||
]
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||||
}
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240
examples/t5_example.py
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240
examples/t5_example.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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from llama_index import SimpleDirectoryReader, LangchainEmbedding, GPTListIndex, GPTSimpleVectorIndex, PromptHelper
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from llama_index import LLMPredictor
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import torch
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from langchain.llms.base import LLM
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from transformers import pipeline
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class FlanLLM(LLM):
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model_name = "google/flan-t5-large"
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pipeline = pipeline("text2text-generation", model=model_name, device=0, model_kwargs={
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"torch_dtype": torch.bfloat16
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})
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def _call(self, prompt, stop=None):
|
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return self.pipeline(prompt, max_length=9999)[0]["generated_text"]
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|
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def _identifying_params(self):
|
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return {"name_of_model": self.model_name}
|
||||
|
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def _llm_type(self):
|
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return "custome"
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||||
|
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llm_predictor = LLMPredictor(llm=FlanLLM())
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hfemb = HuggingFaceEmbeddings()
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embed_model = LangchainEmbedding(hfemb)
|
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|
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text1 = """
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执行计划是对一条 SQL 查询语句在数据库中执行过程的描述。用户可以通过 EXPLAIN 命令查看优化器针对指定 SQL 生成的逻辑执行计划。
|
||||
|
||||
如果要分析某条 SQL 的性能问题，通常需要先查看 SQL 的执行计划，排查每一步 SQL 执行是否存在问题。所以读懂执行计划是 SQL 优化的先决条件，而了解执行计划的算子是理解 EXPLAIN 命令的关键。
|
||||
|
||||
OceanBase 数据库的执行计划命令有三种模式：EXPLAIN BASIC、EXPLAIN 和 EXPLAIN EXTENDED。这三种模式对执行计划展现不同粒度的细节信息:
|
||||
|
||||
EXPLAIN BASIC 命令用于最基本的计划展示。
|
||||
|
||||
EXPLAIN EXTENDED 命令用于最详细的计划展示（通常在排查问题时使用这种展示模式）。
|
||||
|
||||
EXPLAIN 命令所展示的信息可以帮助普通用户了解整个计划的执行方式。
|
||||
|
||||
EXPLAIN 命令格式如下：
|
||||
EXPLAIN [BASIC | EXTENDED | PARTITIONS | FORMAT = format_name] [PRETTY | PRETTY_COLOR] explainable_stmt
|
||||
format_name:
|
||||
{ TRADITIONAL | JSON }
|
||||
explainable_stmt:
|
||||
{ SELECT statement
|
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| DELETE statement
|
||||
| INSERT statement
|
||||
| REPLACE statement
|
||||
| UPDATE statement }
|
||||
|
||||
|
||||
EXPLAIN 命令适用于 SELECT、DELETE、INSERT、REPLACE 和 UPDATE 语句，显示优化器所提供的有关语句执行计划的信息，包括如何处理该语句，如何联接表以及以何种顺序联接表等信息。
|
||||
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一般来说，可以使用 EXPLAIN EXTENDED 命令，将表扫描的范围段展示出来。使用 EXPLAIN OUTLINE 命令可以显示 Outline 信息。
|
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|
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FORMAT 选项可用于选择输出格式。TRADITIONAL 表示以表格格式显示输出，这也是默认设置。JSON 表示以 JSON 格式显示信息。
|
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使用 EXPLAIN PARTITITIONS 也可用于检查涉及分区表的查询。如果检查针对非分区表的查询，则不会产生错误，但 PARTIONS 列的值始终为 NULL。
|
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对于复杂的执行计划，可以使用 PRETTY 或者 PRETTY_COLOR 选项将计划树中的父节点和子节点使用树线或彩色树线连接起来，使得执行计划展示更方便阅读。示例如下：
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obclient> CREATE TABLE p1table(c1 INT ,c2 INT) PARTITION BY HASH(c1) PARTITIONS 2;
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Query OK, 0 rows affected
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obclient> CREATE TABLE p2table(c1 INT ,c2 INT) PARTITION BY HASH(c1) PARTITIONS 4;
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Query OK, 0 rows affected
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obclient> EXPLAIN EXTENDED PRETTY_COLOR SELECT * FROM p1table p1 JOIN p2table p2 ON p1.c1=p2.c2\G
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*************************** 1. row ***************************
