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Flask Web Framework Development Python

Ensuring Your Website Permanence: Techniques for Making Your Website Run Until the End of Time

Table of Contents

1. Different levels for improving website performance

1. Bottleneck Detection

Here are some other common bottlenecks that can affect cloud server performance:

CPU and Memory Bottlenecks Detection

For example, we have a web application running on an AWS t2.micro instance with 1 vCPU and 1 GB of memory. In general, we check CPU and Memory Bottlenecks Detection by maximum CPU and memory utilization

vCPU: 1 Memory: 1 GB CPU Utilization: Percentage of time CPU is busy Memory Utilization = Percentage of memory used

Check the percentage of time the CPU is busy using the “top” command. It should look something like this:

%Cpu(s):  1.0 us,  1.0 sy,  0.0 ni, 98.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st

The “%Cpu(s)” line displays the CPU usage information as a breakdown of different types of CPU usage.

Formula: Percentage of time CPU is busy = 100 - “id” value

To check the percentage of time the CPU is busy, using above Formula

Percentage of time the CPU is busy = 100% - 98.0% = 2.0%

Check the percentage of memory used using the commands “free “or “free -m”(Memory usage information in megabytes (MB)). It should look something like this:

total       used       free     shared    buffers     cached
Mem:       8175876    3068764    5107112      42804     128820    1309372
-/+ buffers/cache:    1621572    6554304
Swap:      4194300          0    4194300

Formula: Percentage of memory used = (“used” / “total”) * 100

To check the percentage of time the memory used, using above Formula

Percentage of memory used = (3068764 / 8175876) * 100 = 37.5%

Generally speaking, if the CPU utilization or Memory Usage consistently exceeds 80-90% over an extended period of time, it could indicate a potential bottleneck.

Disk I/O Bottlenecks Detection

For example, we have a database running on an AWS EC2 instance with with a high volume of read and write operations.

In general, the maximum Disk I/O throughput you can achieve is dependent on the Disk I/O capacity and the Workload demand of the application. Three main areas check for Disk I/O bottlenecks

Check Disk I/O Utilization and Disk I/O Throughput under Linux using iostat

$ sudo apt-get install sysstat -y

$ iostat -d -k -x
Linux 5.15.0-56-generic (VM-0-6-ubuntu) 	04/22/23 	_x86_64_	(2 CPU)

Device            r/s     rkB/s   rrqm/s  %rrqm r_await rareq-sz     w/s     wkB/s   wrqm/s  %wrqm w_await wareq-sz     d/s     dkB/s   drqm/s  %drqm d_await dareq-sz     f/s f_await  aqu-sz  %util
loop0            0.78      6.31     0.00   0.00    0.16     8.07    0.00      0.00     0.00   0.00    0.00     0.00    0.00      0.00     0.00   0.00    0.00     0.00    0.00    0.00    0.00   0.03
loop4            0.41     14.63     0.00   0.00    0.06    35.74    0.00      0.00     0.00   0.00    0.00     0.00    0.00      0.00     0.00   0.00    0.00     0.00    0.00    0.00    0.00   0.13

Disk I/O Utilization: Percentage of time the disk is busy

Disk I/O Throughput includes multiple metrics, including:

Another thing, the iostat command can provide Disk I/O statistics for each device attached to the system. In this example, we do have two devices(loop0 and loop4) attached to system, use lsof commands to identify which devices are associated with specific workloads or applications.

$ sudo lsof /dev/loop2
COMMAND PID USER  FD   TYPE DEVICE SIZE/OFF NODE NAME
snapd   804 root txt    REG    7,4 32485984  150 /snap/snapd/17883/usr/lib/snapd/snapd

Let’s say /snap/snapd/17883/usr/lib/snapd/snapd is our appliction that need to check bottleneck, and you can see snapd process has a file descriptor (FD) open on the /dev/loop2 device, /dev/loop2 using by it😊. now go back to check value on device loop4. Let’s say check %util. If the %util value for a device is consistently high (typically above 90%), it may indicate that the device is experiencing a bottleneck and is unable to handle the current I/O load.

