[{"data":1,"prerenderedAt":3430},["ShallowReactive",2],{"\u002Fdevelopers\u002Fuser-guide\u002Fconnect\u002Fconnectors-overview-app":3,"navigation-developers":897,"\u002Fdevelopers\u002Fuser-guide\u002Fconnect\u002Fconnectors-overview":2796,"surr-\u002Fdevelopers\u002Fuser-guide\u002Fconnect\u002Fconnectors-overview":3427},{"id":4,"title":5,"author":6,"body":7,"description":889,"extension":890,"meta":891,"navigation":660,"path":892,"seo":893,"sitemap":894,"stem":895,"__hash__":896},"content\u002F2.developers\u002F4.user-guide\u002F20.connect\u002F30.connectors-overview.md","Connectors in Pathway",null,{"type":8,"value":9,"toc":877},"minimark",[10,15,24,33,36,513,516,521,533,537,543,547,554,560,563,582,594,598,604,609,613,620,625,628,764,772,776,783,797,800,806,809,813,816,855,859,862,865,873],[11,12,14],"h1",{"id":13},"connectors-in-pathway-live-data-framework","Connectors in Pathway Live Data Framework",[16,17,18,19,23],"p",{},"In order to use Pathway Live Data Framework, one of the first things you need to do is to access the data you want to manipulate.\nIn Pathway Live Data Framework, accessing the data is done using ",[20,21,22],"strong",{},"connectors",".",[16,25,26,27,32],{},"The Pathway Live Data Framework comes with connectors which connect to external data sources at input, as well as connectors which output the data outside of the framework.\nBefore reading any further, make sure you are already familiar with the ",[28,29,31],"a",{"href":30},"\u002Fdevelopers\u002Fuser-guide\u002Fintroduction\u002Fstreaming-and-static-modes","streaming and static modes"," of Pathway Live Data Framework as the connectors are different depending on the chosen mode.",[16,34,35],{},"Before going into more details about the different connectors and how they work, here is a summary of available connectors, grouped by mode and whether they are input or output connectors:",[37,38,41,42,41,62],"table",{"className":39},[40],"w-full!","\n    ",[43,44,45,46,41],"thead",{},"\n        ",[47,48,49,50,49,53,49,58,45],"tr",{},"\n            ",[51,52],"th",{},[51,54,57],{"className":55},[56],"text-center","Streaming mode",[51,59,61],{"className":60},[56],"Static mode",[63,64,45,65,45,325,41],"tbody",{},[47,66,49,67,74,49,263,45],{},[68,69,73],"td",{"className":70},[56,71,72],"font-bold","align-middle","Input connectors\n            ",[68,75,78,79,78,88,78,95,78,102,78,109,78,116,78,123,78,130,78,137,78,144,78,151,78,158,78,165,78,172,78,179,78,186,78,193,78,200,78,207,78,214,78,221,78,228,78,235,78,242,78,249,78,256,49],{"className":76},[56,77],"!align-middle","\n                ",[80,81,84],"span",{"className":82},[83],"block",[28,85,87],{"href":86},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fairbyte","Airbyte",[80,89,91],{"className":90},[83],[28,92,94],{"href":93},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fs3","Amazon S3",[80,96,98],{"className":97},[83],[28,99,101],{"href":100},"\u002Fdevelopers\u002Fuser-guide\u002Fconnect\u002Fconnectors\u002Fcsv_connectors","CSV",[80,103,105],{"className":104},[83],[28,106,108],{"href":107},"\u002Fdevelopers\u002Fuser-guide\u002Fconnect\u002Fconnectors\u002Fdatabase-connectors","Debezium",[80,110,112],{"className":111},[83],[28,113,115],{"href":114},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fdeltalake","Delta Lake",[80,117,119],{"className":118},[83],[28,120,122],{"href":121},"\u002Fdevelopers\u002Fuser-guide\u002Fconnect\u002Fconnectors\u002Ffs-connector","File System",[80,124,126],{"className":125},[83],[28,127,129],{"href":128},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fgdrive","Google Drive",[80,131,133],{"className":132},[83],[28,134,136],{"href":135},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fhttp","http",[80,138,140],{"className":139},[83],[28,141,143],{"href":142},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Ficeberg","Iceberg",[80,145,147],{"className":146},[83],[28,148,150],{"href":149},"\u002Fdevelopers\u002Fuser-guide\u002Fconnect\u002Fconnectors\u002Fjsonlines-connector","JSON