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Query Plan: ==========================================================
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|ID|OPERATOR |NAME |EST. ROWS|COST|
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----------------------------------------------------------
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|0 |PX COORDINATOR | |1 |278 |
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|1 | EXCHANGE OUT DISTR |:EX10001|1 |277 |
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|2 | HASH JOIN | |1 |276 |
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|3 | ├PX PARTITION ITERATOR | |1 |92 |
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|4 | │ TABLE SCAN |P1 |1 |92 |
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|5 | └EXCHANGE IN DISTR | |1 |184 |
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|6 | EXCHANGE OUT DISTR (PKEY)|:EX10000|1 |184 |
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|7 | PX PARTITION ITERATOR | |1 |183 |
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|8 | TABLE SCAN |P2 |1 |183 |
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==========================================================
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Outputs & filters:
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-------------------------------------
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0 - output([INTERNAL_FUNCTION(P1.C1, P1.C2, P2.C1, P2.C2)]), filter(nil)
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1 - output([INTERNAL_FUNCTION(P1.C1, P1.C2, P2.C1, P2.C2)]), filter(nil), dop=1
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2 - output([P1.C1], [P2.C2], [P1.C2], [P2.C1]), filter(nil),
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equal_conds([P1.C1 = P2.C2]), other_conds(nil)
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3 - output([P1.C1], [P1.C2]), filter(nil)
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4 - output([P1.C1], [P1.C2]), filter(nil),
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access([P1.C1], [P1.C2]), partitions(p[0-1])
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5 - output([P2.C2], [P2.C1]), filter(nil)
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6 - (#keys=1, [P2.C2]), output([P2.C2], [P2.C1]), filter(nil), dop=1
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7 - output([P2.C1], [P2.C2]), filter(nil)
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8 - output([P2.C1], [P2.C2]), filter(nil),
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access([P2.C1], [P2.C2]), partitions(p[0-3])
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1 row in set
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## 执行计划形状与算子信息
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在数据库系统中，执行计划在内部通常是以树的形式来表示的，但是不同的数据库会选择不同的方式展示给用户。
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如下示例分别为 PostgreSQL 数据库、Oracle 数据库和 OceanBase 数据库对于 TPCDS Q3 的计划展示。
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```sql
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obclient> SELECT /*TPC-DS Q3*/ *
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FROM (SELECT dt.d_year,
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item.i_brand_id brand_id,
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item.i_brand brand,
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Sum(ss_net_profit) sum_agg
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FROM date_dim dt,
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store_sales,
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item
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WHERE dt.d_date_sk = store_sales.ss_sold_date_sk
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AND store_sales.ss_item_sk = item.i_item_sk
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AND item.i_manufact_id = 914
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AND dt.d_moy = 11
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GROUP BY dt.d_year,
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item.i_brand,
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item.i_brand_id
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ORDER BY dt.d_year,
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sum_agg DESC,
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brand_id)
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WHERE ROWNUM <= 100;
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PostgreSQL 数据库执行计划展示如下：
|
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Limit (cost=13986.86..13987.20 rows=27 width=91)