Workload: High volume of read and write operations, a few key values in the iostat output that can indicate workload:

$ iostat -d -p loop4 1
Linux 5.15.0-56-generic (VM-0-6-ubuntu) 	04/22/23 	_x86_64_	(2 CPU)

Device             tps    kB_read/s    kB_wrtn/s    kB_dscd/s    kB_read    kB_wrtn    kB_dscd
loop4             0.23         8.05         0.00         0.00      74554          0          0

Network Congestion Detection

To check for network congestion, you can use various tools and techniques depending on the specific network and environment you are working with. Here are a few options:

$ sudo apt install traceroute   

$ traceroute google.com
traceroute to google.com (142.250.204.78), 30 hops max, 60 byte packets
 1  * 11.84.200.225 (11.84.200.225)  0.563 ms 11.84.200.193 (11.84.200.193)  0.409 ms
 2  * * *
 3  * * *
 4  10.196.94.161 (10.196.94.161)  1.080 ms 10.162.32.86 (10.162.32.86)  1.362 ms 10.196.94.137 (10.196.94.137)  1.007 ms
 5  10.162.32.98 (10.162.32.98)  0.875 ms 72.14.223.150 (72.14.223.150)  2.603 ms 10.162.32.98 (10.162.32.98)  1.378 ms
 6  72.14.222.196 (72.14.222.196)  1.515 ms  1.265 ms 108.170.241.1 (108.170.241.1)  1.280 ms
 7  * 108.170.241.1 (108.170.241.1)  1.255 ms  1.136 ms
 8  108.170.233.20 (108.170.233.20)  0.678 ms 72.14.232.155 (72.14.232.155)  1.261 ms 72.14.235.205 (72.14.235.205)  1.342 ms
 9  108.170.241.80 (108.170.241.80)  0.940 ms 209.85.142.193 (209.85.142.193)  2.145 ms 216.239.63.216 (216.239.63.216)  14.264 ms
10  hkg07s39-in-f14.1e100.net (142.250.204.78)  1.394 ms  1.318 ms 216.239.63.216 (216.239.63.216)  1.715 ms

- hops: This is the maximum number of hops that the traceroute command will send before stopping. In this example, we are allowing up to 30 hops.

- packets: This is the packet size being sent in each hop. In this example, the packet size is 60 bytes.

- Latency: This is the response time or round-trip time for each hop along the path. A high latency value can indicate network congestion or other issues. The latency is usually measured in milliseconds (ms). The average latency of more than 100 milliseconds (ms) is considered high for a local network, while an average latency of more than 500ms is considered high for a global network.

- Packet loss: Some versions of traceroute also include packet loss statistics for each hop. A high packet loss rate can indicate network congestion or other issues.

- Router information: The output of traceroute also includes information about each router along the path, including its IP address and hostname. This information can be useful for troubleshooting network connectivity issues.

- hkg07s39-in-f14.1e100.net (142.250.204.78): Destination in this case(A Google server located in Hong Kong)

- *: In this case line2, line3 lines indicate that the traceroute did not receive a response from that hop within the allotted time.

Bandwidth Detection

For example, we have a 2 GB package on an AWS t2.micro, which is the smallest instance size in the t2 family, provides a maximum bandwidth of up to 5 Gbps for network traffic. In general, the maximum download speed you can achieve is typically around 80% of the available bandwidth.

Bandwidth: 5 Gbps Package Size: 2GB

Formula 1: Available bandwidth = Bandwidth * 80%

Formula 2: Maximum download time = Package Size / Available bandwidth

Formula 3: Maximum download speed = Package size / Maximum download time

Step 1: Calculate Available Bandwidth using Formula 1

    4Gbps(Available bandwidth) = 5 Gbps x  80%

Step 2: Calculate Maximum download time using Formula 2

    500 seconds(Maximum download time) = 2GB/4Gbps

Step 3: Calculate Maximum download speed using Formula 3

    4 MB/s(Maximum download speed) = 2GB/500 seconds

Now you got the Maximum download speed 4 MB/s, if you find you download speed achieving a Maximum download speed, and you still feel that the download speed is slow and not meeting your requirements, bandwidth is possibly the bottleneck. Consider increasing the available bandwidth for your AWS instance

Server Load Detection

There are a few ways to check the server load on a Linux-based system. Here are some common methods:

Database Bottleneck Detection

To check for database bottlenecks on Linux, you can use a variety of tools and techniques. Here are some steps you can take to identify and resolve database performance issues:

By following these steps, you should be able to identify and resolve database bottlenecks on your Linux system.

