PostgreSQL 规格评估 - 微观、宏观、精准 多视角估算数据库性能(选型、做预算不求人)
背景
在提预算时必不可少的环境是评估需要多少硬件。
通常会要求业务方提供一些数据，例如用户数、PV、UV等。但是这种评估纯靠经验，方法非常的粗糙也不准确。
那么到底如何评估需要多少硬件、或者说需要什么样规格的硬件来支撑你未来的业务呢？
对于PostgreSQL这个数据库产品来说，我介绍一下三种评估方法：
1、微观评估（相对来说比较准确）
2、宏观评估（对选型有帮助，对规格帮助不大，略显粗糙）
3、精准评估（最为准确，但是要求对业务非常熟悉，对未来的瓶颈把握准确）
一、微观估算法
我们在通过SQL与数据库交互时，数据库是如何执行SQL的呢？
首先要PARSE SQL，然后生成执行路径，选择最优执行路径，执行SQL，最关键的是选择最优执行路径。PostgreSQL是CBO的优化器，根据成本选择。
这里提到了成本，成本是怎么算出来的呢？成本是结合扫描方法、统计信息、估算需要扫描多少个数据块，扫描多少条记录，最后通过对应扫描方法的成本估算算法算出来的。
一个 QUERY 有哪些成本
1、成本包括：
IO成本，CPU成本。
2、IO成本包括：
连续IO成本，离散IO层板。
3、CPU成本包括：
获取索引、TOAST索引、堆表、TOAST表的tuple或ITEM的成本；
操作符、函数处理行的成本；
处理JOIN的成本等等。
一个 QUERY 如何执行和传递成本
生成好执行计划后，QUERY的执行就会按执行树来执行
执行树由若干个节点组成，从一个节点，跳到下一个节点，就好像接力赛一样。
节点跟节点之间传递的是什么呢？
Path数据结构，主要包含（rows, startup_cost, total_cost）。一个数据节点
rows，表示这个节点有多少满足条件的行，输出到下一个节点。
startup_cost，表示这个节点得到第一条符合条件的记录，需要多少成本。
total_cost，表示这个节点得到所有符合条件的记录，需要多少成本。
执行节点有哪些种类
执行节点的种类很多，可以从成本计算的代码中得到：
src/backend/optimizer/path/costsize.c
/*
* cost_seqscan
* Determines and returns the cost of scanning a relation sequentially.
*
* 'baserel' is the relation to be scanned
* 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
*/
cost_seqscan(Path *path, PlannerInfo *root, RelOptInfo *baserel, ParamPathInfo *param_info)
/*
* cost_samplescan
* Determines and returns the cost of scanning a relation using sampling.
*
* 'baserel' is the relation to be scanned
* 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
*/
cost_samplescan(Path *path, PlannerInfo *root, RelOptInfo *baserel, ParamPathInfo *param_info)
/*
* cost_gather
* Determines and returns the cost of gather path.
*
* 'rel' is the relation to be operated upon
* 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
* 'rows' may be used to point to a row estimate; if non-NULL, it overrides
* both 'rel' and 'param_info'. This is useful when the path doesn't exactly
* correspond to any particular RelOptInfo.
*/
cost_gather(GatherPath *path, PlannerInfo *root, RelOptInfo *rel,
ParamPathInfo *param_info, double *rows)
/*
* cost_gather_merge
* Determines and returns the cost of gather merge path.
*
* GatherMerge merges several pre-sorted input streams, using a heap that at
* any given instant holds the next tuple from each stream. If there are N
* streams, we need about N*log2(N) tuple comparisons to construct the heap at
* startup, and then for each output tuple, about log2(N) comparisons to
* replace the top heap entry with the next tuple from the same stream.
*/
cost_gather_merge(GatherMergePath *path, PlannerInfo *root, RelOptInfo *rel,
ParamPathInfo *param_info, Cost input_startup_cost, Cost input_total_cost, double *rows)
/*
* cost_index
* Determines and returns the cost of scanning a relation using an index.
*
* 'path' describes the indexscan under consideration, and is complete
* except for the fields to be set by this routine
* 'loop_count' is the number of repetitions of the indexscan to factor into
* estimates of caching behavior
*
* In addition to rows, startup_cost and total_cost, cost_index() sets the
* path's indextotalcost and indexselectivity fields. These values will be
* needed if the IndexPath is used in a BitmapIndexScan.