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Sort (cost=13986.86..13986.93 rows=27 width=65)
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Sort Key: dt.d_year, (sum(store_sales.ss_net_profit)), item.i_brand_id
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HashAggregate (cost=13985.95..13986.22 rows=27 width=65)
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Merge Join (cost=13884.21..13983.91 rows=204 width=65)
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Merge Cond: (dt.d_date_sk = store_sales.ss_sold_date_sk)
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Index Scan using date_dim_pkey on date_dim dt (cost=0.00..3494.62 rows=6080 width=8)
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Filter: (d_moy = 11)
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Sort (cost=12170.87..12177.27 rows=2560 width=65)
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Sort Key: store_sales.ss_sold_date_sk
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Nested Loop (cost=6.02..12025.94 rows=2560 width=65)
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Seq Scan on item (cost=0.00..1455.00 rows=16 width=59)
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Filter: (i_manufact_id = 914)
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Bitmap Heap Scan on store_sales (cost=6.02..658.94 rows=174 width=14)
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Recheck Cond: (ss_item_sk = item.i_item_sk)
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Bitmap Index Scan on store_sales_pkey (cost=0.00..5.97 rows=174 width=0)
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Index Cond: (ss_item_sk = item.i_item_sk)
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|
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|
||||
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Oracle 数据库执行计划展示如下：
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Plan hash value: 2331821367
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--------------------------------------------------------------------------------------------------
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| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
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--------------------------------------------------------------------------------------------------
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| 0 | SELECT STATEMENT | | 100 | 9100 | 3688 (1)| 00:00:01 |
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||||
|* 1 | COUNT STOPKEY | | | | | |
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| 2 | VIEW | | 2736 | 243K| 3688 (1)| 00:00:01 |
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|* 3 | SORT ORDER BY STOPKEY | | 2736 | 256K| 3688 (1)| 00:00:01 |
|
||||
| 4 | HASH GROUP BY | | 2736 | 256K| 3688 (1)| 00:00:01 |
|
||||
|* 5 | HASH JOIN | | 2736 | 256K| 3686 (1)| 00:00:01 |
|
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|* 6 | TABLE ACCESS FULL | DATE_DIM | 6087 | 79131 | 376 (1)| 00:00:01 |
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||||
| 7 | NESTED LOOPS | | 2865 | 232K| 3310 (1)| 00:00:01 |
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||||
| 8 | NESTED LOOPS | | 2865 | 232K| 3310 (1)| 00:00:01 |
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|* 9 | TABLE ACCESS FULL | ITEM | 18 | 1188 | 375 (0)| 00:00:01 |
|
||||
|* 10 | INDEX RANGE SCAN | SYS_C0010069 | 159 | | 2 (0)| 00:00:01 |
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||||
| 11 | TABLE ACCESS BY INDEX ROWID| STORE_SALES | 159 | 2703 | 163 (0)| 00:00:01 |
|
||||
--------------------------------------------------------------------------------------------------
|
||||
|
||||
OceanBase 数据库执行计划展示如下：
|
||||
|ID|OPERATOR |NAME |EST. ROWS|COST |
|
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-------------------------------------------------------
|
||||
|0 |LIMIT | |100 |81141|
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||||
|1 | TOP-N SORT | |100 |81127|
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|2 | HASH GROUP BY | |2924 |68551|
|
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|3 | HASH JOIN | |2924 |65004|
|
||||
|4 | SUBPLAN SCAN |VIEW1 |2953 |19070|
|
||||
|5 | HASH GROUP BY | |2953 |18662|
|
||||
|6 | NESTED-LOOP JOIN| |2953 |15080|
|
||||
|7 | TABLE SCAN |ITEM |19 |11841|
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||||
|8 | TABLE SCAN |STORE_SALES|161 |73 |
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||||
|9 | TABLE SCAN |DT |6088 |29401|
