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R","email":"mail@jannisr.de"},"license":"(Apache-2.0 AND Prosperity-3.0.0)","homepage":"https://github.com/public-transport/gtfs-via-postgres/tree/4.12.0","keywords":["gtfs","public transport","transit","convert","postgres","postgresql","sql","graphql"],"repository":{"type":"git","url":"git+https://github.com/public-transport/gtfs-via-postgres.git"},"description":"Process GTFS using PostgreSQL.","contributors":[{"name":"Daniel Langbein","email":"daniel@systemli.org"},{"name":"Magnus Burton","email":"magnus.burton@me.com"},{"name":"smohiudd","email":"saadiq@developmentseed.org"},{"name":"@mattkinnia"}],"maintainers":[{"name":"derhuerst","email":"mail@jannisr.de"}],"readme":"# gtfs-via-postgres\n\n**Import [GTFS Static/Schedule](https://gtfs.org/documentation/schedule/reference/) datasets into a [PostgreSQL database](https://www.postgresql.org)**, to allow for efficient querying and analysis.\n\n[![npm version](https://img.shields.io/npm/v/gtfs-via-postgres.svg)](https://www.npmjs.com/package/gtfs-via-postgres)\n[![binary build status](https://img.shields.io/github/actions/workflow/status/public-transport/gtfs-via-postgres/publish.yml?label=binary%20build)](https://github.com/public-transport/gtfs-via-postgres/actions)\n[![Prosperity/Apache license](https://img.shields.io/static/v1?label=license&message=Prosperity%2FApache&color=0997E8)](#license)\n![minimum Node.js version](https://img.shields.io/node/v/gtfs-via-postgres.svg)\n[![support me via GitHub Sponsors](https://img.shields.io/badge/support%20me-donate-fa7664.svg)](https://github.com/sponsors/derhuerst)\n[![chat with me via Matrix](https://img.shields.io/badge/chat%20with%20me-via%20Matrix-000000.svg)](https://matrix.to/#/@derhuerst:matrix.org)\n\n- ✅ handles [daylight saving time correctly](#correctness-vs-speed-regarding-gtfs-time-values) but retains reasonable lookup performance\n- ✅ supports `frequencies.txt`\n- ✨ joins `stop_times.txt`/`frequencies.txt`, `calendar.txt`/`calendar_dates.txt`, `trips.txt`, `route.txt` & `stops.txt` into [views](https://www.postgresql.org/docs/14/sql-createview.html) for straightforward data analysis (see below)\n- 🚀 is carefully optimised to let PostgreSQL's query planner do its magic, yielding quick lookups even with large datasets (see [performance section](#performance))\n- ✅ validates and imports `translations.txt`\n- ✨ exposes (almost) all data via GraphQL using [PostGraphile](https://www.graphile.org/postgraphile/introduction/), and as a RESTful API using [PostgREST](https://postgrest.org/)\n\nTo work with the time-related data (`stop_times` etc.), `gtfs-via-postgres` supports two \"mental models\":\n\n- the time-*unexpanded* data that is almost directly taken from the GTFS Schedule data – This is useful if you want to do network analysis.\n- the time-*expanded* view that \"applies\" every trip's `stop_times` rows to all of its service days – This is useful for routing & queries from the traveller's perspective.\n\n> [!NOTE]\n> If you don't have a good reason to use PostgreSQL (e.g. because you use one of `gtfs-via-postgres`' unique features like [GraphQL support](#graphql-support)), I recommend you to use [`gtfs-via-duckdb`](https://github.com/public-transport/gtfs-via-duckdb). It is a fork of and the spiritual successor to `gtfs-via-postgres`. Refer to the [comparison below](#gtfs-via-duckdb) for details.\n\n\n## Installation\n\n```shell\nnpm install -g gtfs-via-postgres\n```\n\nOr use [`npx`](https://npmjs.com/package/npx). ✨\n\nThere are also [prebuilt binaries](https://github.com/public-transport/gtfs-via-postgres/releases/latest) and [Docker images](https://github.com/public-transport/gtfs-via-postgres/pkgs/container/gtfs-via-postgres) available.\n\n*Note:* `gtfs-via-postgres` **needs PostgreSQL >=14** to work, as it uses the [`WITH … AS NOT MATERIALIZED`](https://www.postgresql.org/docs/14/queries-with.html#id-1.5.6.12.7) syntax. You can check your PostgreSQL server's version with `psql -t -c 'SELECT version()'`.\n\n\n## Getting Started\n\nIf you have a `.zip` GTFS feed, unzip it into individual files.\n\nWe're going to use the [2022-07-01 *VBB* feed](https://vbb-gtfs.jannisr.de/2022-07-01/) as an example, which consists of individual files already.\n\n```sh\nwget --compression auto \\\n    -r --no-parent --no-directories -R .csv.gz \\\n    -P gtfs -N 'https://vbb-gtfs.jannisr.de/2022-07-01/'\n# …\n# Downloaded 14 files in 20s.\nls -lh gtfs\n# 3.3K agency.csv\n#  97K calendar.csv\n# 1.1M calendar_dates.csv\n# 2.5K datapackage.json\n#  64B frequencies.csv\n# 5.9K levels.csv\n# 246B license\n# 8.3M pathways.csv\n#  49K routes.csv\n# 146M shapes.csv\n# 368M stop_times.csv\n# 5.0M stops.csv\n# 4.7M transfers.csv\n#  16M trips.csv\n```\n\nDepending on your specific setup, configure access to the PostgreSQL database via [`PG*` environment variables](https://www.postgresql.org/docs/14/libpq-envars.html):\n\n```sh\nexport PGUSER=postgres\nexport PGPASSWORD=password\nenv PGDATABASE=postgres psql -c 'create database vbb_2022_02_25'\nexport PGDATABASE=vbb_2022_02_25\n```\n\n*Note*: `gtfs-via-postgres` generates SQL that contains the `CREATE EXTENSION postgis` instruction. For this to work, the PostgreSQL user you're connecting as needs the `CREATE` [permission](https://www.postgresql.org/docs/14/ddl-priv.html) on the database. Also, the `postgis` extension must either be marked as trusted (by putting `trusted = true` into `$(pg_config --sharedir)/extension/postgis.control`), or your user must be a superuser.