*
* NOTE: path->indexquals must contain only clauses usable as index
* restrictions. Any additional quals evaluated as qpquals may reduce the
* number of returned tuples, but they won't reduce the number of tuples
* we have to fetch from the table, so they don't reduce the scan cost.
*/
cost_index(IndexPath *path, PlannerInfo *root, double loop_count, bool partial_path)
/*
* cost_bitmap_heap_scan
* Determines and returns the cost of scanning a relation using a bitmap
* index-then-heap plan.
*
* 'baserel' is the relation to be scanned
* 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
* 'bitmapqual' is a tree of IndexPaths, BitmapAndPaths, and BitmapOrPaths
* 'loop_count' is the number of repetitions of the indexscan to factor into
* estimates of caching behavior
*
* Note: the component IndexPaths in bitmapqual should have been costed
* using the same loop_count.
*/
cost_bitmap_heap_scan(Path *path, PlannerInfo *root, RelOptInfo *baserel,
ParamPathInfo *param_info, Path *bitmapqual, double loop_count)
/*
* cost_bitmap_tree_node
* Extract cost and selectivity from a bitmap tree node (index/and/or)
*/
cost_bitmap_tree_node(Path *path, Cost *cost, Selectivity *selec)
/*
* cost_bitmap_and_node
* Estimate the cost of a BitmapAnd node
*
* Note that this considers only the costs of index scanning and bitmap
* creation, not the eventual heap access. In that sense the object isn't
* truly a Path, but it has enough path-like properties (costs in particular)
* to warrant treating it as one. We don't bother to set the path rows field,
* however.
*/
cost_bitmap_and_node(BitmapAndPath *path, PlannerInfo *root)
/*
* cost_bitmap_or_node
* Estimate the cost of a BitmapOr node
*
* See comments for cost_bitmap_and_node.
*/
cost_bitmap_or_node(BitmapOrPath *path, PlannerInfo *root)
/*
* cost_tidscan
* Determines and returns the cost of scanning a relation using TIDs.
*
* 'baserel' is the relation to be scanned
* 'tidquals' is the list of TID-checkable quals
* 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
*/
cost_tidscan(Path *path, PlannerInfo *root, RelOptInfo *baserel,
List *tidquals, ParamPathInfo *param_info)
/*
* cost_subqueryscan
* Determines and returns the cost of scanning a subquery RTE.
*
* 'baserel' is the relation to be scanned
* 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
*/
cost_subqueryscan(SubqueryScanPath *path, PlannerInfo *root,
RelOptInfo *baserel, ParamPathInfo *param_info)
/*
* cost_functionscan
* Determines and returns the cost of scanning a function RTE.
*
* 'baserel' is the relation to be scanned
* 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
*/
cost_functionscan(Path *path, PlannerInfo *root, RelOptInfo *baserel, ParamPathInfo *param_info)
/*
* cost_tablefuncscan
* Determines and returns the cost of scanning a table function.
*
* 'baserel' is the relation to be scanned
* 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
*/
cost_tablefuncscan(Path *path, PlannerInfo *root, RelOptInfo *baserel, ParamPathInfo *param_info)
/*
* cost_valuesscan
* Determines and returns the cost of scanning a VALUES RTE.
*
* 'baserel' is the relation to be scanned
* 'param_info' is the ParamPathInfo if this is a parameterized path, else NULL
*/
cost_valuesscan(Path *path, PlannerInfo *root, RelOptInfo *baserel, ParamPathInfo *param_info)
/*
* cost_ctescan
* Determines and returns the cost of scanning a CTE RTE.
*
* Note: this is used for both self-reference and regular CTEs; the
* possible cost differences are below the threshold of what we could
* estimate accurately anyway. Note that the costs of evaluating the
* referenced CTE query are added into the final plan as initplan costs,
* and should NOT be counted here.
*/
cost_ctescan(Path *path, PlannerInfo *root, RelOptInfo *baserel, ParamPathInfo *param_info)
cost_namedtuplestorescan(Path *path, PlannerInfo *root,
RelOptInfo *baserel, ParamPathInfo *param_info)
/*
* cost_recursive_union
* Determines and returns the cost of performing a recursive union,
* and also the estimated output size.
*
* We are given Paths for the nonrecursive and recursive terms.