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=======================================================
|
||||
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由示例可见，OceanBase 数据库的计划展示与 Oracle 数据库类似。
|
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|
||||
OceanBase 数据库执行计划中的各列的含义如下：
|
||||
列名 含义
|
||||
ID 执行树按照前序遍历的方式得到的编号（从 0 开始）。
|
||||
OPERATOR 操作算子的名称。
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NAME 对应表操作的表名（索引名）。
|
||||
EST. ROWS 估算该操作算子的输出行数。
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||||
COST 该操作算子的执行代价（微秒）。
|
||||
|
||||
|
||||
OceanBase 数据库 EXPLAIN 命令输出的第一部分是执行计划的树形结构展示。其中每一个操作在树中的层次通过其在 operator 中的缩进予以展示，层次最深的优先执行，层次相同的以特定算子的执行顺序为标准来执行。
|
||||
|
||||
问题: update a not exists (b…)
|
||||
我一开始以为 B是驱动表，B的数据挺多的 后来看到NLAJ，是说左边的表关联右边的表
|
||||
所以这个的驱动表是不是实际是A，用A的匹配B的，这个理解有问题吗
|
||||
|
||||
回答: 没错 A 驱动 B的
|
||||
|
||||
问题: 光知道最下最右的是驱动表了 所以一开始搞得有点懵 :sweat_smile:
|
||||
|
||||
回答: nlj应该原理应该都是左表(驱动表)的记录探测右表(被驱动表)， 选哪张成为左表或右表就基于一些其他考量了，比如数据量， 而anti join/semi join只是对 not exist/exist的一种优化，相关的原理和资料网上可以查阅一下
|
||||
|
||||
问题: 也就是nlj 就是按照之前理解的谁先执行 谁就是驱动表 也就是执行计划中的最右的表
|
||||
而anti join/semi join，谁在not exist左面，谁就是驱动表。这么理解对吧
|
||||
|
||||
回答: nlj也是左表的表是驱动表，这个要了解下计划执行方面的基本原理，取左表的一行数据，再遍历右表，一旦满足连接条件，就可以返回数据
|
||||
anti/semi只是因为not exists/exist的语义只是返回左表数据，改成anti join是一种计划优化，连接的方式比子查询更优
|
||||
"""
|
||||
|
||||
from llama_index import Document
|
||||
text_list = [text1]
|
||||
documents = [Document(t) for t in text_list]
|
||||
|
||||
num_output = 250
|
||||
max_input_size = 512
|
||||
|
||||
max_chunk_overlap = 20
|
||||
prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap)
|
||||
|
||||
index = GPTListIndex(documents, embed_model=embed_model, llm_predictor=llm_predictor, prompt_helper=prompt_helper)
|
||||
index.save_to_disk("index.json")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import logging
|
||||
logging.getLogger().setLevel(logging.CRITICAL)
|
||||
for d in documents:
|
||||
print(d)
|
||||
|
||||
response = index.query("数据库的执行计划命令有多少?")
|
||||
print(response)
|
||||
@ -1,3 +1 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
__version__ = "0.0.1"
|
||||
|
||||
247
pilot/app.py
247
pilot/app.py
@ -1,241 +1,18 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding:utf-8 -*-
|
||||
|
||||
from llama_index import SimpleDirectoryReader, LangchainEmbedding, GPTListIndex, GPTSimpleVectorIndex, PromptHelper
|
||||
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
|
||||
from llama_index import LLMPredictor
|
||||
import torch
|
||||
from langchain.llms.base import LLM
|
||||
from transformers import pipeline
|
||||
|
||||
from langchain.agents import (
|
||||
load_tools,
|
||||
initialize_agent,
|
||||
AgentType
|
||||
)
|
||||
|
||||
class FlanLLM(LLM):
|
||||
model_name = "google/flan-t5-large"
|
||||
pipeline = pipeline("text2text-generation", model=model_name, device=0, model_kwargs={
|
||||
"torch_dtype": torch.bfloat16
|
||||
})
|
||||
from pilot.model.vicuna_llm import VicunaRequestLLM, VicunaEmbeddingLLM
|
||||
llm = VicunaRequestLLM()
|
||||
|
||||
def _call(self, prompt, stop=None):
|
||||
return self.pipeline(prompt, max_length=9999)[0]["generated_text"]
|
||||
|
||||
def _identifying_params(self):
|
||||
return {"name_of_model": self.model_name}
|
||||
|
||||
def _llm_type(self):
|
||||
return "custome"
|
||||
|
||||
llm_predictor = LLMPredictor(llm=FlanLLM())
|
||||
hfemb = HuggingFaceEmbeddings()
|
||||
embed_model = LangchainEmbedding(hfemb)
|
||||
|
||||
text1 = """
|
||||
执行计划是对一条 SQL 查询语句在数据库中执行过程的描述。用户可以通过 EXPLAIN 命令查看优化器针对指定 SQL 生成的逻辑执行计划。
|
||||
|
||||
如果要分析某条 SQL 的性能问题，通常需要先查看 SQL 的执行计划，排查每一步 SQL 执行是否存在问题。所以读懂执行计划是 SQL 优化的先决条件，而了解执行计划的算子是理解 EXPLAIN 命令的关键。
|
||||
|
||||
OceanBase 数据库的执行计划命令有三种模式：EXPLAIN BASIC、EXPLAIN 和 EXPLAIN EXTENDED。这三种模式对执行计划展现不同粒度的细节信息:
|
||||
|
||||
EXPLAIN BASIC 命令用于最基本的计划展示。
|
||||
|
||||
EXPLAIN EXTENDED 命令用于最详细的计划展示（通常在排查问题时使用这种展示模式）。
|
||||
|
||||
EXPLAIN 命令所展示的信息可以帮助普通用户了解整个计划的执行方式。
|
||||
|
||||
EXPLAIN 命令格式如下：
|
||||
EXPLAIN [BASIC | EXTENDED | PARTITIONS | FORMAT = format_name] [PRETTY | PRETTY_COLOR] explainable_stmt
|
||||
format_name:
|
||||
{ TRADITIONAL | JSON }
|
||||
explainable_stmt:
|
||||
{ SELECT statement
|
||||
| DELETE statement
|
||||
| INSERT statement
|
||||
| REPLACE statement
|
||||
| UPDATE statement }
|
||||
|
||||
|
||||
EXPLAIN 命令适用于 SELECT、DELETE、INSERT、REPLACE 和 UPDATE 语句，显示优化器所提供的有关语句执行计划的信息，包括如何处理该语句，如何联接表以及以何种顺序联接表等信息。
|
||||
|
||||
一般来说，可以使用 EXPLAIN EXTENDED 命令，将表扫描的范围段展示出来。使用 EXPLAIN OUTLINE 命令可以显示 Outline 信息。
|
||||
|
||||
FORMAT 选项可用于选择输出格式。TRADITIONAL 表示以表格格式显示输出，这也是默认设置。JSON 表示以 JSON 格式显示信息。
|
||||
|
||||