Lines",[80,152,154],{"className":153},[83],[28,155,157],{"href":156},"\u002Fdevelopers\u002Fuser-guide\u002Fconnect\u002Fconnectors\u002Fkafka_connectors","Kafka",[80,159,161],{"className":160},[83],[28,162,164],{"href":163},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fkinesis","Kinesis",[80,166,168],{"className":167},[83],[28,169,171],{"href":170},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fminio","MinIO",[80,173,175],{"className":174},[83],[28,176,178],{"href":177},"\u002Fdevelopers\u002Fuser-guide\u002Fconnect\u002Fconnectors\u002Fmongodb-debezium","MongoDB (with Debezium)",[80,180,182],{"className":181},[83],[28,183,185],{"href":184},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fmongodb","MongoDB (with oplog replication)",[80,187,189],{"className":188},[83],[28,190,192],{"href":191},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fmqtt","MQTT",[80,194,196],{"className":195},[83],[28,197,199],{"href":198},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fmssql","MS SQL Server",[80,201,203],{"className":202},[83],[28,204,206],{"href":205},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fnats","NATS",[80,208,210],{"className":209},[83],[28,211,213],{"href":212},"\u002Fdevelopers\u002Fuser-guide\u002Fconnect\u002Fconnectors\u002Fneondb-connector","NeonDB",[80,215,217],{"className":216},[83],[28,218,220],{"href":219},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fplaintext","Plain text",[80,222,224],{"className":223},[83],[28,225,227],{"href":226},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fpostgres","PostgreSQL (via WAL reading)",[80,229,231],{"className":230},[83],[28,232,234],{"href":233},"\u002Fdevelopers\u002Fuser-guide\u002Fconnect\u002Fconnectors\u002Fcustom-python-connectors","Python",[80,236,238],{"className":237},[83],[28,239,241],{"href":240},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Frabbitmq","RabbitMQ",[80,243,245],{"className":244},[83],[28,246,248],{"href":247},"\u002Fdevelopers\u002Fuser-guide\u002Fconnect\u002Fconnectors\u002Fswitching-to-redpanda","Redpanda",[80,250,252],{"className":251},[83],[28,253,255],{"href":254},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-xpacks-sharepoint#pathway.xpacks.connectors.sharepoint.read","SharePoint",[80,257,259],{"className":258},[83],[28,260,262],{"href":261},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fsqlite","SQLite",[68,264,78,266,78,271,78,276,78,281,78,286,78,291,78,296,78,301,78,308,78,313,78,320,49],{"className":265},[56,77],[80,267,269],{"className":268},[83],[28,270,94],{"href":93},[80,272,274],{"className":273},[83],[28,275,101],{"href":100},[80,277,279],{"className":278},[83],[28,280,115],{"href":114},[80,282,284],{"className":283},[83],[28,285,122],{"href":121},[80,287,289],{"className":288},[83],[28,290,129],{"href":128},[80,292,294],{"className":293},[83],[28,295,150],{"href":149},[80,297,299],{"className":298},[83],[28,300,157],{"href":156},[80,302,304],{"className":303},[83],[28,305,307],{"href":306},"\u002Fdevelopers\u002Fapi-docs\u002Fdebug#pathway.debug.table_from_markdown","Markdown",[80,309,311],{"className":310},[83],[28,312,171],{"href":170},[80,314,316],{"className":315},[83],[28,317,319],{"href":318},"\u002Fdevelopers\u002Fapi-docs\u002Fdebug#pathway.debug.table_from_pandas","Pandas",[80,321,323],{"className":322},[83],[28,324,248],{"href":247},[47,326,49,327,331,49,491,45],{},[68,328,330],{"className":329},[56,71,72],"Output connectors\n            ",[68,332,78,334,78,341,78,348,78,353,78,358,78,365,78,372,78,377,78,384,78,389,78,394,78,399,78,404,78,409,78,416,78,423,78,429,78,434,78,439,78,446,78,451,78,456,78,462,78,469,78,474,78,479,78,486,49],{"className":333},[56,77],[80,335,337],{"className":336},[83],[28,338,340],{"href":339},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fbigquery","BigQuery",[80,342,344],{"className":343},[83],[28,345,347],{"href":346},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fclickhouse","ClickHouse",[80,349,351],{"className":350},[83],[28,352,101],{"href":100},[80,354,356],{"className":355},[83],[28,357,115],{"href":114},[80,359,361],{"className":360},[83],[28,362,364],{"href":363},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fdynamodb","DynamoDB",[80,366,368],{"className":367},[83],[28,369,371],{"href":370},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Felasticsearch","Elastic Search",[80,373,375],{"className":374},[83],[28,376,122],{"href":121},[80,378,380],{"className":379},[83],[28,381,383],{"href":382},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fpubsub","Google PubSub",[80,385,387],{"className":386},[83],[28,388,136],{"href":135},[80,390,392],{"className":391},[83],[28,393,143],{"href":142},[80,395,397],{"className":396},[83],[28,398,150],{"href":149},[80,400,402],{"className":401},[83],[28,403,157],{"href":156},[80,405,407],{"className":406},[83],[28,408,164],{"href":163},[80,410,412],{"className":411},[83],[28,413,415],{"href":414},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Flogstash","Logstash",[80,417,419],{"className":418},[83],[28,420,422],{"href":421},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fmilvus","Milvus",[80,424,426],{"className":425},[83],[28,427,428],{"href":184},"MongoDB",[80,430,432],{"className":431},[83],[28,433,192],{"href":191},[80,435,437],{"className":436},[83],[28,438,199],{"href":198},[80,440,442],{"className":441},[83],[28,443,445],{"href":444},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fmysql","MySQL",[80,447,449],{"className":448},[83],[28,450,206],{"href":205},[80,452,454],{"className":453},[83],[28,455,213],{"href":212},[80,457,459],{"className":458},[83],[28,460,461],{"href":107},"PostgreSQL",[80,463,465],{"className":464},[83],[28,466,468],{"href":467},"\u002Fdevelopers\u002Fapi-docs\u002Fpathway-io\u002Fquestdb","QuestDB",[80,470,472],{"className":471},[83],[28,473,241],{"href":240},[80,475,477],{"className":476},[83],[28,478,248],{"href":247},[80,480,482],{"className":481},[83],[28,483,485],{"href":484},"\u002Fdevelopers\u002Fuser-guide\u002Fconnect\u002Fconnectors\u002Fslack_send_alerts","Slack",[80,487,489],{"className":488},[83],[28,490,262],{"href":261},[68,492,78,494,78,499,78,506,49],{"className":493},[56,77],[80,495,497],{"className":496},[83],[28,498,101],{"href":100},[80,500,502],{"className":501},[83],[28,503,505],{"href":504},"\u002Fdevelopers\u002Fapi-docs\u002Fdebug#pathway.debug.compute_and_print","pw.debug.compute_and_print",[80,507,509],{"className":508},[83],[28,510,512],{"href":511},"\u002Fdevelopers\u002Fapi-docs\u002Fdebug#pathway.debug.compute_and_print_update_stream","pw.debug.compute_and_print_update_stream",[16,514,515],{},"The one you need is not in the table? Don't worry, more are coming and you can always reach out to let us know what you would find helpful!",[517,518,520],"h2",{"id":519},"generation-of-artificial-data-streams","Generation of artificial data streams.",[16,522,523,524,532],{},"Obtaining a real data stream for testing your application can be challenging in specific scenarios.\nFortunately, you can use our ",[28,525,527,531],{"href":526},"\u002Fdevelopers\u002Fuser-guide\u002Fconnect\u002Fartificial-streams",[528,529,530],"code",{},"demo"," module"," to simulate an incoming data stream.\nThis module allows you to create custom data streams from scratch or by utilizing a CSV file to test and experiment with realtime data streams.",[517,534,536],{"id":535},"formatting","Formatting",[16,538,539,540,23],{},"Connectors support different formats, such as CSV or JSON.\nThe supported formats depend on the connectors. However, ",[20,541,542],{},"all the connectors support the binary format",[517,544,546],{"id":545},"streaming-mode-connectors","Streaming mode connectors",[16,548,549,550,553],{},"In the streaming mode, input connectors wait for incoming updates.