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app.py
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app.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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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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llm_predictor = LLMPredictor(llm=FlanLLM())
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hfemb = HuggingFaceEmbeddings()
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embed_model = LangchainEmbedding(hfemb)
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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。
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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。
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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:
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EXPLAIN BASIC 命令用于最基本的计划展示。
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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）。
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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。
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EXPLAIN 命令格式如下：
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EXPLAIN [BASIC | EXTENDED | PARTITIONS | FORMAT = format_name] [PRETTY | PRETTY_COLOR] explainable_stmt
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format_name:
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{ TRADITIONAL | JSON }
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explainable_stmt:
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{ SELECT statement
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| DELETE statement
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| INSERT statement
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| REPLACE statement
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| UPDATE statement }
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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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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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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 |
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| 4 | HASH GROUP BY | | 2736 | 256K| 3688 (1)| 00:00:01 |
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|* 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 |
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|* 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 |
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--------------------------------------------------------------------------------------------------
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OceanBase 数据库执行计划展示如下：
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|ID|OPERATOR |NAME |EST. ROWS|COST |
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-------------------------------------------------------
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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|
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|4 | SUBPLAN SCAN |VIEW1 |2953 |19070|
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|5 | HASH GROUP BY | |2953 |18662|
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|6 | NESTED-LOOP JOIN| |2953 |15080|
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|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：
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列名 含义
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ID 执行树按照前序遍历的方式得到的编号（从 0 开始）。
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OPERATOR 操作算子的名称。
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NAME 对应表操作的表名（索引名）。
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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）。
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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。
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问题: update a not exists (b…)
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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
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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
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回答: 没错 A 驱动 B的
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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:
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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
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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
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而anti join/semi join，谁在not exist左面，谁就是驱动表。这么理解对吧
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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
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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
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"""
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from llama_index import Document
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text_list = [text1]
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documents = [Document(t) for t in text_list]
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num_output = 250
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max_input_size = 512
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max_chunk_overlap = 20
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prompt_helper = PromptHelper(max_input_size, num_output, max_chunk_overlap)
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index = GPTListIndex(documents, embed_model=embed_model, llm_predictor=llm_predictor, prompt_helper=prompt_helper)
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index.save_to_disk("index.json")
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if __name__ == "__main__":
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import logging
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logging.getLogger().setLevel(logging.CRITICAL)
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for d in documents:
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print(d)
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response = index.query("数据库的执行计划命令有多少?")
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print(response)
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19
examples/gpt_index.py
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examples/gpt_index.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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import os
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import logging
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import sys
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from llama_index import SimpleDirectoryReader, GPTSimpleVectorIndex
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logging.basicConfig(stream=sys.stdout, level=logging.INFO)
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logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
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# read the document of data dir
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documents = SimpleDirectoryReader("data").load_data()
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# split the document to chunk, max token size=500, convert chunk to vector
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index = GPTSimpleVectorIndex(documents)
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# save index
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index.save_to_disk("index.json")
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0
examples/obgpt_index.ipynb
Normal file
0
examples/obgpt_index.ipynb
Normal file
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