使用 EXPLAIN PARTITITIONS 也可用于检查涉及分区表的查询。如果检查针对非分区表的查询，则不会产生错误，但 PARTIONS 列的值始终为 NULL。
|
||||
|
||||
对于复杂的执行计划，可以使用 PRETTY 或者 PRETTY_COLOR 选项将计划树中的父节点和子节点使用树线或彩色树线连接起来，使得执行计划展示更方便阅读。示例如下：
|
||||
obclient> CREATE TABLE p1table(c1 INT ,c2 INT) PARTITION BY HASH(c1) PARTITIONS 2;
|
||||
Query OK, 0 rows affected
|
||||
|
||||
obclient> CREATE TABLE p2table(c1 INT ,c2 INT) PARTITION BY HASH(c1) PARTITIONS 4;
|
||||
Query OK, 0 rows affected
|
||||
|
||||
obclient> EXPLAIN EXTENDED PRETTY_COLOR SELECT * FROM p1table p1 JOIN p2table p2 ON p1.c1=p2.c2\G
|
||||
*************************** 1. row ***************************
|
||||
Query Plan: ==========================================================
|
||||
|ID|OPERATOR |NAME |EST. ROWS|COST|
|
||||
----------------------------------------------------------
|
||||
|0 |PX COORDINATOR | |1 |278 |
|
||||
|1 | EXCHANGE OUT DISTR |:EX10001|1 |277 |
|
||||
|2 | HASH JOIN | |1 |276 |
|
||||
|3 | ├PX PARTITION ITERATOR | |1 |92 |
|
||||
|4 | │ TABLE SCAN |P1 |1 |92 |
|
||||
|5 | └EXCHANGE IN DISTR | |1 |184 |
|
||||
|6 | EXCHANGE OUT DISTR (PKEY)|:EX10000|1 |184 |
|
||||
|7 | PX PARTITION ITERATOR | |1 |183 |
|
||||
|8 | TABLE SCAN |P2 |1 |183 |
|
||||
==========================================================
|
||||
|
||||
Outputs & filters:
|
||||
-------------------------------------
|
||||
0 - output([INTERNAL_FUNCTION(P1.C1, P1.C2, P2.C1, P2.C2)]), filter(nil)
|
||||
1 - output([INTERNAL_FUNCTION(P1.C1, P1.C2, P2.C1, P2.C2)]), filter(nil), dop=1
|
||||
2 - output([P1.C1], [P2.C2], [P1.C2], [P2.C1]), filter(nil),
|
||||
equal_conds([P1.C1 = P2.C2]), other_conds(nil)
|
||||
3 - output([P1.C1], [P1.C2]), filter(nil)
|
||||
4 - output([P1.C1], [P1.C2]), filter(nil),
|
||||
access([P1.C1], [P1.C2]), partitions(p[0-1])
|
||||
5 - output([P2.C2], [P2.C1]), filter(nil)
|
||||
6 - (#keys=1, [P2.C2]), output([P2.C2], [P2.C1]), filter(nil), dop=1
|
||||
7 - output([P2.C1], [P2.C2]), filter(nil)
|
||||
8 - output([P2.C1], [P2.C2]), filter(nil),
|
||||
access([P2.C1], [P2.C2]), partitions(p[0-3])
|
||||
|
||||
1 row in set
|
||||
|
||||
|
||||
|
||||
|
||||
## 执行计划形状与算子信息
|
||||
|
||||
在数据库系统中，执行计划在内部通常是以树的形式来表示的，但是不同的数据库会选择不同的方式展示给用户。
|
||||
|
||||
如下示例分别为 PostgreSQL 数据库、Oracle 数据库和 OceanBase 数据库对于 TPCDS Q3 的计划展示。
|
||||
|
||||
```sql
|
||||
obclient> SELECT /*TPC-DS Q3*/ *
|
||||
FROM (SELECT dt.d_year,
|
||||
item.i_brand_id brand_id,
|
||||
item.i_brand brand,
|
||||
Sum(ss_net_profit) sum_agg
|
||||
FROM date_dim dt,
|
||||
store_sales,
|
||||
item
|
||||
WHERE dt.d_date_sk = store_sales.ss_sold_date_sk
|
||||
AND store_sales.ss_item_sk = item.i_item_sk
|
||||
AND item.i_manufact_id = 914
|
||||
AND dt.d_moy = 11
|
||||
GROUP BY dt.d_year,
|
||||
item.i_brand,
|
||||
item.i_brand_id
|
||||
ORDER BY dt.d_year,
|
||||
sum_agg DESC,
|
||||
brand_id)
|
||||
WHERE ROWNUM <= 100;
|
||||
|
||||
PostgreSQL 数据库执行计划展示如下：
|
||||
Limit (cost=13986.86..13987.20 rows=27 width=91)
|
||||
Sort (cost=13986.86..13986.93 rows=27 width=65)
|
||||
Sort Key: dt.d_year, (sum(store_sales.ss_net_profit)), item.i_brand_id
|
||||
HashAggregate (cost=13985.95..13986.22 rows=27 width=65)
|
||||
Merge Join (cost=13884.21..13983.91 rows=204 width=65)
|
||||
Merge Cond: (dt.d_date_sk = store_sales.ss_sold_date_sk)
|
||||
Index Scan using date_dim_pkey on date_dim dt (cost=0.00..3494.62 rows=6080 width=8)
|
||||
Filter: (d_moy = 11)
|
||||
Sort (cost=12170.87..12177.27 rows=2560 width=65)
|
||||
Sort Key: store_sales.ss_sold_date_sk
|
||||
Nested Loop (cost=6.02..12025.94 rows=2560 width=65)
|
||||
Seq Scan on item (cost=0.00..1455.00 rows=16 width=59)
|
||||
Filter: (i_manufact_id = 914)
|
||||
Bitmap Heap Scan on store_sales (cost=6.02..658.94 rows=174 width=14)
|
||||
Recheck Cond: (ss_item_sk = item.i_item_sk)
|
||||
Bitmap Index Scan on store_sales_pkey (cost=0.00..5.97 rows=174 width=0)
|
||||
Index Cond: (ss_item_sk = item.i_item_sk)
|
||||
|
||||
|
||||
|
||||
Oracle 数据库执行计划展示如下：
|
||||
Plan hash value: 2331821367
|
||||
--------------------------------------------------------------------------------------------------
|
||||
| Id | Operation | Name | Rows | Bytes | Cost (%CPU)| Time |
|
||||
--------------------------------------------------------------------------------------------------
|
||||
| 0 | SELECT STATEMENT | | 100 | 9100 | 3688 (1)| 00:00:01 |
|
||||
|* 1 | COUNT STOPKEY | | | | | |
|
||||
| 2 | VIEW | | 2736 | 243K| 3688 (1)| 00:00:01 |
|
||||
|* 3 | SORT ORDER BY STOPKEY | | 2736 | 256K| 3688 (1)| 00:00:01 |
|
||||
| 4 | HASH GROUP BY | | 2736 | 256K| 3688 (1)| 00:00:01 |
|
||||
|* 5 | HASH JOIN | | 2736 | 256K| 3686 (1)| 00:00:01 |
|
||||