*/
cost_recursive_union(Path *runion, Path *nrterm, Path *rterm)
/*
* cost_sort
* Determines and returns the cost of sorting a relation, including
* the cost of reading the input data.
*
* If the total volume of data to sort is less than sort_mem, we will do
* an in-memory sort, which requires no I/O and about t*log2(t) tuple
* comparisons for t tuples.
*
* If the total volume exceeds sort_mem, we switch to a tape-style merge
* algorithm. There will still be about t*log2(t) tuple comparisons in
* total, but we will also need to write and read each tuple once per
* merge pass. We expect about ceil(logM(r)) merge passes where r is the
* number of initial runs formed and M is the merge order used by tuplesort.c.
* Since the average initial run should be about sort_mem, we have
* disk traffic = 2 * relsize * ceil(logM(p / sort_mem))
* cpu = comparison_cost * t * log2(t)
*
* If the sort is bounded (i.e., only the first k result tuples are needed)
* and k tuples can fit into sort_mem, we use a heap method that keeps only
* k tuples in the heap; this will require about t*log2(k) tuple comparisons.
*
* The disk traffic is assumed to be 3/4ths sequential and 1/4th random
* accesses (XXX can't we refine that guess?)
*
* By default, we charge two operator evals per tuple comparison, which should
* be in the right ballpark in most cases. The caller can tweak this by
* specifying nonzero comparison_cost; typically that's used for any extra
* work that has to be done to prepare the inputs to the comparison operators.
*
* 'pathkeys' is a list of sort keys
* 'input_cost' is the total cost for reading the input data
* 'tuples' is the number of tuples in the relation
* 'width' is the average tuple width in bytes
* 'comparison_cost' is the extra cost per comparison, if any
* 'sort_mem' is the number of kilobytes of work memory allowed for the sort
* 'limit_tuples' is the bound on the number of output tuples; -1 if no bound
*
* NOTE: some callers currently pass NIL for pathkeys because they
* can't conveniently supply the sort keys. Since this routine doesn't
* currently do anything with pathkeys anyway, that doesn't matter...
* but if it ever does, it should react gracefully to lack of key data.
* (Actually, the thing we'd most likely be interested in is just the number
* of sort keys, which all callers *could* supply.)
*/
cost_sort(Path *path, PlannerInfo *root, List *pathkeys,
Cost input_cost, double tuples, int width, Cost comparison_cost, int sort_mem, double limit_tuples)
/*
* cost_append
* Determines and returns the cost of an Append node.
*
* We charge nothing extra for the Append itself, which perhaps is too
* optimistic, but since it doesn't do any selection or projection, it is a
* pretty cheap node.
*/
cost_append(Path *path, List *subpaths, int num_nonpartial_subpaths)
/*
* cost_merge_append
* Determines and returns the cost of a MergeAppend node.
*
* MergeAppend merges several pre-sorted input streams, using a heap that
* at any given instant holds the next tuple from each stream. If there
* are N streams, we need about N*log2(N) tuple comparisons to construct
* the heap at startup, and then for each output tuple, about log2(N)
* comparisons to replace the top entry.
*
* (The effective value of N will drop once some of the input streams are
* exhausted, but it seems unlikely to be worth trying to account for that.)
*
* The heap is never spilled to disk, since we assume N is not very large.
* So this is much simpler than cost_sort.
*
* As in cost_sort, we charge two operator evals per tuple comparison.
*
* 'pathkeys' is a list of sort keys
* 'n_streams' is the number of input streams
* 'input_startup_cost' is the sum of the input streams' startup costs
* 'input_total_cost' is the sum of the input streams' total costs
* 'tuples' is the number of tuples in all the streams
*/
cost_merge_append(Path *path, PlannerInfo *root, List *pathkeys,
int n_streams, Cost input_startup_cost, Cost input_total_cost, double tuples)
/*
* cost_material
* Determines and returns the cost of materializing a relation, including
* the cost of reading the input data.
*
* If the total volume of data to materialize exceeds work_mem, we will need
* to write it to disk, so the cost is much higher in that case.
*
* Note that here we are estimating the costs for the first scan of the
* relation, so the materialization is all overhead --- any savings will
* occur only on rescan, which is estimated in cost_rescan.
*/
cost_material(Path *path, Cost input_startup_cost,
Cost input_total_cost, double tuples, int width)
/*
* cost_agg
* Determines and returns the cost of performing an Agg plan node,
* including the cost of its input.