\n\nInstall `gtfs-via-postgres` and use it to import the GTFS data:\n\n```sh\nnpm install -D gtfs-via-postgres\nnpm exec -- gtfs-to-sql --require-dependencies -- gtfs/*.csv | sponge | psql -b\n# agency\n# calendar\n# CREATE EXTENSION\n# BEGIN\n# CREATE TABLE\n# COPY 37\n# …\n# CREATE INDEX\n# CREATE VIEW\n# COMMIT\n```\n\nImporting will take 10s to 15m, depending on the size of the feed. On an [M2 MacBook Air](https://support.apple.com/en-us/111867), importing the above feed takes about 9m; Importing the [260kb 2021-10-06 Amtrak feed](https://transitfeeds.com/p/amtrak/1136/20211006) takes 6s.\n\nThe [DELFI](https://www.delfi.de/) feed with ca. 330 MB may take 1 hour or more and require at least 35 GB of disk space for the database.\n\nIn addition to a table for each GTFS file, `gtfs-via-postgres` adds these views to help with real-world analysis:\n\n- `service_days` ([materialized](https://www.postgresql.org/docs/14/sql-creatematerializedview.html)) \"applies\" [`calendar_dates`](https://gtfs.org/documentation/schedule/reference/#calendar_datestxt) to [`calendar`](https://gtfs.org/documentation/schedule/reference/#calendartxt) to give you all days of operation for each \"service\" defined in [`calendar`](https://gtfs.org/documentation/schedule/reference/#calendartxt).\n- `arrivals_departures` \"applies\" [`stop_times`](https://gtfs.org/documentation/schedule/reference/#stop_timestxt)/[`frequencies`](https://gtfs.org/documentation/schedule/reference/#frequenciestxt) to [`trips`](https://gtfs.org/documentation/schedule/reference/#tripstxt) and `service_days` to give you all arrivals/departures at each stop with their *absolute* dates & times. It also resolves each stop's parent station ID & name.\n- `connections` \"applies\" [`stop_times`](https://gtfs.org/documentation/schedule/reference/#stop_timestxt)/[`frequencies`](https://gtfs.org/documentation/schedule/reference/#frequenciestxt) to [`trips`](https://gtfs.org/documentation/schedule/reference/#tripstxt) and `service_days`, just like `arrivals_departures`, but gives you departure (at stop A) & arrival (at stop B) *pairs*.\n- `shapes_aggregated` aggregates individual shape points in [`shapes`](https://gtfs.org/documentation/schedule/reference/#shapestxt) into a [PostGIS `LineString`](http://postgis.net/workshops/postgis-intro/geometries.html#linestrings).\n- `stats_by_route_date` provides the number of arrivals/departures by route ID and date. – [read more](docs/analysis/feed-by-route-date.md)\n- `stats_by_agency_route_stop_hour` provides the number of arrivals/departures by agency ID, route ID, stop ID & hour. – [read more](docs/analysis/feed-by-agency-route-stop-and-hour.md)\n- In contrast to `stats_by_route_date` & `stats_by_agency_route_stop_hour`, `stats_active_trips_by_hour` provides the number of *currently running* trips for each hour in the feeds period of time.\n\nAs an example, we're going to use the `arrivals_departures` view to query all *absolute* departures at `de:11000:900120003` (*S Ostkreuz Bhf (Berlin)*) between `2022-03-23T12:30+01` and  `2022-03-23T12:35+01`:\n\n```sql\nSELECT *\nFROM arrivals_departures\nWHERE station_id = 'de:11000:900120003'\nAND t_departure >= '2022-03-23T12:30+01' AND t_departure <= '2022-03-23T12:35+01'\n```\n\n`route_id` | `route_short_name` | `route_type` | `trip_id` | `date` | `stop_sequence` | `t_arrival` | `t_departure` | `stop_id` | `stop_name` | `station_id` | `station_name`\n-|-|-|-|-|-|-|-|-|-|-|-\n`10148_109` | `S3` | `109` | `169035756` | `2022-03-23 00:00:00` | `19` | `2022-03-23 12:31:24+01` | `2022-03-23 12:32:12+01` | `de:11000:900120003:2:53` | `S Ostkreuz Bhf (Berlin)` | `de:11000:900120003` | `S Ostkreuz Bhf (Berlin)`\n`10148_109` | `S3` | `109` | `169035899` | `2022-03-23 00:00:00` | `10` | `2022-03-23 12:33:06+01` | `2022-03-23 12:33:54+01` | `de:11000:900120003:3:55` | `S Ostkreuz Bhf (Berlin)` | `de:11000:900120003` | `S Ostkreuz Bhf (Berlin)`\n`10162_109` | `S7` | `109` | `169128381` | `2022-03-23 00:00:00` | `19` | `2022-03-23 12:33:54+01` | `2022-03-23 12:34:42+01` | `de:11000:900120003:2:53` | `S Ostkreuz Bhf (Berlin)` | `de:11000:900120003` | `S Ostkreuz Bhf (Berlin)`\n`10162_109` | `S7` | `109` | `169128495` | `2022-03-23 00:00:00` | `9` | `2022-03-23 12:30:36+01` | `2022-03-23 12:31:24+01` | `de:11000:900120003:3:55` | `S Ostkreuz Bhf (Berlin)` | `de:11000:900120003` | `S Ostkreuz Bhf (Berlin)`\n`10223_109` | `S41` | `109` | `169054370` | `2022-03-23 00:00:00` | `21` | `2022-03-23 12:30:24+01` | `2022-03-23 12:31:12+01` | `de:11000:900120003:5:58` | `S Ostkreuz Bhf (Berlin)` | `de:11000:900120003` | `S Ostkreuz Bhf (Berlin)`\n`10227_109` | `S42` | `109` | `169071882` | `2022-03-23 00:00:00` | `6` | `2022-03-23 12:30:30+01` | `2022-03-23 12:31:12+01` | `de:11000:900120003:5:59` | `S Ostkreuz Bhf (Berlin)` | `de:11000:900120003` | `S Ostkreuz Bhf (Berlin)`\n`19040_100` | `RB14` | `100` | `178748721` | `2022-03-23 00:00:00` | `13` | `2022-03-23 12:30:00+01` | `2022-03-23 12:30:00+01` | `de:11000:900120003:1:50` | `S Ostkreuz Bhf (Berlin)` | `de:11000:900120003` | `S Ostkreuz Bhf (Berlin)`\n`22664_2` | `FEX` | `2` | `178748125` | `2022-03-23 00:00:00` | `1` | `2022-03-23 12:32:00+01` | `2022-03-23 12:34:00+01` | `de:11000:900120003:4:57` | `S Ostkreuz Bhf (Berlin)` | `de:11000:900120003` | `S Ostkreuz Bhf (Berlin)`\n\n### translations\n\nThere are some `…_translated` views (e.g. `stops_translated`, `arrivals_departures_translated`) that\n- join their respective source table with `translations`, so that each (translatable) field is translated in every provided language,\n- add a `…_lang` column for each translated column (e.g. `stop_name_lang` for `stop_name`) that indicates the language of the translation.\n\nAssuming a dataset with `translations.csv`, let's query all stops with a `de-CE` translation, falling back to the untranslated values:\n\n```sql\nSELECT\n    stop_id,\n    stop_name, stop_name_lang,\n    stop_url,\nFROM stops_translated\nWHERE (stop_name_lang = 'de-CH' OR stop_name_lang IS NULL)\nAND (stop_url_lang = 'de-CH' OR stop_url_lang IS NULL)\n```\n\n\n## Usage\n\n```\nUsage:\n    gtfs-to-sql [options] [--] <gtfs-file> ...\nOptions:\n    --silent                  -s  Don't show files being converted.\n    --require-dependencies    -d  Require files that the specified GTFS files depend\n                                  on to be specified as well (e.g. stop_times.txt\n                                  requires trips.txt). Default: false\n    --ignore-unsupported      -u  Ignore unsupported files. Default: false\n    --route-types-scheme          Set of route_type values to support.\n                                    - basic: core route types in the GTFS spec\n                                    - google-extended: Extended GTFS Route Types [1]\n                                    - tpeg-pti: proposed TPEG-PTI-based route types [2]\n                                    May also be a set of these schemes, separated by `,`.\n                                    Default: google-extended\n    --trips-without-shape-id      Don't require trips.txt items to have a shape_id.\n                                    Default if shapes.txt has not been provided.\n    --routes-without-agency-id    Don't require routes.txt items to have an agency_id.\n    --stops-without-level-id      Don't require stops.txt items to have a level_id.\n                                    Default if levels.txt has not been provided.\n    --stops-location-index        Create a spatial index on stops.stop_loc for efficient\n                                    queries by geolocation.\n    --lower-case-lang-codes       Accept Language Codes (e.g. in feed_info.feed_lang)\n                                    with a different casing than the official BCP-47\n                                    language tags (as specified by the GTFS spec),\n                                    by lower-casing all of them before validating.\n                                    http://www.rfc-editor.org/rfc/bcp/bcp47.txt\n                                    http://www.w3.org/International/articles/language-tags/\n    --stats-by-route-date         Wether to generate a stats_by_route_date view\n                                    letting you analyze all data per routes and/or date:\n                                    - none: Don't generate a view.\n                                    - view: Fast generation, slow access.\n                                    - materialized-view: Slow generation, fast access.\n                                    Default: none\n    --stats-by-agency-route-stop-hour\n                                  Generate a view letting you analyze arrivals/\n                                    departures per route, stop and hour.\n                                    The flag works like --stats-by-route-date.\n    --stats-active-trips-by-hour  Generate a view letting you analyze the number of\n                                    currently running trips over time, by hour.\n                                    Like --stats-by-route-date, this flag accepts\n                                    none, view & materialized-view.\n    --schema                      The schema to use for the database. Default: public\n                                    Even when importing into a schema other than `public`,\n                                    a function `public.gtfs_via_postgres_import_version()`\n                                    gets created, to ensure that multiple imports into the\n                                    same database are all made using the same version. See\n                                    also multiple-datasets.md in the docs.