MySQL 8 ST_GeomFromGeoJSON is throwing longitude out of range error - geometry
I have a table with polygons stored as geojson.
CREATE TABLE `location_boundaries` (
`id` INT UNSIGNED NOT NULL,
`name` VARCHAR(255) NULL DEFAULT NULL COLLATE 'utf8mb4_unicode_ci',
`geo_json` JSON NULL DEFAULT NULL,
`geom` GEOMETRY NULL DEFAULT NULL,
PRIMARY KEY (`id`)
)
COLLATE='utf8mb4_unicode_ci'
ENGINE=InnoDB
I have geojson multipolygon for asia as follows in the table:
{"type": "MultiPolygon", "coordinates": [[[[-168.25, 77.7], [-180, 77.7], [-180, 58.1], [-168.25, 58.1], [-168.25, 77.7]]], [[[39.6908, 84.52666], [180, 84.38487], [180, 26.27883], [142.084541, 22.062707], [130.147, 3.608598], [141.1373, -1.666358], [141.0438, -9.784795], [130.2645, -10.0399], [118.2545, -13.01165], [102.7975, -8.388008], [89.50451, -11.1417], [61.62511, -9.103512], [51.62645, 12.54865], [44.20775, 11.6786], [39.78016, 16.56855], [31.60401, 31.58641], [33.27769, 34.00057], [34.7674, 34.85347], [35.72423, 36.32686], [36.5597, 37.66439], [44.1053, 37.98438], [43.01638, 41.27191], [41.28304, 41.41274], [36.26378, 44.40772], [36.61315, 45.58723], [37.48493, 46.80924], [38.27497, 47.61317], [39.56164, 48.43141], [39.77264, 50.58891], [39.6908, 84.52666]]]]}
When I run the following
UPDATE location_boundaries SET geom = ST_GeomFromGeoJSON(geo_json) where id = 6255147
I'm getting the following error:
Longitude -180.000000 is out of range in function st_geomfromgeojson. It must be within (-180.000000, 180.000000].")
What's going on. All this was working fine in mysql 5.7 yet in mysql 8 everything has messed up?
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N'Asia/Vientiane', N'Asia/Vladivostok', N'Asia/Yakutsk', N'Asia/Yangon', N'Asia/Yekaterinburg', N'Asia/Yerevan', N'Atlantic/Azores', N'Atlantic/Bermuda', N'Atlantic/Canary', N'Atlantic/Cape_Verde', N'Atlantic/Faeroe', N'Atlantic/Faroe', N'Atlantic/Jan_Mayen', N'Atlantic/Madeira', N'Atlantic/Reykjavik', N'Atlantic/South_Georgia', N'Atlantic/St_Helena', N'Atlantic/Stanley', N'Australia/ACT', N'Australia/Adelaide', N'Australia/Brisbane', N'Australia/Broken_Hill', N'Australia/Canberra', N'Australia/Currie', N'Australia/Darwin', N'Australia/Eucla', N'Australia/Hobart', N'Australia/LHI', N'Australia/Lindeman', N'Australia/Lord_Howe', N'Australia/Melbourne', N'Australia/NSW', N'Australia/North', N'Australia/Perth', N'Australia/Queensland', N'Australia/South', N'Australia/Sydney', N'Australia/Tasmania', N'Australia/Victoria', N'Australia/West', N'Australia/Yancowinna', N'Brazil/Acre', N'Brazil/DeNoronha', N'Brazil/East', N'Brazil/West', N'CET', N'CST6CDT', N'Canada/Atlantic', N'Canada/Central', N'Canada/Eastern', N'Canada/Mountain', N'Canada/Newfoundland', N'Canada/Pacific', N'Canada/Saskatchewan', N'Canada/Yukon', N'Chile/Continental', N'Chile/EasterIsland', N'Cuba', N'EET', N'EST', N'EST5EDT', N'Egypt', N'Eire', N'Etc/GMT', N'Etc/GMT+0', N'Etc/GMT+1', N'Etc/GMT+10', N'Etc/GMT+11', N'Etc/GMT+12', N'Etc/GMT+2', N'Etc/GMT+3', N'Etc/GMT+4', N'Etc/GMT+5', N'Etc/GMT+6', N'Etc/GMT+7', N'Etc/GMT+8', N'Etc/GMT+9', N'Etc/GMT-0', N'Etc/GMT-1', N'Etc/GMT-10', N'Etc/GMT-11', N'Etc/GMT-12', N'Etc/GMT-13', N'Etc/GMT-14', N'Etc/GMT-2', N'Etc/GMT-3', N'Etc/GMT-4', N'Etc/GMT-5', N'Etc/GMT-6', N'Etc/GMT-7', N'Etc/GMT-8', N'Etc/GMT-9', N'Etc/GMT0', N'Etc/Greenwich', N'Etc/UCT', N'Etc/UTC', N'Etc/Universal', N'Etc/Zulu', N'Europe/Amsterdam', N'Europe/Andorra', N'Europe/Astrakhan', N'Europe/Athens', N'Europe/Belfast', N'Europe/Belgrade', N'Europe/Berlin', N'Europe/Bratislava', N'Europe/Brussels', N'Europe/Bucharest', N'Europe/Budapest', N'Europe/Busingen', N'Europe/Chisinau', N'Europe/Copenhagen', N'Europe/Dublin', N'Europe/Gibraltar', N'Europe/Guernsey', N'Europe/Helsinki', N'