🕷️ Crawler Inspector

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Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 37 (from laksa067)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa037.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://neo4j.com/graphgists/getting-the-results-you-want/\')), getAhrefsUnparsedNoserviceFromURL(\'https://neo4j.com/graphgists/getting-the-results-you-want/\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/neo4j.com\/graphgists\/getting-the-results-you-want\/","crawl_time":1774000235,"first_indexed_time":1623245184,"http_code":200,"src_unparsed":"com,neo4j!\/graphgists\/getting-the-results-you-want\/ s443","src_root_hash":"12828843287614304637","history_drop_reason":null,"meta_title":"Getting the Results You Want - graphgists","meta_descriptions":[],"attrs_boilerpipe_text":"Let’s first get some data in to retrieve results from:\nCREATE (matrix:Movie {title:\"The Matrix\",released:1997})\nCREATE (cloudAtlas:Movie {title:\"Cloud Atlas\",released:2012})\nCREATE (forrestGump:Movie {title:\"Forrest Gump\",released:1994})\nCREATE (keanu:Person {name:\"Keanu Reeves\", born:1964})\nCREATE (robert:Person {name:\"Robert Zemeckis\", born:1951})\nCREATE (tom:Person {name:\"Tom Hanks\", born:1956})\nCREATE (tom)-[:ACTED_IN {roles:[\"Forrest\"]}]->(forrestGump)\nCREATE (tom)-[:ACTED_IN {roles:['Zachry']} ]->(cloudAtlas)\nCREATE (robert)-[:DIRECTED]->(forrestGump)\nThis is the data we will start out with:\nNodes created: 6\nRelationships created: 3\nProperties set: 14\nLabels added: 6\nFiltering Results\nSo far we’ve matched patterns in the graph and always returned all results we found.\nQuite often there are conditions in play for what we want to see.\nSimilar to in\nSQL\nthose filter conditions are expressed in a\nWHERE\nclause.\nThis clause allows to use any number of boolean expressions (predicates) combined with\nAND\n,\nOR\n,\nXOR\nand\nNOT\n.\nThe simplest predicates are comparisons, especially equality.\nMATCH (m:Movie)\nWHERE m.title = \"The Matrix\"\nRETURN m\nFor equality on one or more properties, a more compact syntax can be used as well:\nMATCH (m:Movie {title: \"The Matrix\"})\nRETURN m\nOther options are numeric comparisons, matching regular expressions and checking the existence of values within a collection.\nThe\nWHERE\nclause below includes a regular expression match, a greater than comparison and a test to see if a value exists in a collection.\nMATCH (p:Person)-[r:ACTED_IN]->(m:Movie)\nWHERE p.name =~ \"K.+\"\n   OR m.released > 2000\n   OR \"Neo\" IN r.roles\nRETURN p,r,m\nOne aspect that might be a little surprising is that you can even use patterns as predicates.\nWhere\nMATCH\nexpands the number and shape of patterns matched, a pattern predicate restricts the current result set.\nIt only allows the paths to pass that satisfy the additional patterns as well (or\nNOT\n).\nMATCH (p:Person)-[:ACTED_IN]->(m)\nWHERE NOT (p)-[:DIRECTED]->()\nRETURN p,m\nHere we find actors, because they sport an\nACTED_IN\nrelationship but then skip those that ever\nDIRECTED\nany movie.\nThere are also more advanced ways of filtering like collection-predicates which we will look at later on.\nReturning Results\nSo far we’ve returned only nodes, relationships, or paths directly via their identifiers.\nBut the\nRETURN\nclause can actually return any number of expressions.\nBut what are actually expressions in Cypher?\nThe simplest expressions are literal values like numbers, strings and arrays as\n[1,2,3]\n, and maps like\n{name:\"Tom Hanks\", born:1964, movies:[\"Forrest Gump\", …], count:13}\n.\nYou can access individual properties of any node, relationship, or map with a dot-syntax like\nn.name\n.\nIndividual elements or slices of arrays can be retrieved with subscripts like\nnames[0]\nor\nmovies[1..-1]\n.\nEach function evaluation like\nlength(array)\n,\ntoInt(\"12\")\n,\nsubstring(\"2014-07-01\",0,4)\n, or\ncoalesce(p.nickname,\"n\/a\")\nis also an expression.\nPredicates that you’d use in\nWHERE\ncount as boolean expressions.