\n    --no-transaction              *Do not* wrap all import SQL commands in a `BEGIN`/`COMMIT`.\n    --postgraphile                Tweak generated SQL for PostGraphile usage.\n                                    https://www.graphile.org/postgraphile/\n    --postgraphile-password       Password for the PostGraphile PostgreSQL user.\n                                    Default: $POSTGRAPHILE_PGPASSWORD, fallback random.\n    --postgrest                   Tweak generated SQL for PostgREST usage.\n                                    Please combine it with --schema.\n                                    https://postgrest.org/\n    --postgrest-password          Password for the PostgREST PostgreSQL user `web_anon`.\n                                    Default: $POSTGREST_PGPASSWORD, fallback random.\n    --postgrest-query-cost-limit  Define a cost limit [1] for queries executed by PostgREST\n                                    on behalf of a user. It is only enforced if\n                                    pg_plan_filter [2] is installed in the database!\n                                    Must be a positive float. Default: none\n                                    [1] https://www.postgresql.org/docs/14/using-explain.html\n                                    [2] https://github.com/pgexperts/pg_plan_filter\n    --import-metadata             Create functions returning import metadata:\n                                    - gtfs_data_imported_at (timestamp with time zone)\n                                    - gtfs_via_postgres_version (text)\n                                    - gtfs_via_postgres_options (jsonb)\n                                    - gtfs_feed_digest (text)\nExamples:\n    gtfs-to-sql some-gtfs/*.txt | sponge | psql -b # import into PostgreSQL\n    gtfs-to-sql -u -- some-gtfs/*.txt | gzip >gtfs.sql.gz # generate a gzipped SQL dump\n\n[1] https://developers.google.com/transit/gtfs/reference/extended-route-types\n[2] https://groups.google.com/g/gtfs-changes/c/keT5rTPS7Y0/m/71uMz2l6ke0J\n```\n\nSome notable limitations mentioned in the [PostgreSQL 14 documentation on date/time types](https://www.postgresql.org/docs/14/datatype-datetime.html):\n\n> For `timestamp with time zone`, the internally stored value is always in UTC (Universal Coordinated Time, traditionally known as Greenwich Mean Time, GMT). An input value that has an explicit time zone specified is converted to UTC using the appropriate offset for that time zone.\n\n> When a `timestamp with time zone` value is output, it is always converted from UTC to the current `timezone` zone, and displayed as local time in that zone. To see the time in another time zone, either change `timezone` or use the `AT TIME ZONE` construct […].\n\nYou can run queries with date+time values in any timezone (offset) and they will be processed correctly, but the output will always be in the database timezone (offset), unless you have explicitly used `AT TIME ZONE`.\n\n### With Docker\n\n*Note:* Just like the `npm`-installed variant, the Docker integration too assumes that your GTFS dataset consists of individual files (i.e. unzipped).\n\nInstead of installing via `npm`, you can use [the `ghcr.io/public-transport/gtfs-via-postgres` Docker image](https://github.com/public-transport/gtfs-via-postgres/pkgs/container/gtfs-via-postgres):\n\n```shell\n# variant A: use Docker image just to convert GTFS to SQL\ndocker run --rm --volume /path/to/gtfs:/gtfs \\\n\tghcr.io/public-transport/gtfs-via-postgres --require-dependencies -- '/gtfs/*.csv' \\\n    | sponge | psql -b\n```\n\n*Note:* Remember to pass the `/gtfs/*.csv` glob as a string (with `'`), so that it gets evaluated *inside* the Docker container.\n\nWith the code above, the `psql -b` process will run *outside* of the Docker container, so your host machine needs access to PostgreSQL.\n\nIf you want to directly import the GTFS data *from within the Docker container*, you need add `psql` to the image and run it from inside. To do that, write a new Dockerfile that extends the `ghcr.io/public-transport/gtfs-via-postgres` image:\n\n```Dockerfile\nFROM ghcr.io/public-transport/gtfs-via-postgres\nENV PGPORT=5432 PGUSER=postgres\nWORKDIR /gtfs\n# pass all arguments into gtfs-via-postgres, pipe output into psql:\nENTRYPOINT [\"/bin/sh\", \"-c\", \"gtfs-via-postgres $0 $@ | sponge | psql -b\"]\n```\n\n```shell\n# start PostgreSQL DB in another container \"db\"\ndocker run --name db -p 5432:5432 -e POSTGRES_PASSWORD=password postgis/postgis\n\n# variant B: use Docker image to convert GTFS to SQL and import it directly\ndocker build -t import-gtfs . # build helper Docker image from Dockerfile\ndocker run --rm --volume /path/to/gtfs:/gtfs \\\n\t--link db -e PGHOST=db -e PGPASSWORD=password \\\n\timport-gtfs --require-dependencies -- '/gtfs/*.csv'\n```\n\n### Importing a GTFS Schedule feed continuously\n\n[postgis-gtfs-importer](https://github.com/mobidata-bw/postgis-gtfs-importer) imports [GTFS Schedule](https://gtfs.org/schedule/) data into a [PostGIS](https://postgis.net) database using `gtfs-via-postgres`. It allows running a production service (e.g. an API) on top of programmatically re-imported data from a periodically changing GTFS feed without downtime.