|* 6 | TABLE ACCESS FULL | DATE_DIM | 6087 | 79131 | 376 (1)| 00:00:01 |
|
||||
| 7 | NESTED LOOPS | | 2865 | 232K| 3310 (1)| 00:00:01 |
|
||||
| 8 | NESTED LOOPS | | 2865 | 232K| 3310 (1)| 00:00:01 |
|
||||
|* 9 | TABLE ACCESS FULL | ITEM | 18 | 1188 | 375 (0)| 00:00:01 |
|
||||
|* 10 | INDEX RANGE SCAN | SYS_C0010069 | 159 | | 2 (0)| 00:00:01 |
|
||||
| 11 | TABLE ACCESS BY INDEX ROWID| STORE_SALES | 159 | 2703 | 163 (0)| 00:00:01 |
|
||||
--------------------------------------------------------------------------------------------------
|
||||
|
||||
OceanBase 数据库执行计划展示如下：
|
||||
|ID|OPERATOR |NAME |EST. ROWS|COST |
|
||||
-------------------------------------------------------
|
||||
|0 |LIMIT | |100 |81141|
|
||||
|1 | TOP-N SORT | |100 |81127|
|
||||
|2 | HASH GROUP BY | |2924 |68551|
|
||||
|3 | HASH JOIN | |2924 |65004|
|
||||
|4 | SUBPLAN SCAN |VIEW1 |2953 |19070|
|
||||
|5 | HASH GROUP BY | |2953 |18662|
|
||||
|6 | NESTED-LOOP JOIN| |2953 |15080|
|
||||
|7 | TABLE SCAN |ITEM |19 |11841|
|
||||
|8 | TABLE SCAN |STORE_SALES|161 |73 |
|
||||
|9 | TABLE SCAN |DT |6088 |29401|
|
||||
=======================================================
|
||||
|
||||
由示例可见，OceanBase 数据库的计划展示与 Oracle 数据库类似。
|
||||
|
||||
OceanBase 数据库执行计划中的各列的含义如下：
|
||||
列名 含义
|
||||
ID 执行树按照前序遍历的方式得到的编号（从 0 开始）。
|
||||
OPERATOR 操作算子的名称。
|
||||
NAME 对应表操作的表名（索引名）。
|
||||
EST. ROWS 估算该操作算子的输出行数。
|
||||
COST 该操作算子的执行代价（微秒）。
|
||||
|
||||
|
||||
OceanBase 数据库 EXPLAIN 命令输出的第一部分是执行计划的树形结构展示。其中每一个操作在树中的层次通过其在 operator 中的缩进予以展示，层次最深的优先执行，层次相同的以特定算子的执行顺序为标准来执行。
|
||||
|
||||
问题: update a not exists (b…)
|
||||
我一开始以为 B是驱动表，B的数据挺多的 后来看到NLAJ，是说左边的表关联右边的表
|
||||
所以这个的驱动表是不是实际是A，用A的匹配B的，这个理解有问题吗
|
||||
|
||||
回答: 没错 A 驱动 B的
|
||||
|
||||
问题: 光知道最下最右的是驱动表了 所以一开始搞得有点懵 :sweat_smile:
|
||||
|
||||
回答: nlj应该原理应该都是左表(驱动表)的记录探测右表(被驱动表)， 选哪张成为左表或右表就基于一些其他考量了，比如数据量， 而anti join/semi join只是对 not exist/exist的一种优化，相关的原理和资料网上可以查阅一下
|
||||
|
||||
问题: 也就是nlj 就是按照之前理解的谁先执行 谁就是驱动表 也就是执行计划中的最右的表
|
||||
而anti join/semi join，谁在not exist左面，谁就是驱动表。这么理解对吧
|
||||
|
||||
回答: nlj也是左表的表是驱动表，这个要了解下计划执行方面的基本原理，取左表的一行数据，再遍历右表，一旦满足连接条件，就可以返回数据
|
||||
anti/semi只是因为not exists/exist的语义只是返回左表数据，改成anti join是一种计划优化，连接的方式比子查询更优
|
||||
|
||||
"""
|
||||
|
||||
from llama_index import Document
|
||||
text_list = [text1]
|
||||
documents = [Document(t) for t in text_list]
|
||||
|
||||
num_output = 250
|
||||
max_input_size = 512
|
||||
|
||||
max_chunk_overlap = 20
|
||||
prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap)
|
||||
|
||||
index = GPTListIndex(documents, embed_model=embed_model, llm_predictor=llm_predictor, prompt_helper=prompt_helper)
|
||||
index.save_to_disk("index.json")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import logging
|
||||
logging.getLogger().setLevel(logging.CRITICAL)
|
||||
for d in documents:
|
||||
print(d)
|
||||
|
||||
response = index.query("数据库的执行计划命令有多少?")
|
||||
print(response)
|
||||
tools = load_tools(['python_repl'], llm=llm)
|
||||
agent = initialize_agent(tools, llm, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
|
||||
agent.run(
|
||||
"Write a python script that prints 'Hello World!'"
|
||||
)
|
||||
@ -9,9 +9,18 @@ model_path = os.path.join(root_path, "models")
|
||||
vector_storepath = os.path.join(root_path, "vector_store")
|
||||
|
||||
|
||||
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
llm_model_config = {
|
||||
"flan-t5-base": os.path.join(model_path, "flan-t5-base"),
|
||||
"vicuna-13b": os.path.join(model_path, "vicuna-13b")
|
||||
}
|
||||
|
||||
LLM_MODEL = "vicuna-13b"
|
||||
LLM_MODEL = "vicuna-13b"
|
||||
|
||||
|
||||
vicuna_model_server = "http://192.168.31.114:21000/"
|
||||
|
||||
|
||||
# Load model config
|
||||
isload_8bit = True
|
||||
isdebug = False
|
||||
|
||||
86
pilot/model/inference.py
Normal file
86
pilot/model/inference.py
Normal file
@ -0,0 +1,86 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import torch
|
||||
|
||||
@torch.inference_mode()
|
||||
def generate_output(model, tokenizer, params, device, context_len=2048):
|
||||
prompt = params["prompt"]
|
||||
temperature = float(params.get("temperature", 1.0))
|
||||
max_new_tokens = int(params.get("max_new_tokens", 256))
|
||||
stop_parameter = params.get("stop", None)
|
||||
|
||||
if stop_parameter == tokenizer.eso_token:
|
||||
stop_parameter = None
|
||||
|
||||
stop_strings = []
|
||||
if isinstance(stop_parameter, str):
|
||||
stop_strings.append(stop_parameter)
|
||||
elif isinstance(stop_parameter, list):
|
||||
stop_strings = stop_parameter
|
||||
elif stop_parameter is None:
|
||||
pass
|
||||
else:
|
||||
raise TypeError("Stop parameter must be string or list of strings.")