*
* aggcosts can be NULL when there are no actual aggregate functions (i.e.,
* we are using a hashed Agg node just to do grouping).
*
* Note: when aggstrategy == AGG_SORTED, caller must ensure that input costs
* are for appropriately-sorted input.
*/
cost_agg(Path *path, PlannerInfo *root, AggStrategy aggstrategy,
const AggClauseCosts *aggcosts, int numGroupCols, double numGroups, Cost input_startup_cost, Cost input_total_cost, double input_tuples)
/*
* cost_windowagg
* Determines and returns the cost of performing a WindowAgg plan node,
* including the cost of its input.
*
* Input is assumed already properly sorted.
*/
cost_windowagg(Path *path, PlannerInfo *root, List *windowFuncs,
int numPartCols, int numOrderCols, Cost input_startup_cost, Cost input_total_cost, double input_tuples)
/*
* cost_group
* Determines and returns the cost of performing a Group plan node,
* including the cost of its input.
*
* Note: caller must ensure that input costs are for appropriately-sorted
* input.
*/
cost_group(Path *path, PlannerInfo *root, int numGroupCols, double numGroups,
Cost input_startup_cost, Cost input_total_cost,
double input_tuples)
/*
* cost_subplan
* Figure the costs for a SubPlan (or initplan).
*
* Note: we could dig the subplan's Plan out of the root list, but in practice
* all callers have it handy already, so we make them pass it.
*/
cost_subplan(PlannerInfo *root, SubPlan *subplan, Plan *plan)
/*
* cost_rescan
* Given a finished Path, estimate the costs of rescanning it after
* having done so the first time. For some Path types a rescan is
* cheaper than an original scan (if no parameters change), and this
* function embodies knowledge about that. The default is to return
* the same costs stored in the Path. (Note that the cost estimates
* actually stored in Paths are always for first scans.)
*
* This function is not currently intended to model effects such as rescans
* being cheaper due to disk block caching; what we are concerned with is
* plan types wherein the executor caches results explicitly, or doesn't
* redo startup calculations, etc.
*/
cost_rescan(PlannerInfo *root, Path *path, Cost *rescan_startup_cost, /* output parameters */
Cost *rescan_total_cost)
/*
* cost_qual_eval
* Estimate the CPU costs of evaluating a WHERE clause.
* The input can be either an implicitly-ANDed list of boolean
* expressions, or a list of RestrictInfo nodes. (The latter is
* preferred since it allows caching of the results.)
* The result includes both a one-time (startup) component,
* and a per-evaluation component.
*/
cost_qual_eval(QualCost *cost, List *quals, PlannerInfo *root)
/*
* cost_qual_eval_node
* As above, for a single RestrictInfo or expression.
*/
cost_qual_eval_node(QualCost *cost, Node *qual, PlannerInfo *root)
cost_qual_eval_walker(Node *node, cost_qual_eval_context *context)
如何估算每个节点的成本
记得前面提到的接力棒吗？接力棒里面包含了rows，这个非常关键。
rows是告诉下一个节点，你可能要处理这么多行。
而有rows是不够的，还有成本因子，因为每行还可能涉及到操作符的计算、并行worker的成本等。
这些因子的设置如下：