Europe/Isle_of_Man', N'Europe/Istanbul', N'Europe/Jersey', N'Europe/Kaliningrad', N'Europe/Kiev', N'Europe/Kirov', N'Europe/Lisbon', N'Europe/Ljubljana', N'Europe/London', N'Europe/Luxembourg', N'Europe/Madrid', N'Europe/Malta', N'Europe/Mariehamn', N'Europe/Minsk', N'Europe/Monaco', N'Europe/Moscow', N'Europe/Nicosia', N'Europe/Oslo', N'Europe/Paris', N'Europe/Podgorica', N'Europe/Prague', N'Europe/Riga', N'Europe/Rome', N'Europe/Samara', N'Europe/San_Marino', N'Europe/Sarajevo', N'Europe/Saratov', N'Europe/Simferopol', N'Europe/Skopje', N'Europe/Sofia', N'Europe/Stockholm', N'Europe/Tallinn', N'Europe/Tirane', N'Europe/Tiraspol', N'Europe/Ulyanovsk', N'Europe/Uzhgorod', N'Europe/Vaduz', N'Europe/Vatican', N'Europe/Vienna', N'Europe/Vilnius', N'Europe/Volgograd', N'Europe/Warsaw', N'Europe/Zagreb', N'Europe/Zaporozhye', N'Europe/Zurich', N'GB', N'GB-Eire', N'GMT', N'GMT+0', N'GMT-0', N'GMT0', N'Greenwich', N'HST', N'Hongkong', N'Iceland', N'Indian/Antananarivo', N'Indian/Chagos', N'Indian/Christmas', N'Indian/Cocos', N'Indian/Comoro', N'Indian/Kerguelen', N'Indian/Mahe', N'Indian/Maldives', N'Indian/Mauritius', N'Indian/Mayotte', N'Indian/Reunion', N'Iran', N'Israel', N'Jamaica', N'Japan', N'Kwajalein', N'Libya', N'MET', N'MST', N'MST7MDT', N'Mexico/BajaNorte', N'Mexico/BajaSur', N'Mexico/General', N'NZ', N'NZ-CHAT', N'Navajo', N'PRC', N'PST8PDT', N'Pacific/Apia', N'Pacific/Auckland', N'Pacific/Bougainville', N'Pacific/Chatham', N'Pacific/Chuuk', N'Pacific/Easter', N'Pacific/Efate', N'Pacific/Enderbury', N'Pacific/Fakaofo', N'Pacific/Fiji', N'Pacific/Funafuti', N'Pacific/Galapagos', N'Pacific/Gambier', N'Pacific/Guadalcanal', N'Pacific/Guam', N'Pacific/Honolulu', N'Pacific/Johnston', N'Pacific/Kiritimati', N'Pacific/Kosrae', N'Pacific/Kwajalein', N'Pacific/Majuro', N'Pacific/Marquesas', N'Pacific/Midway', N'Pacific/Nauru', N'Pacific/Niue', N'Pacific/Norfolk', N'Pacific/Noumea', N'Pacific/Pago_Pago', N'Pacific/Palau', N'Pacific/Pitcairn', N'Pacific/Pohnpei', N'Pacific/Ponape', N'Pacific/Port_Moresby', N'Pacific/Rarotonga', N'Pacific/Saipan', N'Pacific/Samoa', N'Pacific/Tahiti', N'Pacific/Tarawa', N'Pacific/Tongatapu', N'Pacific/Truk', N'Pacific/Wake', N'Pacific/Wallis', N'Pacific/Yap', N'Poland', N'Portugal', N'ROC', N'ROK', N'Singapore', N'Turkey', N'UCT', N'US/Alaska', N'US/Aleutian', N'US/Arizona', N'US/Central', N'US/East-Indiana', N'US/Eastern', N'US/Hawaii', N'US/Indiana-Starke', N'US/Michigan', N'US/Mountain', N'US/Pacific', N'US/Pacific-New', N'US/Samoa', N'UTC', N'Universal', N'W-SU', N'WET', N'Zulu')), [clipTemplateId] NVARCHAR(255) NULL, [shopId] NVARCHAR(255) NULL, [createdAt] DATETIMEOFFSET NOT NULL, [updatedAt] DATETIMEOFFSET NOT NULL, PRIMARY KEY ([id]), FOREIGN KEY ([clipTemplateId]) REFERENCES [ClipTemplate] ([id]) ON DELETE NO ACTION, FOREIGN KEY ([shopId]) REFERENCES [Shop] ([id]) ON DELETE NO ACTION); Same format as the other queries, except it's got a list of timezones. I can see the previous queries in the audit log, but not this one. I've run SELECT OBJECT_ID('[Client]', 'U') and it returns 1218103380, so the query shouldn't actually be doing anything, given the IF at the top? Reproducable every time against multiple Azure SQL databases in different development environments with the node app. Other SQL clients send the query with success. App last worked yesterday. No app or infra changes since. When putting the query into some online SQL validators like https://www.eversql.com/sql-syntax-check-validator/, it says the query is invalid, but MS SQL Server Management Studio thinks it's fine, and I can run it without issue. Sequelize Config: database: { database: process.env.DATABASE_NAME, dialect: "mssql", host: process.env.DATABASE_HOST, loggingEnabled: false, password: process.env.DATABASE_PASSWORD, pool: { acquire: 30000, idle: 30000, max: 10, min: 0, }, port: parseInt(process.env.DATABASE_PORT, 10) || 1433, transactionTimeout: parseInt(process.env.DB_TRANSACTION_TIMEOUT, 10) || 30000, username: process.env.DATABASE_USERNAME, }