|
||||
|
||||
pos = -1
|
||||
input_ids = tokenizer(prompt).input_ids
|
||||
output_ids = []
|
||||
|
||||
max_src_len = context_len - max_new_tokens - 8
|
||||
input_ids = input_ids[-max_src_len:]
|
||||
|
||||
for i in range(max_new_tokens):
|
||||
if i == 0:
|
||||
out = model(torch.as_tensor([input_ids], device=device), use_cache=True)
|
||||
logits = out.logits
|
||||
past_key_values = out.past_key_values
|
||||
else:
|
||||
out = model(
|
||||
input_ids=torch.as_tensor([[token]], device=device),
|
||||
use_cache=True,
|
||||
past_key_values=past_key_values,
|
||||
)
|
||||
logits = out.logits
|
||||
past_key_values = out.past_key_value
|
||||
last_token_logits = logits[0][-1]
|
||||
|
||||
if temperature < 1e-4:
|
||||
token = int(torch.argmax(last_token_logits))
|
||||
else:
|
||||
probs = torch.softmax(last_token_logits / temperature, dim=1)
|
||||
token = int(torch.multinomial(probs, num_samples=1))
|
||||
|
||||
output_ids.append(token)
|
||||
|
||||
if token == tokenizer.eos_token_id:
|
||||
stopped = True
|
||||
else:
|
||||
stopped = False
|
||||
|
||||
output = tokenizer.decode(output_ids, skip_special_tokens=True)
|
||||
for stop_str in stop_strings:
|
||||
pos = output.rfind(stop_str)
|
||||
if pos != -1:
|
||||
output = output[:pos]
|
||||
stoppped = True
|
||||
break
|
||||
else:
|
||||
pass
|
||||
|
||||
if stoppped:
|
||||
break
|
||||
|
||||
del past_key_values
|
||||
if pos != -1:
|
||||
return output[:pos]
|
||||
return output
|
||||
|
||||
|
||||
@torch.inference_mode()
|
||||
def get_embeddings(model, tokenizer, prompt):
|
||||
input_ids = tokenizer(prompt).input_ids
|
||||
input_embeddings = model.get_input_embeddings()
|
||||
embeddings = input_embeddings(torch.LongTensor([input_ids]))
|
||||
mean = torch.mean(embeddings[0], 0).cpu().detach()
|
||||
return mean
|
||||
@ -2,8 +2,6 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import torch
|
||||
from pilot.utils import get_gpu_memory
|
||||
from fastchat.serve.inference import compress_module
|
||||
from transformers import (
|
||||
AutoTokenizer,
|
||||
AutoModelForCausalLM,
|
||||
@ -28,12 +26,12 @@ class ModerLoader:
|
||||
tokenizer = AutoTokenizer.from_pretrained(self.model_path, use_fast=False)
|
||||
model = AutoModelForCausalLM.from_pretrained(self.model_path, low_cpu_mem_usage=True, **self.kwargs)
|
||||
|
||||
if load_8bit:
|
||||
compress_module(model, self.device)
|
||||
|
||||
if debug:
|
||||
print(model)
|
||||
|
||||
if self.device == "cuda":
|
||||
model.to(self.device)
|
||||
|
||||
return model, tokenizer
|
||||
|
||||
|
||||
|
||||
@ -1,9 +1,84 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding:utf-8 -*-
|
||||
|
||||
from transformers import pipeline
|
||||
import json
|
||||
import requests
|
||||
from urllib.parse import urljoin
|
||||
from langchain.embeddings.base import Embeddings
|
||||
from pydantic import BaseModel
|
||||
from typing import Any, Mapping, Optional, List
|
||||
from langchain.llms.base import LLM
|
||||
from configs.model_config import *
|
||||
|
||||
class VicunaLLM(LLM):
|
||||
model_name = llm_model_config[LLM_MODEL]
|
||||
class VicunaRequestLLM(LLM):
|
||||
|
||||
vicuna_generate_path = "generate"
|
||||
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
|
||||
if isinstance(stop, list):
|
||||
stop = stop + ["Observation:"]
|
||||
|
||||
params = {
|
||||
"prompt": prompt,
|
||||
"temperature": 0,
|
||||
"max_new_tokens": 256,
|
||||
"stop": stop
|
||||
}
|
||||
response = requests.post(
|
||||
url=urljoin(vicuna_model_server, self.vicuna_generate_path),
|
||||
data=json.dumps(params)
|
||||
)
|
||||
response.raise_for_status()
|
||||
return response.json()["response"]
|
||||
|
||||
@property
|
||||
def _llm_type(self) -> str:
|
||||
return "custome"
|
||||
|
||||
def _identifying_params(self) -> Mapping[str, Any]:
|
||||
return {}
|
||||
|
||||
|
||||
class VicunaEmbeddingLLM(BaseModel, Embeddings):
|
||||
|
||||
vicuna_embedding_path = "embedding"
|
||||
|
||||
def _call(self, prompt: str) -> str:
|
||||
p = prompt.strip()
|
||||
print("Sending prompt ", p)
|
||||
|
||||
response = requests.post(
|
||||
url=urljoin(vicuna_model_server, self.vicuna_embedding_path),
|
||||
json={
|
||||
"prompt": p
|
||||
}
|
||||
)
|
||||
response.raise_for_status()
|
||||
return response.json()["response"]
|
||||
|
||||
def embed_documents(self, texts: List[str]) -> List[List[float]]:
|
||||
""" Call out to Vicuna's server embedding endpoint for embedding search docs.
|
||||
|
||||
Args:
|
||||
texts: The list of text to embed
|
||||
|
||||
Returns:
|
||||
List of embeddings. one for each text.
|
||||
"""
|
||||
results = []
|
||||
for text in texts:
|
||||
response = self.embed_query(text)
|
||||
results.append(response)
|
||||
return results
|
||||
|
||||
|
||||
def embed_query(self, text: str) -> List[float]:
|
||||
""" Call out to Vicuna's server embedding endpoint for embedding query text.
|
||||
|
||||
Args:
|
||||
text: The text to embed.