SQL Queries Type

There are several types of SQL queries that can cause slow query performance, including:

To improve query performance, it’s important to optimize the database schema, use indexes, and analyze slow queries using tools like the MySQL slow query log or performance monitoring tools. Additionally, it’s important to write efficient SQL code, use proper indexing techniques, and avoid unnecessary joins, subqueries, and other complex SQL constructs when possible.

Add index for slow SQL

// This will add an index on the timestamp column in the mytable table, which can help improve the performance of queries that filter data based on this column. ALTER TABLE mytable ADD INDEX idx_timestamp (timestamp);


- Queries that join multiple tables: Adding indexes on the columns used in the join condition can help improve the performance of join queries. For example, if you have a users table and an orders table that are joined on the user_id column, you could use the following query:

// Adding an index on the user_id column in the orders table can help improve the performance of this query. SELECT * FROM users JOIN orders ON users.id = orders.user_id;

// This will add an index on the user_id column in the orders table, which can help improve the performance of join queries that join the orders and users tables on this column. ALTER TABLE orders ADD INDEX idx_user_id (user_id);


- Queries that use subqueries or nested queries: Adding indexes on the columns used in the subquery or nested query can help improve the performance of these types of queries. For example, if you have a products table and you want to retrieve all products that have a higher price than the average price of all products, you could use the following query:

// Adding an index on the price column can help improve the performance of this query. SELECT * FROM products WHERE price > (SELECT AVG(price) FROM products);

// This will add an index on the price column in the products table, which can help improve the performance of queries that use subqueries or nested queries to filter data based on this column. ALTER TABLE products ADD INDEX idx_price (price);


- Queries that use complex or inefficient WHERE clauses: Adding indexes on the columns used in the WHERE clause can help improve the performance of these types of queries. For example, if you have a users table and you want to retrieve all users who are older than 30 and live in a specific city, you could use the following query:

// Adding indexes on the age and city columns can help improve the performance of this query. SELECT * FROM users WHERE age > 30 AND city = ‘New York’;

// These commands will add indexes on the age and city columns in the users table, which can help improve the performance of queries that filter data based on these columns. ALTER TABLE users ADD INDEX idx_age (age); ALTER TABLE users ADD INDEX idx_city (city);


- Queries that use ORDER BY or GROUP BY clauses: Adding indexes on the columns used in the ORDER BY or GROUP BY clause can help improve the performance of these types of queries. For example, if you have a users table and you want to retrieve all users grouped by city, you could use the following query:

// Adding an index on the city column can help improve the performance of this query. SELECT city, COUNT(*) FROM users GROUP BY city;

// This will add an index on the city column in the users table, which can help improve the performance of queries that group or sort data based on this column. ALTER TABLE users ADD INDEX idx_city (city);


- Queries that use unoptimized or inefficient SQL code: Rewriting the SQL code to be more efficient or optimized can help improve the performance of these types of queries. Adding indexes on the columns used in the optimized query can further improve performance.


#### Tips


##### Composite index
Composite index  is an index that includes multiple columns. Here's an example of how to create a composite index on the col1 and col2 columns in the mytable table:

ALTER TABLE mytable ADD INDEX idx_col1_col2 (col1, col2);

This will create a composite index on the col1 and col2 columns, which means that the index will be used to optimize queries that filter, sort, or group data based on these columns. For example, the following query will benefit from the composite index:

SELECT * FROM mytable WHERE col1 = ‘value’ AND col2 = ‘other_value’;


In this case, the database engine will use the composite index to quickly find the rows that match the filter condition on col1 and col2.

In general, the database engine will choose the most efficient index to use for a given query based on the available indexes and the query's filtering, sorting, and grouping requirements. However, there may be cases where the database engine chooses an inefficient index or no index at all, in which case you can use the USE INDEX clause to specify a different index to use.

For example, consider a table with a composite index on columns col1 and col2, and a query that filters data based on col1 only:

SELECT * FROM mytable WHERE col1 = ‘value’;

In this case, the database engine will automatically use the composite index to optimize the query, as the index includes the col1 column. You do not need to specify the index name in the query.

However, if you have a different index on col1 alone that you know to be more efficient for this query, you could use the USE INDEX clause to force the database engine to use that index instead:

SELECT * FROM mytable USE INDEX (idx_col1) WHERE col1 = ‘value’;


This will force the database engine to use the idx_col1 index instead of the composite index on col1 and col2.