It seems 10.17.0 broke my DB connection for long queries. Rolling back to Node v10.16.3 has resolved the issue. On start up, my Node program was pulling the latest 10.x version. It started using Node v10.17.0, released 21st October 2019, which was when the issue started.
Here is a solution for timeout error while running long queries. connection timeout or already too many clients
Convert HQL to SparkSQL
I'm trying to convert HQL to Spark. I have the following query (Works in Hue with Hive editor): select reflect('java.util.UUID', 'randomUUID') as id, tt.employee, cast( from_unixtime(unix_timestamp (date_format(current_date(),'dd/MM/yyyy HH:mm:ss'), 'dd/MM/yyyy HH:mm:ss')) as timestamp) as insert_date, collect_set(tt.employee_detail) as employee_details, collect_set( tt.emp_indication ) as employees_indications, named_struct ('employee_info', collect_set(tt.emp_info), 'employee_mod_info', collect_set(tt.emp_mod_info), 'employee_comments', collect_set(tt.emp_comment) ) as emp_mod_details, from ( select views_ctr.employee, if ( views_ctr.employee_details.so is not null, views_ctr.employee_details, null ) employee_detail, if ( views_ctr.employee_info.so is not null, views_ctr.employee_info, null ) emp_info, if ( views_ctr.employee_comments.so is not null, views_ctr.employee_comments, null ) emp_comment, if ( views_ctr.employee_mod_info.so is not null, views_ctr.employee_mod_info, null ) emp_mod_info, if ( views_ctr.emp_indications.so is not null, views_ctr.emp_indications, null ) employees_indication, from ( select * from views_sta where emp_partition=0 and employee is not null ) views_ctr ) tt group by employee distribute by employee First, What I'm trying is to write it in spark.sql as follow: sparkSession.sql("select reflect('java.util.UUID', 'randomUUID') as id, tt.employee, cast( from_unixtime(unix_timestamp (date_format(current_date(),'dd/MM/yyyy HH:mm:ss'), 'dd/MM/yyyy HH:mm:ss')) as timestamp) as insert_date, collect_set(tt.employee_detail) as employee_details, collect_set( tt.emp_indication ) as employees_indications, named_struct ('employee_info', collect_set(tt.emp_info), 'employee_mod_info', collect_set(tt.emp_mod_info), 'employee_comments', collect_set(tt.emp_comment) ) as emp_mod_details, from ( select views_ctr.employee, if ( views_ctr.employee_details.so is not null, views_ctr.employee_details, null ) employee_detail, if ( views_ctr.employee_info.so is not null, views_ctr.employee_info, null ) emp_info, if ( views_ctr.employee_comments.so is not null, views_ctr.employee_comments, null ) emp_comment, if ( views_ctr.employee_mod_info.so is not null, views_ctr.employee_mod_info, null ) emp_mod_info, if ( views_ctr.emp_indications.so is not null, views_ctr.emp_indications, null ) employees_indication, from ( select * from views_sta where emp_partition=0 and employee is not null ) views_ctr ) tt group by employee distribute by employee") But I got the following exception: Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failute: Task not serializable: java.io.NotSerializableException: org.apache.spark.unsafe.types.UTF8String$IntWrapper -object not serializable (class : org.apache.spark.unsafe.types.UTF8String$IntWrapper, value: org.apache.spark.unsafe.types.UTF8String$IntWrapper#30cfd641) If I'm trying to run my query without collect_set function its work, It's can fail because struct column types in my table? How can I write my HQL query in Spark / fix my exception?