\n\nBecause it works as [atomically](https://en.wikipedia.org/wiki/Atomicity_(database_systems)) as possible with PostgreSQL, it makes the import pipeline *robust*, even if an import fails or if simultaneous imports get started.\n\n### Exporting data efficiently\n\nIf you want to export data from the database, use the [`COPY` command](https://www.postgresql.org/docs/14/sql-copy.html); On an [M1 MacBook Air](https://en.wikipedia.org/wiki/MacBook_Air_(Apple_silicon)#Third_generation_(Retina_with_Apple_silicon)), PostgreSQL 14 can export about 500k `connections` rows per second.\n\n```shell\npsql -c 'COPY (SELECT * FROM connections) TO STDOUT csv HEADER' >connections.csv\n```\n\nIn the nested `SELECT` query, you can use features like `WHERE`, `ORDER BY` and `LIMIT`. Because `psql` passes on the exported data right away, you could stream it into another process.\n\n### Querying stops by location efficiently\n\nIf you want to find stops by (geo)location, run `gtfs-via-postgres` with `--stops-location-index`. This will create a [spatial index](https://postgis.net/workshops/postgis-intro/indexing.html) on `stops.stop_loc`, so that most [PostGIS functions & operators](https://postgis.net/docs/manual-3.2/reference.html#Measurement_Functions) make use of it.\n\n### GraphQL support\n\nThe `--postgraphile` flag changes the SQL generated by `gtfs-via-postgres` slightly, so that you get a reasonably idiomatic GraphQL API out-of-the-box when running [PostGraphile](https://www.graphile.org/postgraphile/) v4 on it:\n\n```shell\n# import data into PostgreSQL with PostGraphile tweaks\nnpm exec -- gtfs-to-sql -d --postgraphile -- gtfs/*.csv | sponge | psql -b\n```\n\nIn line with the intended PostGraphile usage, `gtfs-via-postgres` will create a PostgreSQL role/user `postgraphile` with read-only access to the DB. You can set the `postgraphile`'s password with the `--postgraphile-password` option, or using the `$POSTGRAPHILE_PGPASSWORD` environment variable; By default, it will use (and log) a random password.\n\n`gtfs-via-postgres` *doesn't* specify PostGraphile as a regular dependency, but as `peerDependencies`, in order to stay lightweight for users who don't need the GraphQL interface. Some versions of some package managers install unmet peer dependencies, some don't. Let's make sure that PostGraphile (and its plugins) are installed:\n\n```shell\nnpm install \\\n    postgraphile@^4.12 \\\n    @graphile-contrib/pg-simplify-inflector@^6.1 \\\n    @graphile/postgis@^0.2.0-0\n```\n\nThe `serve-gtfs-via-graphql` helper script configures and runs PostGraphile. With `NODE_ENV=development`, it will\n\n- serve a fully configured [GraphiQL UI](https://graphql-dotnet.github.io/docs/getting-started/graphiql/) at `/graphiql`\n- provide more errors on database & query errors\n- allow [using PostgreSQL's `EXPLAIN` via GraphQL](https://www.graphile.org/postgraphile/debugging/#via-postgraphiql-explain)\n\n```\n# listens on port 3000, this can be changed using $PORT\nenv NODE_ENV=development npm exec -- serve-gtfs-via-graphql\n```\n\n**As an example for the GraphQL API, check out the [test query](test/sample-gtfs-feed-postgraphile-test.graphql)** or open the [GraphiQL UI](https://github.com/graphql/graphiql) served at [`localhost:3000/graphiql`](http://localhost:3000/graphiql).\n\n### REST API support\n\nWith the `--postgrest` flag, `gtfs-via-postgres` will augment the schema with a `web_anon` role and some comments, so that when running [PostgREST](https://postgrest.org/) on the database, you will get a powerful REST API.\n\n[read more](docs/postgrest.md)\n\n### more guides\n\nThe [`docs` directory](docs) contains more instructions on how to use `gtfs-via-postgres`.\n\n\n## Correctness vs. Speed regarding GTFS Time Values\n\nWhen matching time values from `stop_times` against dates from `calendar`/`calendar_dates`, you have to take into account that **GTFS Time values can be >24h and [are not relative to the beginning of the day but relative to noon - 12h](https://gist.github.com/derhuerst/574edc94981a21ef0ce90713f1cff7f6)**. ([There are a few libraries that don't do this.](https://github.com/r-transit/tidytransit/issues/175#issuecomment-979213277))\n\nThis means that, in order to determine all *absolute* points in time where a particular trip departs at a particular stop, you *cannot* just loop over all \"service dates\" and add the time value (as in `beginning_of_date + departure_time`); Instead, for each date, you have to determine noon, subtract 12h and then apply the time, which might extend arbitrarily far into the following days.\n\nLet's consider two examples:\n\n- A `departure_time` of `26:59:00` with a trip running on `2021-03-01`: The time, applied to this specific date, \"extends\" into the following day, so it actually departs at `2021-03-02T02:59+01`.\n- A departure time of `03:01:00` with a trip running on `2021-03-28`: This is when the standard -> DST switch happens in the `Europe/Berlin` timezone. Because the dep. time refers to noon - 12h (*not* to midnight), it actually happens at `2021-03-28T03:01+02` which is *not* `3h1m` after `2021-03-28T00:00+01`.