\nOf course simpler expressions can be composed and concatenated to form more complex expressions.\nBy default the expression itself will be used as label for the column, in many cases you want to alias that with a more understandable name using\nexpression AS alias\n.\nYou can later on refer to that column using its alias.\nMATCH (p:Person)\nRETURN p, p.name AS name, upper(p.name), coalesce(p.nickname,\"n\/a\") AS nickname, {name: p.name, label:head(labels(p))} AS person\nIf you’re interested in unique results you can use the\nDISTINCT\nkeyword after\nRETURN\nto indicate that.\nAggregating Information\nIn many cases you want to aggregate or group the data that you encounter while traversing patterns in your graph.\nIn Cypher aggregation happens in the\nRETURN\nclause while computing your final results.\nMany common aggregation functions are supported, e.g.\ncount\n,\nsum\n,\navg\n,\nmin\n, and\nmax\n, but there are several more.\nCounting the number of people in your database could be achieved by this:\nMATCH (:Person)\nRETURN count(*) as people\nPlease note that\nNULL\nvalues are skipped during aggregation.\nFor aggregating only unique values use\nDISTINCT\n, like in\ncount(DISTINCT role)\n.\nAggregation in Cypher just works.\nYou specify which result columns you want to aggregate and\nCypher will use all non-aggregated columns as grouping keys\n.\nAggregation affects which data is still visible in ordering or later query parts.\nTo find out how often an actor and director worked together, you’d run this statement:\nMATCH (actor:Person)-[:ACTED_IN]->(movie:Movie)<-[:DIRECTED]-(director:Person)\nRETURN actor,director,count(*) as collaborations\nFrequently you want to sort and paginate after aggregating a\ncount(x)\n.\nOrdering and Pagination\nOrdering works like in other query languages, with an\nORDER BY expression [ASC|DESC]\nclause.\nThe expression can be any expression discussed before as long as it is computable from the returned information.\nSo for instance if you return\nperson.name\nyou can still\nORDER BY person.age\nas both are accessible from the\nperson\nreference.\nYou cannot order by things that you can’t infer from the information you return.\nThis is especially important with aggregation and\nDISTINCT\nreturn values as both remove the visibility of data that is aggregated.\nPagination is a straightforward use of\nSKIP {offset} LIMIT {count}\n.\nA common pattern is to aggregate for a count (score or frequency), order by it and only return the top-n entries.\nFor instance to find the most prolific actors you could do:\nMATCH (a:Person)-[:ACTED_IN]->(m:Movie)\nRETURN a,count(*) as appearances\nORDER BY appearances DESC\nLIMIT 10;\nCollecting Aggregation\nThe most helpful aggregation function is\ncollect\n, which, as the name says, collects all aggregated values into a\nreal\narray or list.\nThis comes very handy in many situations as you don’t loose the detail information while aggregating.\nCollect is well suited for retrieving the typical parent-child structures, where one core entity (parent, root or head) is returned per row with all it’s dependent information in associated collections created with\ncollect\n.\nThis means there’s no need to repeat the parent information per each child-row or even running 1+n statements to retrieve the parent and its children individually.\nTo retrieve the cast of each movie in our database you could use this statement:\nMATCH (m:Movie)<-[:ACTED_IN]-(a:Person)\nRETURN m.title as movie, collect(a.name) as cast, count(*) as actors\nThe lists created by collect can either be used from the client consuming the Cypher results or directly within a statement with any of the collection functions or predicates.","attrs_markdown":"[![Neo4j GraphGists](https:\/\/dist.neo4j.com\/wp-content\/uploads\/20231023102534\/neo4j-logo.svg)](https:\/\/neo4j.com\/)   [GraphGists](https:\/\/neo4j.com\/graphgists\/)\n\n[Use Cases](https:\/\/neo4j.com\/graphgists\/)\n\n[General Business](https:\/\/neo4j.com\/graphgists\/categories\/general-business\/)\n\n[Data