I am thinking to migrate my website to Google Cloud SQL and I signed up for a free account (D32).
Upon testing on a table with 23k records the performances were very poor so I read that if I move from the free account to a full paid account I would have access to faster CPU and HDD... so I did.
performances are still VERY POOR.
I am running my own MySQL server for years now, upgrading as needed to handle more and more connections and to gain raw speed (needed because of a legacy application). I highly optimize tables, configuration, and heavy use of query cache, etc...
A few pages of our legacy system have over 1.5k of queries per page, currently I was able to push the mysql query time (execution and pulling of the data) down to 3.6seconds for all those queries, meaning that MySQL takes about 0.0024 seconds to execute the queries and return the values.. not the greatest but acceptable for those pages.
I upload a table involved in those many queries to Google Cloud SQL. I notices that the INSERT already takes SECONDS to execute instead than milliseconds.. but I think that it might be the sync vs async setting. I change it to async and the execution time for the insert doesn't feel like it changes. for now not a big problem, I am only testing queries for now.
I run a simple select * FROM <table> and I notice that it takes over 6 seconds.. I think that maybe the query cache needs to build.. i try again and this times it takes 4 seconds (excluding network traffic). I run the same query on my backup server after a restart and with no connections at all, and it takes less than 1 second.. running it again, 0.06 seconds.
Maybe the problem is the cache, too big... let's try a smaller subset
select * from <table> limit 5;
to my server: 0.00 seconds
GCS: 0.04
so I decide to try a dumb select on an empty table, no records at all, just created with only 1 field
to my server: 0.00 seconds
GCS: 0.03
profiling doesn't give any insights except that the query cache is not running on Google Cloud SQL and that the queries execution seems faster but .. is not...
My Server:
mysql> show profile;
+--------------------------------+----------+
| Status | Duration |
+--------------------------------+----------+
| starting | 0.000225 |
| Waiting for query cache lock | 0.000116 |
| init | 0.000115 |
| checking query cache for query | 0.000131 |
| checking permissions | 0.000117 |
| Opening tables | 0.000124 |
| init | 0.000129 |
| System lock | 0.000124 |
| Waiting for query cache lock | 0.000114 |
| System lock | 0.000126 |
| optimizing | 0.000117 |
| statistics | 0.000127 |
| executing | 0.000129 |
| end | 0.000117 |
| query end | 0.000116 |
| closing tables | 0.000120 |
| freeing items | 0.000120 |
| Waiting for query cache lock | 0.000140 |
| freeing items | 0.000228 |
| Waiting for query cache lock | 0.000120 |
| freeing items | 0.000121 |
| storing result in query cache | 0.000116 |
| cleaning up | 0.000124 |
+--------------------------------+----------+
23 rows in set, 1 warning (0.00 sec)
Google Cloud SQL:
mysql> show profile;
+----------------------+----------+
| Status | Duration |
+----------------------+----------+
| starting | 0.000061 |
| checking permissions | 0.000012 |
| Opening tables | 0.000115 |
| System lock | 0.000019 |
| init | 0.000023 |
| optimizing | 0.000008 |
| statistics | 0.000012 |
| preparing | 0.000005 |
| executing | 0.000021 |
| end | 0.000024 |
| query end | 0.000007 |
| closing tables | 0.000030 |
| freeing items | 0.000018 |
| logging slow query | 0.000006 |
| cleaning up | 0.000005 |
+----------------------+----------+
15 rows in set (0.03 sec)
keep in mind that I connect to both server remotely from a server located in VA and my server is located in Texas (even if it should not matter that much).
What am I doing wrong ? why simple queries take this long ? am I missing or not understanding something here ?
As of right now I won't be able to use Google Cloud SQL because a page with 1500 queries will take way too long (circa 45 seconds)
I know this question is old but....
CloudSQL has poor support for MyISAM tables, it's recommend to use InnoDB.
We had poor performance when migrating a legacy app, after reading through the doc's and contacting the paid support, we had to migrate the tables into InnoDB; No query cache was also a killer.
You may also find later on you'll need to tweak the mysql conf via the 'flags' in the google console. An example being 'wait_timeout' is set too high by default (imo.)
Hope this helps someone :)
Query cache is not as yet a feature of Cloud SQL. This may explain the results. However, I recommend closing this question as it is quite broad and doesn't fit the format of a neat and tidy Q&A. There are just too many variables not mentioned in the Q&A and it doesn't appear clear what a decisive "answer" would look like to the very general question of optimization when there are so many variables at play.
Related
I'm new to flyway & have been going through the documentation of flyway but couldn't find a doc which describes what each column in schema_version_history (or whatever you would have configured to name the flyway table) means. I'm specifically intrigued by the column named "type". So far the possible values for this column that I've observed in some legacy project at work are SQL & DELETE.