\n\n`gtfs-via-postgres` always prioritizes correctness over speed. Because it follows the GTFS semantics, when filtering `arrivals_departures` by *absolute* departure date+time, it cannot automatically filter `service_days` (which is `calendar` and `calendar_dates` combined), because **even a date *before* the date of the desired departure time frame might still end up *within*, when combined with a `departure_time` of e.g. `27:30:00`**; Instead, it has to consider all `service_days` and apply the `departure_time` to all of them to check if they're within the range.\n\nHowever, if you determine your feed's largest `arrival_time`/`departure_time`, you can filter on `date` when querying `arrivals_departures`; This allows PostgreSQL to reduce the number of joins and calendar calculations by orders of magnitude, speeding up your queries significantly. `gtfs-via-postgres` provides two low-level helper functions `largest_arrival_time()` & `largest_departure_time()` for this, as well as two high-level helper functions `dates_filter_min(t_min)` & `dates_filter_max(t_max)` (see below).\n\nFor example, when querying all *absolute* departures at `de:11000:900120003` (*S Ostkreuz Bhf (Berlin)*) between `2022-03-23T12:30+01` and  `2022-03-23T12:35+01` within the [2022-02-25 *VBB* feed](https://vbb-gtfs.jannisr.de/2022-02-25/), filtering by `date` speeds it up nicely (Apple M1, PostgreSQL 14.2):\n\n`station_id` filter | `date` filter | query time | nr of results\n-|-|-|-\n`de:11000:900120003` | *none* | 230ms | ~574k\n`de:11000:900120003` | `2022-03-13` >= `date` < `2022-04-08` | 105ms | ~51k\n`de:11000:900120003` | `2022-03-23` >= `date` < `2022-03-24` | 55ms | ~2k\n`de:11000:900120003` | `2022-03-22` > `date` < `2022-03-24` | 55ms | ~2k\n*none* | *none* | 192s | 370m\n*none* | `2022-03-13` >= `date` < `2022-04-08` | 34s | ~35m\n*none* | `2022-03-22` > `date` < `2022-03-24` | 2.4s | ~1523k\n\nUsing `dates_filter_min(t_min)` & `dates_filter_max(t_max)`, we can easily filter by `date`. When filtering by `t_departure` (absolute departure date+time), `t_min` is the lower `t_departure` bound, whereas `t_max` is the upper bound. The VBB example above can be queried like this:\n\n```sql\nSELECT *\nFROM arrivals_departures\n-- filter by absolute departure date+time\nWHERE t_departure >= '2022-03-23T12:30+01' AND t_departure <= '2022-03-23T12:35+01'\n-- allow \"cutoffs\" by filtering by date\nAND \"date\" >= dates_filter_min('2022-03-23T12:30+01') -- evaluates to 2023-03-22\nAND \"date\" <= dates_filter_max('2022-03-23T12:35+01') -- evaluates to 2023-03-23\n```\n\n\n## Performance\n\nWith all use cases I could think of, `gtfs-via-postgres` is reasonably fast. If there's a particular kind of query that you think should be faster, please [open an Issue](https://github.com/public-transport/gtfs-via-postgres/issues/new)!\n\nThe following benchmarks were run with the [2022-07-01 VBB GTFS dataset](https://vbb-gtfs.jannisr.de/2022-07-01/) (41k `stops`, 6m `stop_times`, 207m arrivals/departures) using `gtfs-via-postgres@4.11.9` and PostgreSQL 18.1 on an [M2 MacBook Air](https://support.apple.com/en-us/111867) running macOS 14.8; All measurements are in milliseconds.\n\n| query | avg | min | p25 | p50 | p75 | p95 | p99 | max | iterations |\n| - | - | - | - | - | - | - | - | - | - |\n| <pre>SELECT *<br>FROM stops<br>ORDER BY ST_Distance(stop_loc::geometry, ST_SetSRID(ST_MakePoint(9.7, 50.547), 4326)) ASC<br>LIMIT 100</pre> | 15 | 14.506 | 15 | 15 | 15 | 15 | 15 | 14.658 | 170 |\n| <pre>SELECT *<br>FROM arrivals_departures<br>WHERE route_short_name = 'S1'<br>AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02'<br>AND date >= dates_filter_min('2022-08-09T07:10+02')<br>AND date <= dates_filter_max('2022-08-09T07:30+02')</pre> | 22 | 21.77 | 22 | 22 | 22 | 23 | 24 | 24.999 | 90 |\n| <pre>SELECT *<br>FROM arrivals_departures<br>WHERE station_id = 'de:11000:900100001' -- S+U Friedrichstr. (Berlin)<br>AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02'<br>AND date >= dates_filter_min('2022-08-09T07:10+02')<br>AND date <= dates_filter_max('2022-08-09T07:30+02')</pre> | 19 | 18.939 | 19 | 19 | 19 | 20 | 21 | 21.706 | 170 |\n| <pre>SELECT *<br>FROM arrivals_departures<br>WHERE station_id = 'de:11000:900100001' -- S+U Friedrichstr. (Berlin)<br>AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02'<br>AND date >= dates_filter_min('2022-08-09T07:10+02')<br>AND date <= dates_filter_max('2022-08-09T07:30+02')<br>AND stop_sequence = 0</pre> | 5 | 5.375 | 5 | 5 | 5 | 6 | 6 | 5.684 | 500 |\n| <pre>SELECT *<br>FROM arrivals_departures<br>WHERE stop_id = 'de:11000:900100001::4' -- S+U Friedrichstr. (Berlin)<br>AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02'<br>AND date >= dates_filter_min('2022-08-09T07:10+02')<br>AND date <= dates_filter_max('2022-08-09T07:30+02')</pre> | 8 | 8.163 | 8 | 8 | 8 | 8 | 8 | 9.107 | 400 |\n| <pre>SELECT *<br>FROM arrivals_departures<br>WHERE trip_id = '168977951'<br>AND date > '2022-08-08' AND date <= '2022-08-09'</pre> | 2 | 2.085 | 2 | 2 | 2 | 2 | 2 | 2.277 | 500 |\n| <pre>SELECT count(*)<br>FROM arrivals_departures<br>WHERE stop_id = 'de:11000:900100001::4' -- S+U Friedrichstr. (Berlin)</pre> | 67 | 66.065 | 67 | 67 | 67 | 67 | 69 | 68.927 | 50 |\n| <pre>SELECT count(*)<br>FROM arrivals_departures<br>WHERE stop_id = 'definitely-non-existent'</pre> | 5 | 5.379 | 5 | 5 | 6 | 6 | 6 | 6.351 | 500 |\n| <pre>SELECT *<br>FROM arrivals_departures<br>WHERE t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02'<br>AND date >= dates_filter_min('2022-08-09T07:10+02'::timestamp with time zone)<br>AND date <= dates_filter_max('2022-08-09T07:30+02'::timestamp with time zone)</pre> | 2734 | 2698.415 | 2704 | 2711 | 2738 | 2804 | 2817 | 2819.877 | 5 |\n| <pre>SELECT *<br>FROM arrivals_departures<br>WHERE t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02'<br>AND date >= '2022-08-08'<br>AND date <= '2022-08-09'</pre> | 2153 | 1781.159 | 1886 | 2156 | 2183 | 2642 | 2734 | 2756.867 | 5 |\n| <pre>SELECT *<br>FROM connections<br>WHERE route_short_name = 'S1'<br>AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02'<br>AND date >= dates_filter_min('2022-08-09T07:10+02')<br>AND date <= dates_filter_max('2022-08-09T07:30+02')</pre> | 84 | 82.654 | 83 | 84 | 84 | 84 | 84 | 84.33 | 20 |\n| <pre>SELECT *<br>FROM connections<br>WHERE from_station_id = 'de:11000:900100001' -- S+U Friedrichstr. (Berlin)<br>AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02'<br>AND date >= dates_filter_min('2022-08-09T07:10+02')<br>AND date <= dates_filter_max('2022-08-09T07:30+02')</pre> | 58 | 57.796 | 58 | 58 | 58 | 58 | 58 | 58.308 | 50 |\n| <pre>SELECT *<br>FROM connections<br>WHERE from_station_id = 'de:11000:900100001' -- S+U Friedrichstr. (Berlin)<br>AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02'<br>AND date >= dates_filter_min('2022-08-09T07:10+02')<br>AND date <= dates_filter_max('2022-08-09T07:30+02')<br>AND from_stop_sequence = 0</pre> | 10 | 9.851 | 10 | 10 | 10 | 11 | 11 | 12.626 | 300 |\n| <pre>SELECT *<br>FROM connections<br>WHERE from_stop_id = 'de:11000:900100001::4' -- S+U Friedrichstr. (Berlin)<br>AND t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02'<br>AND date >= dates_filter_min('2022-08-09T07:10+02')<br>AND date <= dates_filter_max('2022-08-09T07:30+02')</pre> | 14 | 13.467 | 14 | 14 | 14 | 14 | 14 | 13.714 | 200 |\n| <pre>SELECT *<br>FROM connections<br>WHERE trip_id = '168977951'<br>AND date > '2022-08-08' AND date <= '2022-08-09'</pre> | 4 | 3.577 | 4 | 4 | 4 | 4 | 5 | 6.152 | 500 |\n| <pre>SELECT count(*)<br>FROM connections<br>WHERE from_stop_id = 'de:11000:900100001::4' -- S+U Friedrichstr. (Berlin)</pre> | 87 | 86.362 | 87 | 87 | 87 | 87 | 87 | 87.518 | 40 |\n| <pre>SELECT count(*)<br>FROM connections<br>WHERE from_stop_id = 'definitely-non-existent'</pre> | 8 | 7.237 | 8 | 8 | 8 | 8 | 9 | 13.125 | 500 |\n| <pre>SELECT *<br>FROM connections<br>WHERE t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02'<br>AND date >= dates_filter_min('2022-08-09T07:10+02'::timestamp with time zone)<br>AND date <= dates_filter_max('2022-08-09T07:30+02'::timestamp with time zone)<br>ORDER BY t_departure<br>LIMIT 100</pre> | 15584 | 15517.829 | 15549 | 15580 | 15617 | 15647 | 15653 | 15654.632 | 3 |\n| <pre>SELECT *<br>FROM connections<br>WHERE t_departure >= '2022-08-09T07:10+02' AND t_departure <= '2022-08-09T07:30+02'<br>AND date >= '2022-08-08'<br>AND date <= '2022-08-09'<br>ORDER BY t_departure<br>LIMIT 100</pre> | 6816 | 6559.442 | 6685 | 6811 | 6945 | 7052 | 7074 | 7079.167 | 3 |\n| <pre>SELECT *<br>FROM stats_by_route_date<br>WHERE route_id = '17452_900' -- M4<br>AND date >= '2022-08-08' AND date <= '2022-08-14'<br>AND is_effective = true</pre> | 688 | 677.417 | 678 | 678 | 681 | 715 | 722 | 723.411 | 5 |\n\n\n## Related Projects\n\nThere are some projects that are very similar to `gtfs-via-postgres`:\n\n### gtfs-via-duckdb\n\n[`gtfs-via-duckdb`](https://github.com/public-transport/gtfs-via-duckdb) is a fork of and the spiritual successor to `gtfs-via-postgres`, importing GTFS into a [*DuckDB*](https://duckdb.org/) database file. The two have the same features, shortcomings and conceptual design (runtime access to the GTFS happens using SQL only).\n\n`gtfs-via-duckdb`'s usage in production systems is much less complex: [`duckdb-gtfs-importer`](https://github.com/OpenDataVBB/duckdb-gtfs-importer) can enable the *robustness* and *atomicity* goals [explained in `postgis-gtfs-importer`'s readme](https://github.com/mobidata-bw/postgis-gtfs-importer/blob/v5/README.md) with a much simpler design (>1 DB files, symlink to the latest).\n\nBecause DuckDB tends to be more efficient than PostgreSQL, with the mostly [OLAP](https://en.wikipedia.org/wiki/Online_analytical_processing)-like queries run for GTFS analysis, so `gtfs-via-duckdb` imports datasets faster and queries for common use cases usually run faster.\n\n### Node-GTFS\n\n[Node-GTFS (`gtfs` npm package)](https://github.com/BlinkTagInc/node-gtfs) is widely used. It covers three use cases: importing GTFS into an [SQLite](https://sqlite.org/) DB, exporting GTFS/GeoJSON from it, and generating HTML or charts for humans.