|
||||
Returns:
|
||||
Embedding for the text
|
||||
"""
|
||||
embedding = self._call(text)
|
||||
return embedding
|
||||
|
||||
|
||||
@ -1,3 +1,56 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding:utf-8 -*-
|
||||
|
||||
import requests
|
||||
import json
|
||||
import time
|
||||
from urllib.parse import urljoin
|
||||
import gradio as gr
|
||||
from configs.model_config import *
|
||||
vicuna_base_uri = "http://192.168.31.114:21002/"
|
||||
vicuna_stream_path = "worker_generate_stream"
|
||||
vicuna_status_path = "worker_get_status"
|
||||
|
||||
def generate(prompt):
|
||||
params = {
|
||||
"model": "vicuna-13b",
|
||||
"prompt": prompt,
|
||||
"temperature": 0.7,
|
||||
"max_new_tokens": 512,
|
||||
"stop": "###"
|
||||
}
|
||||
|
||||
sts_response = requests.post(
|
||||
url=urljoin(vicuna_base_uri, vicuna_status_path)
|
||||
)
|
||||
print(sts_response.text)
|
||||
|
||||
response = requests.post(
|
||||
url=urljoin(vicuna_base_uri, vicuna_stream_path), data=json.dumps(params)
|
||||
)
|
||||
|
||||
skip_echo_len = len(params["prompt"]) + 1 - params["prompt"].count("</s>") * 3
|
||||
for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"):
|
||||
if chunk:
|
||||
data = json.loads(chunk.decode())
|
||||
if data["error_code"] == 0:
|
||||
output = data["text"]
|
||||
yield(output)
|
||||
|
||||
time.sleep(0.02)
|
||||
|
||||
if __name__ == "__main__":
|
||||
print(LLM_MODEL)
|
||||
with gr.Blocks() as demo:
|
||||
gr.Markdown("数据库SQL生成助手")
|
||||
with gr.Tab("SQL生成"):
|
||||
text_input = gr.TextArea()
|
||||
text_output = gr.TextArea()
|
||||
text_button = gr.Button("提交")
|
||||
|
||||
|
||||
text_button.click(generate, inputs=text_input, outputs=text_output)
|
||||
|
||||
demo.queue(concurrency_count=3).launch(server_name="0.0.0.0")
|
||||
|
||||
|
||||
@ -7,7 +7,6 @@ import torch
|
||||
import gradio as gr
|
||||
from fastchat.serve.inference import generate_stream, compress_module
|
||||
|
||||
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
BASE_MODE = "/home/magic/workspace/github/DB-GPT/models/vicuna-13b"
|
||||
@ -21,12 +20,12 @@ model = AutoModelForCausalLM.from_pretrained(
|
||||
)
|
||||
|
||||
def generate(prompt):
|
||||
# compress_module(model, device)
|
||||
# model.to(device)
|
||||
compress_module(model, device)
|
||||
model.to(device)
|
||||
print(model, tokenizer)
|
||||
params = {
|
||||
"model": "vicuna-13b",
|
||||
"prompt": prompt,
|
||||
"prompt": "这是一个用户与助手之间的对话, 助手精通数据库领域的知识, 并能够对数据库领域知识做出非常专业的回答。以下是用户的问题:" + prompt,
|
||||
"temperature": 0.7,
|
||||
"max_new_tokens": 512,
|
||||
"stop": "###"
|
||||
@ -36,9 +35,6 @@ def generate(prompt):
|
||||
|
||||
for chunk in output:
|
||||
yield chunk
|
||||
#for chunk in output.iter_lines(decode_unicode=False, delimiter=b"\0"):
|
||||
# if chunk:
|
||||
# yield chunk
|
||||
|
||||
if __name__ == "__main__":
|
||||
with gr.Blocks() as demo:
|
||||
@ -53,5 +49,3 @@ if __name__ == "__main__":
|
||||
|
||||
demo.queue(concurrency_count=3).launch(server_name="0.0.0.0")
|
||||
|
||||
|
||||
|
||||
|
||||
48
pilot/server/vicuna_server.py
Normal file
48
pilot/server/vicuna_server.py
Normal file
@ -0,0 +1,48 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
from typing import Optional, List
|
||||
from fastapi import FastAPI
|
||||
from pydantic import BaseModel
|
||||
from pilot.model.inference import generate_output, get_embeddings
|
||||
from pilot.model.loader import ModerLoader
|
||||
from pilot.configs.model_config import *
|
||||
|
||||
model_path = llm_model_config[LLM_MODEL]
|
||||
ml = ModerLoader(model_path=model_path)
|
||||
model, tokenizer = ml.loader(load_8bit=isload_8bit, debug=isdebug)
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
class PromptRequest(BaseModel):
|
||||
prompt: str
|
||||
temperature: float
|
||||
max_new_tokens: int
|
||||
stop: Optional(List[str]) = None
|
||||
|
||||
|
||||
class EmbeddingRequest(BaseModel):
|
||||
prompt: str
|
||||
|
||||
|
||||
@app.post("/generate")
|
||||
def generate(prompt_request: PromptRequest):
|
||||
params = {
|
||||
"prompt": prompt_request.prompt,
|
||||
"temperature": prompt_request.temperature,
|
||||
"max_new_tokens": prompt_request.max_new_tokens,
|
||||
"stop": prompt_request.stop
|
||||
}
|
||||
|
||||
print("Receive prompt: ", params["prompt"])
|
||||
output = generate_output(model, tokenizer, params, DEVICE)
|
||||
print("Output: ", output)
|
||||
return {"response": output}
|
||||
|
||||
|
||||
@app.post("/embedding")
|
||||
def embeddings(prompt_request: EmbeddingRequest):
|
||||
params = {"prompt": prompt_request.prompt}
|
||||
print("Received prompt: ", params["prompt"])
|
||||
output = get_embeddings(model, tokenizer, params["prompt"])
|
||||
return {"response": [float(x) for x in output]}
|
||||
Loading…
Reference in New Issue
Block a user