But I have no clue what this means in terms of flyway migrations.
Below are some sample rows from the table. Note that for installed rank 54 & 56, same migration file is present with same checksum but one has type SQL and another has DELETE.
-[ RECORD 53 ]-+---------------------------------------------------------------------------------------------------
installed_rank | 54
version | 2022.11.18.11.35.49.65
description | add column seqence in attribute table
type | SQL
script | V2022_11_18_11_35_49_65__add_column_seqence_in_attribute_table.sql
checksum | 408921517
installed_by | postgres
installed_on | 2022-11-18 12:04:47.652058
execution_time | 345
success | t
-[ RECORD 54 ]-+---------------------------------------------------------------------------------------------------
installed_rank | 55
version | 2022.11.15.14.17.44.36
description | update address column in attribute table
type | DELETE
script | V2022_11_15_14_17_44_36__update_address_column_in_attribute_table.sql
checksum | 1347853326
installed_by | postgres
installed_on | 2022-11-18 14:52:09.265902
execution_time | 0
success | t
-[ RECORD 55 ]-+---------------------------------------------------------------------------------------------------
installed_rank | 56
version | 2022.11.18.11.35.49.65
description | add column seqence in attribute table
type | DELETE
script | V2022_11_18_11_35_49_65__add_column_seqence_in_attribute_table.sql
checksum | 408921517
installed_by | postgres
installed_on | 2022-11-18 14:52:09.265902
execution_time | 0
success | t
-[ RECORD 56 ]-+---------------------------------------------------------------------------------------------------
installed_rank | 58
version | 2022.11.18.11.35.49.65
description | add column seqence in attribute table
type | SQL
script | V2022_11_18_11_35_49_65__add_column_seqence_in_attribute_table.sql
checksum | 408921517
installed_by | postgres
installed_on | 2022-12-09 14:01:59.352589
execution_time | 174
success | t
Great question. This is as close as I got to documentation on that table:
https://www.red-gate.com/hub/product-learning/flyway/exploring-the-flyway-schema-history-table
That article doesn't really describe the type column well at all, suggesting it only has two possible values and I've seen at least three; DELETE, SQL and JDBC. Not sure what else it may have.
EDIT: Also now confirmed these two values; BASELINE and UNDO_SQL
It's actually marked as intentionally not documented since it's not a part of the public API:
https://flywaydb.org/documentation/learnmore/faq#case-sensitive
I was trying to implement code through flyway:
create index concurrently if not exists api_client_system_role_idx2 on profile.api_client_system_role (api_client_id);
create index concurrently if not exists api_client_system_role_idx3 on profile.api_client_system_role (role_type_id);
create index concurrently if not exists api_key_idx2 on profile.api_key (api_client_id);
However flyway sessions were blocking each other and script is in "pending" state.
| Versioned | 20.1 | add email verification table | SQL | 2021-11-01 21:55:52 | Success |
| Versioned | 21.1 | create role for doc api | SQL | 2021-11-01 21:55:52 | Success |
| Versioned | 22 | create indexes for profile | SQL | 2022-10-21 10:23:41 | Success |
| Versioned | 23 | test flyway | SQL | | Pending |
+-----------+---------+----------------------------------------------+--------+---------------------+---------+
Flyway: Flyway Community Edition 9.3.1 by Redgate
Database: Postgresql 14.4
Can you please advice how to properly implement creating indexes concurrently in postgresql?
I've tried simply to kill blocking session and let the script to continue, however then implementation failed and scripts stayed in "Pending" status.
Update:
It is solved. Please check myself's answer if you are interested in it. Thanks to everyone all the same!
My original post:
MongoDB server version: 3.6.8 (WSL Ubuntu 20.04)
pymongo 4.1.0
I am learning machine learning. Because I feel TensorBoard is hard to use, I try to implement a simple "traceable and visible training system" ("tvts") that has partial features of TensorBoard by MongoDB and pymongo. I choose MongoDB because it is document-based, NoSQL, and more suitable for recording arbitrary properties of model training.