Analysis](https:\/\/neo4j.com\/graphgists\/categories\/data-analysis\/)\n\n[Pop Culture](https:\/\/neo4j.com\/graphgists\/categories\/pop-culture\/)\n\n[Network and IT 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Want\nAuthor\n\nwhatSocks\n\nIndustries\n\n[Transportation and Logistics](https:\/\/neo4j.com\/graphgists\/categories\/transportation-and-logistics\/) [Finance](https:\/\/neo4j.com\/graphgists\/categories\/finance\/) [Retail](https:\/\/neo4j.com\/graphgists\/categories\/retail\/) [Comp Sci and Programming](https:\/\/neo4j.com\/graphgists\/categories\/computer-science-and-programming\/) [Science](https:\/\/neo4j.com\/graphgists\/categories\/science\/) [Web & Social](https:\/\/neo4j.com\/graphgists\/categories\/web-amp-social\/) [Sports and Recreation](https:\/\/neo4j.com\/graphgists\/categories\/sports-and-gaming\/) [Health Care](https:\/\/neo4j.com\/graphgists\/categories\/health-care-and-science\/) [Education](https:\/\/neo4j.com\/graphgists\/categories\/education\/)\n\nUse cases\n\n[Graph Gist How-tos](https:\/\/neo4j.com\/graphgists\/categories\/graph-gist-how-tos\/)\n\n- [Show source](https:\/\/portal.graphgist.org\/graph_gists\/getting-the-results-you-want\/source)\n- [Download as Jupyter Notebook](https:\/\/neo4j.com\/graphgists\/_attachments\/getting-the-results-you-want.ipynb)\n- Open in Neo4j Browser\n  \n  Connection URL\n  \n  [Open]()\n\nLet’s first get some data in to retrieve results from:\n```\nCREATE (matrix:Movie {title:\"The Matrix\",released:1997})\nCREATE (cloudAtlas:Movie {title:\"Cloud Atlas\",released:2012})\nCREATE (forrestGump:Movie {title:\"Forrest Gump\",released:1994})\nCREATE (keanu:Person {name:\"Keanu Reeves\", born:1964})\nCREATE (robert:Person {name:\"Robert Zemeckis\", born:1951})\nCREATE (tom:Person {name:\"Tom Hanks\", born:1956})\nCREATE (tom)-[:ACTED_IN {roles:[\"Forrest\"]}]->(forrestGump)\nCREATE (tom)-[:ACTED_IN {roles:['Zachry']} ]->(cloudAtlas)\nCREATE (robert)-[:DIRECTED]->(forrestGump)\n```\nThis is the data we will start out with:\n```\nNodes created: 6\nRelationships created: 3\nProperties set: 14\nLabels added: 6\n```\n# Filtering Results\nSo far we’ve matched patterns in the graph and always returned all results we found. Quite often there are conditions in play for what we want to see. Similar to in *SQL* those filter conditions are expressed in a `WHERE` clause. This clause allows to use any number of boolean expressions (predicates) combined with `AND`, `OR`, `XOR` and `NOT`. The simplest predicates are comparisons, especially equality.\n```\nMATCH (m:Movie)\nWHERE m.title = \"The Matrix\"\nRETURN m\n```\nFor equality on one or more properties, a more compact syntax can be used as well:\n```\nMATCH (m:Movie {title: \"The Matrix\"})\nRETURN m\n```\nOther options are numeric comparisons, matching regular expressions and checking the existence of values within a collection.\n\nThe `WHERE` clause below includes a regular expression match, a greater than comparison and a test to see if a value exists in a collection.\n```\nMATCH (p:Person)-[r:ACTED_IN]->(m:Movie)\nWHERE p.name =~ \"K.+\"\n   OR m.released > 2000\n   OR \"Neo\" IN r.roles\nRETURN p,r,m\n```\nOne aspect that might be a little surprising is that you can even use patterns as predicates. Where `MATCH` expands the number and shape of patterns matched, a pattern predicate restricts the current result set. It only allows the paths to pass that satisfy the additional patterns as well (or `NOT`).\n```\nMATCH (p:Person)-[:ACTED_IN]->(m)\nWHERE NOT (p)-[:DIRECTED]->()\nRETURN p,m\n```\nHere we find actors, because they sport an `ACTED_IN` relationship but then skip those that ever `DIRECTED` any movie.\n\nThere are also more advanced ways of filtering like collection-predicates which we will look at later on.\n\n# Returning Results\nSo far we’ve returned only nodes, relationships, or paths directly via their identifiers. But the `RETURN` clause can actually return any number of expressions. But what are actually expressions in Cypher?