\nWhenever an update is received, it is propagated in the dataflow (more details ",[28,551,552],{"href":30},"here",") until it reaches the output connectors which output the resulting changes.\nThis is where it becomes interesting: the table created by the input connector, and all the computations based on it, will be automatically updated whenever an update is received (e.g. a new CSV file has been created).\nAll the computations and outputs are automatically updated by Pathway Live Data Framework to take into account the updates from the stream, without requiring any operation from your part: this is the magic of the framework!\nIn practice, the updates are triggered by commits, which ensure the atomicity of each update.",[16,555,556,557],{},"As Pathway Live Data Framework deals with never-ending streaming data: the computation runs forever until the process gets killed.\n",[20,558,559],{},"This is the normal behavior of the framework.",[16,561,562],{},"Output connectors are the only way to access the results obtained in the streaming mode.\nHowever, our outputs are not static but are updated with every new received update: we do not output the full table but only the changes.\nEvery change is represented by a row containing the following columns:",[564,565,566,570,576],"ul",{},[567,568,569],"li",{},"the columns of the table, representing the values which have been modified.",[567,571,572,575],{},[528,573,574],{},"time",", representing the logical time of the update: this time is incremented at each new commit.",[567,577,578,581],{},[528,579,580],{},"diff",", representing whether this update is an addition or a removal. Only two values are possible, 1 for addition and -1 for removal.\nAn update is represented by two rows, one for deleting the previous value and one to add the new.",[16,583,584,585,589,590,593],{},"To see how to set up a realtime streaming application with Pathway, you can try our examples with ",[28,586,588],{"href":587},"\u002Fdevelopers\u002Fuser-guide\u002Fintroduction\u002Ffirst-realtime-app","csv"," and ",[28,591,157],{"href":592},"\u002Fdevelopers\u002Ftemplates\u002Fetl\u002Flinear_regression_with_kafka"," input connectors.",[517,595,597],{"id":596},"connectors-in-static-mode","Connectors in static mode",[16,599,600,601],{},"In the static mode, the computation is done in batch mode: all the data is read, processed, and output at once.\nThere is no notion of update in this mode.\n",[20,602,603],{},"This mode is mainly used for debugging and testing.",[16,605,606,607,23],{},"In addition of a csv connector which dumps the output table into a csv file, Pathway Live Data Framework provides a function which allows to build the graph, to ingest all the data and to print a given table contained in the graph: ",[528,608,505],{},[517,610,612],{"id":611},"persistence-in-connectors","Persistence in connectors",[16,614,615,616,619],{},"Regardless of the mode, a connector can persist the data read along with results of some intermediate computations. This feature can be enabled by specifying persistence config in ",[528,617,618],{},"pw.run"," method. If the connector is persistent, the Pathway Live Data Framework program will preserve its' auxiliary data and will be able to restart from the place where it was terminated last time. This feature may be useful for you in case you need to do re-runs of a program with some added data, or, you want to have a possibility to survive code crashes.",[621,622,624],"h3",{"id":623},"example","Example",[16,626,627],{},"Let's have a quick example on how to manipulate hand-written table in the static mode:",[629,630,635],"pre",{"className":631,"code":632,"language":633,"meta":634,"style":634},"language-python shiki shiki-themes material-theme-palenight","import pathway as pw\n\nt = pw.debug.table_from_markdown(\n    \"\"\"\n    | name  | age\n 1  | Alice | 15\n 2  | Bob   | 32\n 3  | Carole| 28\n 4  | David | 35 \"\"\"\n)\n\npw.debug.compute_and_print(t)\n","python","",[528,636,637,655,662,690,696,703,709,715,721,730,736,741],{"__ignoreMap":634},[80,638,641,645,649,652],{"class":639,"line":640},"line",1,[80,642,644],{"class":643},"s6cf3","import",[80,646,648],{"class":647},"s0W1g"," pathway ",[80,650,651],{"class":643},"as",[80,653,654],{"class":647}," pw\n",[80,656,658],{"class":639,"line":657},2,[80,659,661],{"emptyLinePlaceholder":660},true,"\n",[80,663,665,668,672,675,677,681,683,687],{"class":639,"line":664},3,[80,666,667],{"class":647},"t ",[80,669,671],{"class":670},"sAklC","=",[80,673,674],{"class":647}," pw",[80,676,23],{"class":670},[80,678,680],{"class":679},"s-wAU","debug",[80,682,23],{"class":670},[80,684,686],{"class":685},"sdLwU","table_from_markdown",[80,688,689],{"class":670},"(\n",[80,691,693],{"class":639,"line":692},4,[80,694,695],{"class":670},"    \"\"\"\n",[80,697,699],{"class":639,"line":698},5,[80,700,702],{"class":701},"sfyAc","    | name  | age\n",[80,704,706],{"class":639,"line":705},6,[80,707,708],{"class":701}," 1  | Alice | 15\n",[80,710,712],{"class":639,"line":711},7,[80,713,714],{"class":701}," 2  | Bob   | 32\n",[80,716,718],{"class":639,"line":717},8,[80,719,720],{"class":701}," 3  | Carole| 28\n",[80,722,724,727],{"class":639,"line":723},9,[80,725,726],{"class":701}," 4  | David | 35 ",[80,728,729],{"class":670},"\"\"\"\n",[80,731,733],{"class":639,"line":732},10,[80,734,735],{"class":670},")\n",[80,737,739],{"class":639,"line":738},11,[80,740,661],{"emptyLinePlaceholder":660},[80,742,744,747,749,751,753,756,759,762],{"class":639,"line":743},12,[80,745,746],{"class":647},"pw",[80,748,23],{"class":670},[80,750,680],{"class":679},[80,752,23],{"class":670},[80,754,755],{"class":685},"compute_and_print",[80,757,758],{"class":670},"(",[80,760,761],{"class":685},"t",[80,763,735],{"class":670},[629,765,770],{"className":766,"code":768,"language":769},[767],"language-text","            | name   | age\n^YYY4HAB... | Alice  | 15\n^Z3QWT29... | Bob    | 32\n^3CZ78B4... | Carole | 28\n^3HN31E1... | David  | 35\n","text",[528,771,768],{"__ignoreMap":634},[517,773,775],{"id":774},"compatibility-issues","Compatibility issues",[16,777,778,779,782],{},"Both modes are incompatible: ",[20,780,781],{},"you cannot mix connectors from streaming and static modes",".\nIndeed, the nature of the data manipulated by both types of connectors is very different: data streams versus static data.",[16,784,785,786,788,789,792,793,796],{},"For instance, one might want to print the value of their table ",[528,787,37],{}," in their pipeline to check if the values are correct.\nBetween two ",[528,790,791],{},"select",", a ",[528,794,795],{},"pw.debug.compute_and_print(table)"," is inserted and the computation is run with streaming input connectors.",[16,798,799],{},"What do you think would happen?",[16,801,802,803,805],{},"The program loops. Indeed, ",[528,804,505],{}," waits for the whole data to be ingested entirely in the dataflow before printing the table.\nThis makes sense with finite static data but not in the streaming mode where updates are continuously coming!",[16,807,808],{},"Be careful when you want to debug your pipeline with static data!",[517,810,812],{"id":811},"tutorials","Tutorials",[16,814,815],{},"To learn how to use the different connectors, you can see our tutorials:",[564,817,818,823,828,833,838,843,849],{},[567,819,820],{},[28,821,822],{"href":100},"CSV connectors",[567,824,825],{},[28,826,827],{"href":107},"Database connectors",[567,829,830],{},[28,831,832],{"href":156},"Kafka connectors",[567,834,835],{},[28,836,837],{"href":247},"Switch from Kafka to Redpanda",[567,839,840],{},[28,841,842],{"href":233},"Python input connector",[567,844,845],{},[28,846,848],{"href":847},"\u002Fdevelopers\u002Fuser-guide\u002Fconnect\u002Fconnectors\u002Fpython-output-connectors","Python output 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