##### using index in Python
hen you use the filter method in SQLAlchemy, the database engine will automatically choose the most efficient index to use for the query based on the available indexes and the query conditions.

If you want to specify a specific index to use in the query, you can use the with_index method in SQLAlchemy. Here's an example:

results = session.query(MyModel).with_index(MyModel.idx_age).filter(MyModel.age > 30).all()




##### Show index of ur table

SHOW INDEX FROM mytable;


##### checking index for our table

Demo1

mysql> Explain SELECT * FROM mytable USE INDEX (idx_id) WHERE id <1000; +—-+————-+———+————+——-+—————+——–+———+——+——+———-+———————–+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +—-+————-+———+————+——-+—————+——–+———+——+——+———-+———————–+ | 1 | SIMPLE | mytable | NULL | range | idx_id | idx_id | 4 | NULL | 999 | 100.00 | Using index condition | +—-+————-+———+————+——-+—————+——–+———+——+——+———-+———————–+ 1 row in set, 1 warning (0.00 sec)


Demo2

mysql> EXPLAIN SELECT * FROM mytable WHERE id >1; +—-+————-+———+————+——-+—————-+———+———+——+———+———-+————-+ | id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra | +—-+————-+———+————+——-+—————-+———+———+——+———+———-+————-+ | 1 | SIMPLE | mytable | NULL | range | PRIMARY,idx_id | PRIMARY | 4 | NULL | 1588125 | 100.00 | Using where | +—-+————-+———+————+——-+—————-+———+———+——+———+———-+————-+ 1 row in set, 1 warning (0.00 sec)

Here's a breakdown of the output from the EXPLAIN statement above:

- id column: shows the sequence in which MySQL is executing the query. In this case, there is only one row in the output, so the value is 1.
- select_type column: shows the type of query being executed. In this case, the query type is SIMPLE, which means that it's a simple SELECT statement with no subqueries or UNIONs.
- table column: shows the name of the table being queried. In this case, the table name is mytable.
- partitions column: shows whether the query is accessing a specific partition of a partitioned table. In this case, the value is NULL, which means that the query is not accessing a partitioned table.
- type column: shows the type of access method being used to retrieve the data. In this case, the access method is range, which means that MySQL is using an index to retrieve a range of rows that match the query conditions.
- possible_keys column: shows which indexes MySQL could potentially use to optimize the query. In this case, the possible keys are PRIMARY and idx_id, which are the primary key and a non-unique index on the id column, respectively.
- key column: shows which index MySQL is actually using to optimize the query. In this case, MySQL is using the PRIMARY index to optimize the query, as indicated by the value PRIMARY.
- key_len column: shows the length of the index being used, in bytes. In this case, the PRIMARY index is using 4 bytes to store the value of the id column.
- ref column: shows which columns or constants are being used with the index to retrieve the data. In this case, the value is NULL, which means that no columns or constants are being used with the index.
- rows column: shows the estimated number of rows that MySQL needs to examine to execute the query. In this case, MySQL estimates that it will need to examine 999 rows to retrieve the data.
- filtered column:  shows the percentage of rows that MySQL expects to examine, based on the query conditions and the available statistics. In
- Extra column: Using where, whichWHERE clause to filter the data





Slow query log: The slow query log is a MySQL feature that logs all queries that take longer than a specified amount of time to execute. By default, the slow query log is not enabled, so you'll need to enable it by modifying your MySQL configuration file (my.cnf or my.ini). Once the slow query log is enabled, you can review it periodically to identify queries that are taking too long to execute.
To enable the slow query log, add the following lines to your my.cnf or my.ini file:

slow_query_log = 1 slow_query_log_file = /path/to/slow-query.log long_query_time = 1


This will enable the slow query log, specify the log file location, and set the threshold for a "slow" query to 1 second. You can adjust the long_query_time value to a different threshold as needed.

Once the slow query log is enabled, you can review it by opening the log file and looking for queries that took longer than the specified threshold to execute. You can then optimize these queries or adjust your MySQL configuration to improve performance.

Performance_schema database: The performance_schema database is a MySQL feature that provides detailed information about MySQL server performance. It contains tables that track events such as query execution, table access, and resource usage.
To use the performance_schema database, you'll need to enable it by modifying your MySQL configuration file. Here's an example of how to enable the performance_schema database:

performance_schema = ON


Once the performance_schema database is enabled, you can use SQL queries to query the various tables in the database to gather detailed information about MySQL server performance. For example, you can use the performance_schema.events_statements_summary_by_digest table to view statistics about query execution, such as the total number of queries executed, the total execution time, and the average execution time.