src/backend/optimizer/path/costsize.c
* seq_page_cost Cost of a sequential page fetch
* random_page_cost Cost of a non-sequential page fetch
* cpu_tuple_cost Cost of typical CPU time to process a tuple
* cpu_index_tuple_cost Cost of typical CPU time to process an index tuple
* cpu_operator_cost Cost of CPU time to execute an operator or function
* parallel_tuple_cost Cost of CPU time to pass a tuple from worker to master backend
* parallel_setup_cost Cost of setting up shared memory for parallelism
成本计算图
一些优化器的成本估算例子，可以参考文档：
https://www.postgresql.org/docs/10/static/planner-stats-details.html
src/backend/optimizer/path/costsize.c
从成本如何得到执行时间
注意成本是虚化的东西，和时间是不挂钩的，但是我们可以让他们挂钩起来。
这就需要做校准，把成本因子调教成输出的cost等于执行时间的值。
我在之前发表的文章中提到了如何校准，请参考。
《优化器成本因子校对 - PostgreSQL explain cost constants alignment to timestamp》
如何在用户没有数据的情况下，估算性能
实际上方法很简单，我们需要业务方提供几个东西即可：
1、表定义
2、被评估的SQL
3、统计信息，需要提供我用中文注释的部分。
View "pg_catalog.pg_stats"
Column | Type | Collation | Nullable | Default
------------------------+----------+-----------+----------+---------
schemaname | name | | |
tablename | name | | |
attname | name | | |
inherited | boolean | | |
null_frac | real | | | 空值比例
avg_width | integer | | | 平均行长度
n_distinct | real | | | 多少唯一值，或唯一值比例，-1表示唯一
most_common_vals | anyarray | | | 高频词
most_common_freqs | real[] | | | 高频词的出现频率
histogram_bounds | anyarray | | | 按记录数均分为若干BUCKET的 分位数（列值）
correlation | real | | | 存储和实际值的线性相关性
most_common_elems | anyarray | | | 对于多值类型（数组），元素的高频词
most_common_elem_freqs | real[] | | | 元素高频词出现的频率
elem_count_histogram | real[] | | | 元素按记录数均分为若干BUCKET的 分位数（元素值）
因为pg_stats支持导出导入，所以不需要实际数据即可完成，postgrespro版本就提供了这样的功能。
https://postgrespro.com/docs/postgresproee/9.6/dump-stat.html
4、已调教好的成本因子
* seq_page_cost Cost of a sequential page fetch
* random_page_cost Cost of a non-sequential page fetch
* cpu_tuple_cost Cost of typical CPU time to process a tuple
* cpu_index_tuple_cost Cost of typical CPU time to process an index tuple
* cpu_operator_cost Cost of CPU time to execute an operator or function
* parallel_tuple_cost Cost of CPU time to pass a tuple from worker to master backend
* parallel_setup_cost Cost of setting up shared memory for parallelism
int effective_cache_size = DEFAULT_EFFECTIVE_CACHE_SIZE;
有以上要素，我们就能通过explain SQL得到估算出来的SQL执行时间。
就可以得到TPS等等。
从执行时间如何得到TPS
分为几种情况
1、CPU是瓶颈时，TPS = 核数*(1秒/执行时间)。
2、IO是瓶颈时，TPS = (磁盘带宽或IO能力) / (每个query的读写吞吐或IO)
二、宏观估算法
宏观估算，通过产品本身的特色来估算。
Greenplum和PostgreSQL两个产品的特色
1、RDS PostgreSQL 10 适合以10TB ~ 100TB，OLTP为主，OLAP为辅的场景。与Oracle覆盖的场景非常类似。
兼容SQL:2011，百万+级tpmC。
支持多核并行计算。
支持可读写的OSS对象存储外部表。
支持常用类型、扩展数据类型：JSON(B)、Hstore(KV), PostGIS空间数据库、pgrouting(路由,图式搜索)、数组、ltree树类型、HLL估值类型, smlar, imgsmlr等。
支持SQL流计算插件
支持时序插件
支持btree, hash, gin, gist, sp-gist, bloom, brin等索引。
支持plpgsql, sql服务端编程。
支持分析型语法（多维计算、窗口查询）、递归查询(树形查询、图式搜索、等场景)。支持文本全文检索、模糊查询、相似查询、正则查询。支持数组相似查询，图像相似查询。
1.1 适合业务场景：
TB+级OLTP(在线事务处理)+OLAP(实时分析)。
模糊查询、相似搜索、正则搜索
全文检索
物联网
流式数据处理
社交
图式搜索
独立事件分析
冷热分离
异步消息
多值类型、图像特征值 相似搜索
实时数据清洗
GIS应用
任意字段实时搜索
... ...