\n\nI don't use it though because\n\n- it doesn't handle GTFS Time values correctly ([1](https://github.com/BlinkTagInc/node-gtfs/blob/master/lib/utils.js#L36-L46)/[2](https://github.com/BlinkTagInc/node-gtfs/blob/4d5e5369d5d94052a5004204182a2582ced8f619/lib/import.js#L233), checked on 2022-03-01)\n- it doesn't always work in a streaming/iterative way ([1](https://github.com/BlinkTagInc/node-gtfs/blob/4d5e5369d5d94052a5004204182a2582ced8f619/lib/export.js#L65)/[2](https://github.com/BlinkTagInc/node-gtfs/blob/4d5e5369d5d94052a5004204182a2582ced8f619/lib/geojson-utils.js#L118-L126), checked on 2022-03-01)\n- sometimes does synchronous fs calls ([1](https://github.com/BlinkTagInc/node-gtfs/blob/4d5e5369d5d94052a5004204182a2582ced8f619/lib/import.js#L65)/[2](https://github.com/BlinkTagInc/node-gtfs/blob/4d5e5369d5d94052a5004204182a2582ced8f619/lib/import.js#L298), checked on 2022-03-01)\n\n### gtfs-sequelize\n\n[gtfs-sequelize](https://github.com/evansiroky/gtfs-sequelize) uses [sequelize.js](https://sequelize.org) to import a GTFS feed and query the DB.\n\nI don't use it because\n\n- it doesn't handle GTFS Time values correctly ([1](https://github.com/evansiroky/gtfs-sequelize/blob/ba101fa82e730694c536c43e615ff38fd264a65b/lib/gtfsLoader.js#L616-L617)/[2](https://github.com/evansiroky/gtfs-sequelize/blob/ba101fa82e730694c536c43e615ff38fd264a65b/lib/gtfsLoader.js#L24-L33), cheked on 2022-03-01)\n- it doesn't provide much tooling for analyzing all arrivals/departures (checked on 2022-03-01)\n- some of its operations are quite slow, because they fetch related records of a record via JS instead of using `JOIN`s\n\n### gtfs-sql-importer\n\nThere are several forks of the [original outdated project](https://github.com/cbick/gtfs_SQL_importer); [fitnr's fork](https://github.com/fitnr/gtfs-sql-importer) seems to be the most recent one.\n\nThe project has a slightly different goal than `gtfs-via-postgres`: While `gtfs-sql-importer` is designed to import multiple versions of a GTFS dataset in an idempotent fashion, `gtfs-via-postgres` assumes that *one* (version of a) GTFS dataset is imported into *one* DB exactly once.\n\n`gtfs-via-postgres` aims to provide more tools – e.g. the `arrivals_departures` & `connections` views – to help with the analysis of a GTFS dataset, whereas `gtfs-sql-importer` just imports the data.\n\n### other related projects\n\n- [gtfsdb](https://github.com/OpenTransitTools/gtfsdb) – Python library for converting GTFS files into a relational database.\n- [pygtfs](https://github.com/jarondl/pygtfs) – A python (2/3) library for GTFS (fork of [gtfs-sql](https://github.com/andrewblim/gtfs-sql))\n- [gtfspy](https://github.com/CxAalto/gtfspy) – Public transport network analysis using Python and SQLite.\n- [GTFS Kit](https://github.com/mrcagney/gtfs_kit) – A Python 3.6+ tool kit for analyzing General Transit Feed Specification (GTFS) data.\n- [GtfsToSql](https://github.com/OpenMobilityData/GtfsToSql) – Parses a GTFS feed into an SQL database (Java)\n- [gtfs-to-sqlite](https://github.com/aytee17/gtfs-to-sqlite) – A tool for generating an SQLite database from a GTFS feed. (Java)\n- [gtfs-lib](https://github.com/conveyal/gtfs-lib) – Java library & CLI for importing GTFS files into a PostgreSQL database.\n- [gtfs-schema](https://github.com/tyleragreen/gtfs-schema) – PostgreSQL schemas for GTFS feeds. (plain SQL)\n- [markusvalo/HSLtraffic](https://github.com/markusvalo/HSLtraffic) – Scripts to create a PostgreSQL database for HSL GTFS-data. (plain SQL)\n\n\n## License\n\nThis project is dual-licensed: **My ([@derhuerst](https://github.com/derhuerst)) contributions are licensed under the [*Prosperity Public License*](https://prosperitylicense.com), [contributions of other people](https://github.com/public-transport/gtfs-via-postgres/graphs/contributors) are licensed as [Apache 2.0](https://apache.org/licenses/LICENSE-2.0)**.\n\n> This license allows you to use and share this software for noncommercial purposes for free and to try this software for commercial purposes for thirty days.\n\n> Personal use for research, experiment, and testing for the benefit of public knowledge, personal study, private entertainment, hobby projects, amateur pursuits, or religious observance, without any anticipated commercial application, doesn’t count as use for a commercial purpose.\n\n[Get in touch with me](https://jannisr.de/) to buy a commercial license or read more about [why I sell private licenses for my projects](https://gist.github.com/derhuerst/0ef31ee82b6300d2cafd03d10dd522f7).\n\n\n## Contributing\n\nIf you have a question or need support using `gtfs-via-postgres`, please double-check your code and setup first. If you think you have found a bug or want to propose a feature, use [the issues page](https://github.com/public-transport/gtfs-via-postgres/issues).\n\nBy contributing, you agree to release your modifications under the [Apache 2.0 license](LICENSE-APACHE).\n","readmeFilename":"readme.md"}