By using the slow query log and performance_schema database together, you can gain a comprehensive understanding of your MySQL server performance and identify bottlenecks that may be affecting performance.

Time: 2022-05-01T12:34:56.789012Z

User@Host: myuser[123.45.67.89:12345]

Query_time: 5.123456 Lock_time: 0.000000 Rows_sent: 0 Rows_examined: 0

SET timestamp=1651439696; SELECT * FROM mytable WHERE id = 123456; In this example, the slow query log entry shows that a SELECT query took 5.123456 seconds to execute, with no lock time and no rows sent or examined. The log entry also shows the user and host that executed the query, the timestamp of the query, and the query itself.


When analyzing the slow query log, there are several values that you should pay attention to:

- Query execution time
    - This is the amount of time it takes for the query to execute, as measured by the MySQL server.
    - Threshold: Any query that takes longer than a few seconds to execute may be a candidate for optimization.

- Lock time
    - This is the amount of time that the query holds locks on tables or rows, which can impact the performance of other queries running on the same database server.
    - Threshold: Queries that hold locks on tables or rows for more than a few seconds can impact the performance of other queries running on the same database server.

- Rows examined
    - This is the number of rows that the query examines to retrieve the result set. Queries that examine a large number of rows can be slower than queries that examine a smaller number of rows.
    - Threshold: Queries that examine a large number of rows can be slower than queries that examine a smaller number of rows. You should pay attention to queries that examine more than a few thousand rows and consider optimizing them to reduce the number of examined rows.

- Index usage
    - This indicates whether the query is using indexes to retrieve the data, which can help improve performance.
    - Queries that do not use indexes or use indexes inefficiently can be slower than queries that use indexes efficiently. You should pay attention to queries that do not use indexes or have low index efficiency and consider optimizing them by adding or modifying indexes.

Regarding the performance_schema database, there are several tables and columns that you can use to identify database bottlenecks. Here are a few examples:

- performance_schema.events_statements_summary_by_digest: This table provides a summary of query execution statistics, including the number of queries executed, the total execution time, and the average execution time. You can use this table to identify queries that are taking a long time to execute and may be causing performance issues.
    - SUM_TIMER_WAIT: This column shows the total execution time for each query in microseconds. You should monitor this column to identify queries that are taking a long time to execute, as these queries may be causing performance issues. This column shows the total execution time for each query in microseconds. You might want to start paying attention to queries with a SUM_TIMER_WAIT value that exceeds a few seconds, as these queries may be taking a long time to execute and may be causing performance issues. However, the specific threshold will depend on the performance requirements of your application and the resources available on your server.

    - EXECUTIONS: This column shows the total number of times each query has been executed. You should monitor this column to identify queries that are executed frequently, as these queries may be a good target for optimization. This column shows the total number of times each query has been executed. You might want to start paying attention to queries with a high EXECUTIONS value, as these queries may be executed frequently and may be a good target for optimization. However, the specific threshold will depend on the available CPU and memory resources on your server, as well as the performance requirements of your application.

    - AVG_TIMER_WAIT: This column shows the average execution time for each query in microseconds. You should monitor this column to identify queries that are consistently slow, as these queries may be a good target for optimization.This column shows the average execution time for each query in microseconds. You might want to start paying attention to queries with an AVG_TIMER_WAIT value that exceeds a few hundred milliseconds, as these queries may be consistently slow and may be a good target for optimization. However, the specific threshold will depend on the performance requirements of your application and the acceptable response time for your users.

    performance_schema.events_statements_summary_by_digest table with some sample content:

+————————————————————————————————————————+————-+————+—————-+———————–+———————–+————————+————————+ | DIGEST | SCHEMA_NAME | EXEC_COUNT | SUM_TIMER_WAIT | SUM_LOCK_TIME | SUM_ROWS_SENT | SUM_ROWS_EXAMINED | SUM_CREATED_TMP_DISK_TABLES | +————————————————————————————————————————+————-+————+—————-+———————–+———————–+————————+————————+ | 5c5e5d5e5c5d5e5d5c5d5e5c5d5e5d5e5c5d5e5d5c5d5e5d5e5c5d5e5d5e5c5d5e5d5e5c5d5e5d5c5d5e5d5e5c5d5e5d5c5d5e | test | 1 | 30000 | 0 | 10 | 1000 | 0 | | 5d5e5c5d5e5c5d5e5c5d5e5c5d5e5d5e5c5d5e5d5c5d5e5d5e5c5d5e5d5e5c5d5e5d5e5c5d5e5d5c5d5e5d5c5d5e5d5c5d5e | test | 2 | 40000 | 0 | 20 | 2000 | 0 | | 5e5c5d5e5c5d5e5d5c5d5e5c5d5e5d5e5c5d5e5d5c5d5e5d5e5c5d5e5d5e5c5d5e5d5e5c5d5e5d5c5d5e5d5c5d5e5d5c5d5e | test | 3 | 50000 | 0 | 30 | 3000 | 0 | +————————————————————————————————————————+————-+————+—————-+———————–+———————–+————————+————————+

In this example, the performance_schema.events_statements_summary_by_digest table includes the following columns:

- DIGEST: This column contains a hash value or fingerprint of the SQL statement.

- SCHEMA_NAME: This column contains the name of the schema where the query was executed.

- EXEC_COUNT: This column contains the number of times the query was executed.

- SUM_TIMER_WAIT: This column contains the total execution time of the query, in microseconds.

- SUM_LOCK_TIME: This column contains the total time the query spent waiting for locks, in microseconds.

- SUM_ROWS_SENT: This column contains the total number of rows sent by the query.

- SUM_ROWS_EXAMINED: This column contains the total number of rows examined by the query.

- SUM_CREATED_TMP_DISK_TABLES: This column contains the total number of temporary disk tables created by the query.

The DIGEST column in the performance_schema.events_statements_summary_by_digest table contains a hash value or fingerprint of the SQL statement, which can be difficult for humans to read and understand. However, you can use other tables in the performance_schema database, such as performance_schema.events_statements_history, to retrieve the actual SQL statements that correspond to each DIGEST value.

For example, you can use the following query to retrieve the SQL statements for the top 10 queries with the highest SUM_TIMER_WAIT value:

SELECT DIGEST, SCHEMA_NAME, EXEC_COUNT, SUM_TIMER_WAIT, LEFT(DIGEST_TEXT, 100) AS SQL_STATEMENT FROM performance_schema.events_statements_summary_by_digest JOIN performance_schema.events_statements_history ON events_statements_history.digest = events_statements_summary_by_digest.digest ORDER BY SUM_TIMER_WAIT DESC LIMIT 10; ``` In this query, the LEFT function is used to retrieve only the first 100 characters of the DIGEST_TEXT column, which contains the actual SQL statement.

To determine the appropriate thresholds for your environment, you should monitor the performance_schema.events_statements_summary_by_digest table over time, and use the data to identify trends and potential bottlenecks.

Here are some general guidelines for the values you might want to pay attention to in this table:

To determine the appropriate thresholds for your environment, you should monitor the performance_schema.file_summary_by_instance table over time

Here are some general guidelines for the values you might want to pay attention to in this table:

To determine the appropriate thresholds for your environment, you should monitor the performance_schema.table_io_waits_summary_by_index_usage table over time, and use the data to identify trends and potential bottlenecks.

To determine the appropriate thresholds for your environment, you should monitor the performance_schema.memory_summary_by_instance table over time, and use the data to identify trends and potential memory-related issues.

1. Code Techniques Level

pass

2. Web Framework Level

There are several ways to check the performance of your Flask web app before and after adding a web server such as uWSGI or Gunicorn. Here are some steps you can follow:

In summary, using benchmarking and load testing tools can help you identify and address performance issues in your web app before they become a problem, and ensure that your app can handle high concurrency traffic with ease.

Testing an API that involves multiple systems such as Redis, MySQL, and a message queue, you can use a single benchmarking tool to test the entire system and measure the overall performance. This can be more convenient and efficient than testing each system separately.

However, there are some cases where testing each system separately may be useful. For example, if you suspect that one of the systems is a performance bottleneck, testing it separately can help you identify the issue more quickly and accurately. It can also help you isolate the issue and avoid false positives or conflicting results.

Additionally, testing each system separately can help you optimize each system independently and improve its performance. For example, if you find that your Redis cache is slow or unable to handle the load, you can optimize it separately from your message queue or MySQL database.

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