Below is how I use it to record training conditions:
import tvts
# before training the modle
ts = tvts.tvts(NAME, '172.26.41.157', init_params={
'ver': VER,
'batch_size': N_BATCH_SIZE,
'lr': LR,
'n_epoch': N_EPOCH,
}, save_dir=SAVE_DIR, save_freq=SAVE_FREQ)
# after an epoch is done
ts.save(epoch, {
'cost': cost_avg,
'acc': metrics_avg[0][0],
'cost_val': cost_avg_val,
'acc_val': metrics_avg_val[0][0],
}, save_path)
I write all such data into a collection of my MondoDB, and then I can get statistics and charts like below:
Name: mnist_multi_clf_by_numpynet_mlp_softmax_ok from 172.26.41.157:27017 tvts.train_log
+----------+-----------+-----------------+-----------------+-------------+-------------------+----------------------------+----------------------------+----------------+
| train_id | parent | cost(min:last) | LR(b-e:max-min) | epoch_count | existed save/save | from | to | duration |
+----------+-----------+-----------------+-----------------+-------------+-------------------+----------------------------+----------------------------+----------------+
| 1 | None-None | 1.01055:1.01055 | 0.1:0.1 | 100 | 10/10 | 2022-04-14 11:56:17.618000 | 2022-04-14 11:56:21.273000 | 0:00:03.655000 |
+----------+-----------+-----------------+-----------------+-------------+-------------------+----------------------------+----------------------------+----------------+
| 2 | 1-100 | 0.56357:0.56357 | 0.1:0.1 | 100 | 10/10 | 2022-04-14 12:00:53.170000 | 2022-04-14 12:00:56.705000 | 0:00:03.535000 |
+----------+-----------+-----------------+-----------------+-------------+-------------------+----------------------------+----------------------------+----------------+
| 3 | 2-100 | 0.15667:0.15667 | 0.1:0.1 | 300 | 15/15 | 2022-04-14 12:01:35.233000 | 2022-04-14 12:01:45.795000 | 0:00:10.562000 |
+----------+-----------+-----------------+-----------------+-------------+-------------------+----------------------------+----------------------------+----------------+
| 4 | 3-300 | 0.06820:0.06839 | 0.1:0.1 | 300 | 15/15 | 2022-04-14 18:16:08.720000 | 2022-04-14 18:16:19.606000 | 0:00:10.886000 |
+----------+-----------+-----------------+-----------------+-------------+-------------------+----------------------------+----------------------------+----------------+
| 5 | 2-100 | 0.03418:0.03418 | 0.5:0.5 | 200 | 10/10 | 2022-04-14 18:18:27.665000 | 2022-04-14 18:18:34.644000 | 0:00:06.979000 |
+----------+-----------+-----------------+-----------------+-------------+-------------------+----------------------------+----------------------------+----------------+
| 6 | None-None | 1.68796:1.68858 | 0.001:0.001 | 3000 | 30/30 | 2022-04-16 09:15:56.085000 | 2022-04-16 09:18:01.608000 | 0:02:05.523000 |
+----------+-----------+-----------------+-----------------+-------------+-------------------+----------------------------+----------------------------+----------------+
I found out that it get stuck if I try to get the list of statistics when I densely writing into the collection at the same time. I.e. I try to get the statistics on-the-fly of training and each epoch of the training is very short (about 0.03 second).
But I found out that I can still read out the records by Stuido 3T (a GUI of MongoDB) when I densely writing into the collection.
I googled a lot, but I still cannot solve it. Someone said the writing lock is exclusive (such as link: mongodb write is occuring then a read must wait or not wait?), but why the Studio 3T can make it?
Acturally I am new to MongoDB, I can use it because I have a littel experience with MySQL and in this "tvts" there is only insertion and query, i.e. it is a rahter simple usage of MongoDB. Is there some equivalent concepts of "concurrent inserts" in MySQL? (such as link: concurrent read and write in MySQL) I guess it is not a very hard task of MongoDB to read from it when writing into it.
Although it is a simple simulation of partial features of TensorBoard, I have already coded almost 600 lines of code. So, I am sorry that changing database is not prefered.
Please help me. Thanks a lot!
Unbelievable! I accidentally solved it just a few minutes after I posted this question. It seems that MongoDB collection could be read even if there are dense insertions, and I guess it is its normal performance. I guess that I cannot google an answer because it is not a real issue. My issue may be caused by the IDE Pycharm that I am using. I have the issue if I run my script inside Pycharm. It is OK when I run it in a system shell window.
I'm setting up airflow such that webserver runs on one machine and scheduler runs on another. Both share the same MySQL metastore database. Both instances come up without any errors in the logs but the scheduler is not picking up any DAG Runs that are created by manually triggering the DAGs via the Web UI.