1.2 主打：功能、稳定性、性能、高可用、可靠性、Oracle兼容性、HTAP。
2、HybridDB for PostgreSQL(Greenplum开源版GPDB改进而来) 适合PB级实时OLAP，非常典型的海量数仓。
兼容SQL:2008，兼容TPC-H，TPC-DS。有数十年的商业化历练经验。
支持可读写的OSS对象存储外部表
支持常用类型、扩展数据类型：JSON、PostGIS空间数据库、数组、HLL估值类型。
支持bitmap, hash, btree索引。
支持pljava服务端编程。
支持分析型语法（多维计算、窗口查询、MADlib机器学习）、支持全文检索语法。
支持列存储、行存储、压缩、混合存储。
支持4阶段聚合，支持节点间自动重分布。
支持水平扩容。
2.1 适合业务场景：
PB+级实时分析。（传统统计；时间、空间、属性多维属性透视、圈人；任意表、任意维度JOIN；）
2.2 主打：分析型SQL兼容性、功能、稳定性、性能、高可用、扩展性。
3、RDS PPAS 9.6
PostgreSQL商业版本PPAS，TB+级OLTP+OLAP数据库，兼容SQL:2011，兼容Oracle(SQL语法、函数、PLSQL存储过程)，支持单机多核并行计算，百万+级tpmC。
适合业务场景：
最小化业务改动量，低成本去O。
TB+级OLTP(在线事务处理)+OLAP(实时分析)。
主打 功能、稳定性、性能、高可用、Oracle兼容性、HTAP。
三、精准实测法
精准实测，需要清楚的了解业务。了解业务的数据结构，业务逻辑，模拟事务请求。
压测方法
根据实际业务，设计测试模型，模拟事务请求，压测得到benchmark。
例子
上面的两篇文档中，设计了一个场景，里面就涉及到如何设计场景，如何设计结构、QUERY、以及测试脚本，压测。
下面再列举一个例子。
1、设计表结构
create table a(id int8 primary key, info text, crt_time timestamp);
2、设计SQL
insert into a values (:id, md5(random()::text), now()) on conflict (id) do update set info=excluded.info, crt_time=excluded.crt_time;
3、设计测试脚本
pgbench里面支持多种随机数生成方法，支持sleep来模拟客户端业务逻辑的处理，支持多线程。具体详见pgbench文档。
vi test.sql
\set id random(1,100000000)
insert into a values (:id, md5(random()::text), now()) on conflict (id) do update set info=excluded.info, crt_time=excluded.crt_time;
4、压测（连接数、压测时长）
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 32 -j 32 -T 120
transaction type: ./test.sql
scaling factor: 1
query mode: prepared
number of clients: 32
number of threads: 32
duration: 120 s
number of transactions actually processed: 37100343
latency average = 0.103 ms
latency stddev = 0.282 ms
tps = 309166.975398 (including connections establishing)
tps = 309180.511436 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.001 \set id random(1,100000000)
0.103 insert into a values (:id, md5(random()::text), now()) on conflict (id) do update set info=excluded.info, crt_time=excluded.crt_time;
PostgreSQL测试客户端pgbench文档：
https://www.postgresql.org/docs/9.6/static/pgbench.html
工业标准测试
1、tpc-b
PostgreSQL pgbench客户端自带的测试模型，就是tpc-b。具体请参考pgbench的帮助文档，很简单。
https://www.postgresql.org/docs/9.6/static/pgbench.html
2、pgbench for sysbench
这个测试的是一些mysql流行的场景
《PostgreSQL 使用 pgbench 测试 sysbench 相关case》
3、tpc-c
TPC-C是工业标准的OLTP测试，涉及较多复杂查询。
4、linkbenchmark
linkbench是facebook的一个测试模型，用于测试一些图论相关的写入和查询
《facebook linkbench 测试PostgreSQL社交关系图谱场景性能》
四、一些常见性能指标
https://github.com/digoal/blog/blob/master/201711/readme.md
小结
根据业务的发展，估算数据库性能，估算需要投入多少硬件，本文提供了三种方法。
1、微观评估（相对来说比较准确）
当业务开发好后，表结构、QUERY都已经固定了，唯一不固定的是数据。数据可以通过业务方来估算，多少条记录，有多少唯一值，相关性如何，高频词情况如何等等。
结合 成本因子的调教、统计信息、结构、query，得到每一种QUERY的执行时间。评估达到这样的TPS需要多少硬件。
2、宏观评估（对选型有帮助，对规格帮助不大，略显粗糙）
宏观评估，适合选型，因为它只是多各种产品的特性的总结。
3、精准评估（最为准确，但是要求对业务非常熟悉，对未来的瓶颈把握准确）
这个可以在业务开发初期就进行评估，而且相对来说比较准确。
根据表结构，业务逻辑，设计测试脚本，根据实际的测试结果，结合业务的发展期望进行评估。
最后，本文还提供了若干种工业标准测试的方法，以及若干种已有的测试数据仅供参考。