The dag_run table in MysQL shows few entries, all in running state:
mysql> select * from dag_run;
+----+--------------------------------+----------------------------+---------+------------------------------------+------------------+----------------+----------+----------------------------+
| id | dag_id | execution_date | state | run_id | external_trigger | conf | end_date | start_date |
+----+--------------------------------+----------------------------+---------+------------------------------------+------------------+----------------+----------+----------------------------+
| 1 | example_bash_operator | 2017-12-14 11:33:08.479040 | running | manual__2017-12-14T11:33:08.479040 | 1 | �� }�. | NULL | 2017-12-14 11:33:09.000000 |
| 2 | example_bash_operator | 2017-12-14 11:38:27.888317 | running | manual__2017-12-14T11:38:27.888317 | 1 | �� }�. | NULL | 2017-12-14 11:38:27.000000 |
| 3 | example_branch_dop_operator_v3 | 2017-12-14 13:47:05.170752 | running | manual__2017-12-14T13:47:05.170752 | 1 | �� }�. | NULL | 2017-12-14 13:47:05.000000 |
| 4 | example_branch_dop_operator_v3 | 2017-12-15 04:26:07.208501 | running | manual__2017-12-15T04:26:07.208501 | 1 | �� }�. | NULL | 2017-12-15 04:26:07.000000 |
| 5 | example_branch_dop_operator_v3 | 2017-12-15 06:12:10.965543 | running | manual__2017-12-15T06:12:10.965543 | 1 | �� }�. | NULL | 2017-12-15 06:12:11.000000 |
| 6 | example_branch_dop_operator_v3 | 2017-12-15 06:28:43.282447 | running | manual__2017-12-15T06:28:43.282447 | 1 | �� }�. | NULL | 2017-12-15 06:28:43.000000 |
+----+--------------------------------+----------------------------+---------+------------------------------------+------------------+----------------+----------+----------------------------+
6 rows in set (0.21 sec)
But the Scheduler that's started up on another machine and connected to the same MySQL DB is just not interested in talking to this DB and actually running these DAG runs and converting them to Task Instances.
Not sure what I'm missing in the setup here. So few questions:
When and how is the DAGS folder located at $AIRFLOW_HOME/dags populated? I think its when the webserver is started. But then if I just start the scheduler on another machine, how will the DAGS folder on that machine be filled up?
Currently, I'm doing airflow initdb only on the machine hosting the webserver and not on scheduler. Hope that is correct.
Can I enable debug logs for Scheduler to get more logs that could indicate what's missing? From the current logs it looks like it just looks in the DAGS folder on local system and finds no DAGS there ( not even example ones ) inspite of the config to load examples set as True.
Don't think it matters but I'm currently using a LocalExecutor
Any help is appreciated.
Edit: I know that I need to sync up DAGS folder across machines as the airflow docs suggest but not sure if this is the reason why Scheduler is not picking up the tasks in the above case.
Ok, I got the answer - It looks like the Scheduler does not query the DB until there are any DAGS in the local DAG Folder. The code in job.py looks like
ti_query = (
session
.query(TI)
.filter(TI.dag_id.in_(simple_dag_bag.dag_ids))
.outerjoin(DR,
and_(DR.dag_id == TI.dag_id,
DR.execution_date == TI.execution_date))
.filter(or_(DR.run_id == None,
not_(DR.run_id.like(BackfillJob.ID_PREFIX + '%'))))
.outerjoin(DM, DM.dag_id==TI.dag_id)
.filter(or_(DM.dag_id == None,
not_(DM.is_paused)))
)
I added a simple DAG in my local DAG folder on the machine hosting Scheduler and it started picking up other DAG instances as well.
We raised an issue for this - https://issues.apache.org/jira/browse/AIRFLOW-1934
I am building an app that applies a datascience model on a SQL Database, for sensor metrics. For this purpose I chose PipelineDB (based on Postgres) that enables me to build a Continuous View on my metrics and apply the model to each new line.
For now, I just want to observe the metrics I collect through the sensor on a dashboard. The table "metrics" looks like this :
+---------------------+--------+---------+------+-----+
| timestamp | T (°C) | P (bar) | n | ... |
+---------------------+--------+---------+------+-----+
| 2015-12-12 20:00:00 | 20 | 1.13 | 0.9 | |
+---------------------+--------+---------+------+-----+
| 2015-12-13 20:00:00 | 20 | 1.132 | 0.9 | |
+---------------------+--------+---------+------+-----+
| 2015-12-14 20:00:00 | 40 | 1.131 | 0.96 | |
+---------------------+--------+---------+------+-----+
I'd like to build a dashboard in which I could see all my metric evolving through time. Even be able to choose which column to display.
So I found a few tools that could match with my need, which are Grafana or Chronograf for InfluxDB.
But neither of them enable me to plug directly on Postgres and query my table to generate metric-formatted data that is required by these tools.
Do you have any advice on what I should do to use such dashboards with such data ?
A bit late here, but Grafana now supports Postgresql datasources directly: https://grafana.com/docs/features/datasources/postgres. I've used it in several projects and it has been really easy to set up and use.