\n\nThe simplest expressions are literal values like numbers, strings and arrays as `[1,2,3]`, and maps like `{name:\"Tom Hanks\", born:1964, movies:[\"Forrest Gump\", …], count:13}`. You can access individual properties of any node, relationship, or map with a dot-syntax like `n.name`. Individual elements or slices of arrays can be retrieved with subscripts like `names[0]` or `movies[1..-1]`. Each function evaluation like `length(array)`, `toInt(\"12\")`, `substring(\"2014-07-01\",0,4)`, or `coalesce(p.nickname,\"n\/a\")` is also an expression.\n\nPredicates that you’d use in `WHERE` count as boolean expressions.\n\nOf course simpler expressions can be composed and concatenated to form more complex expressions.\n\nBy default the expression itself will be used as label for the column, in many cases you want to alias that with a more understandable name using `expression AS alias`. You can later on refer to that column using its alias.\n```\nMATCH (p:Person)\nRETURN p, p.name AS name, upper(p.name), coalesce(p.nickname,\"n\/a\") AS nickname, {name: p.name, label:head(labels(p))} AS person\n```\nIf you’re interested in unique results you can use the `DISTINCT` keyword after `RETURN` to indicate that.\n\n# Aggregating Information\nIn many cases you want to aggregate or group the data that you encounter while traversing patterns in your graph. In Cypher aggregation happens in the `RETURN` clause while computing your final results. Many common aggregation functions are supported, e.g. `count`, `sum`, `avg`, `min`, and `max`, but there are several more.\n\nCounting the number of people in your database could be achieved by this:\n```\nMATCH (:Person)\nRETURN count(*) as people\n```\nPlease note that `NULL` values are skipped during aggregation. For aggregating only unique values use `DISTINCT`, like in `count(DISTINCT role)`.\n\nAggregation in Cypher just works. You specify which result columns you want to aggregate and *Cypher will use all non-aggregated columns as grouping keys*.\n\nAggregation affects which data is still visible in ordering or later query parts.\n\nTo find out how often an actor and director worked together, you’d run this statement:\n```\nMATCH (actor:Person)-[:ACTED_IN]->(movie:Movie)<-[:DIRECTED]-(director:Person)\nRETURN actor,director,count(*) as collaborations\n```\nFrequently you want to sort and paginate after aggregating a `count(x)`.\n\n# Ordering and Pagination\nOrdering works like in other query languages, with an `ORDER BY expression [ASC|DESC]` clause. The expression can be any expression discussed before as long as it is computable from the returned information.\n\nSo for instance if you return `person.name` you can still `ORDER BY person.age` as both are accessible from the `person` reference. You cannot order by things that you can’t infer from the information you return. This is especially important with aggregation and `DISTINCT` return values as both remove the visibility of data that is aggregated.\n\nPagination is a straightforward use of `SKIP {offset} LIMIT {count}`.\n\nA common pattern is to aggregate for a count (score or frequency), order by it and only return the top-n entries.\n\nFor instance to find the most prolific actors you could do:\n```\nMATCH (a:Person)-[:ACTED_IN]->(m:Movie)\nRETURN a,count(*) as appearances\nORDER BY appearances DESC\nLIMIT 10;\n```\n# Collecting Aggregation\nThe most helpful aggregation function is `collect`, which, as the name says, collects all aggregated values into a *real* array or list. This comes very handy in many situations as you don’t loose the detail information while aggregating.\n\nCollect is well suited for retrieving the typical parent-child structures, where one core entity (parent, root or head) is returned per row with all it’s dependent information in associated collections created with `collect`. This means there’s no need to repeat the parent information per each child-row or even running 1+n statements to retrieve the parent and its children individually.\n\nTo retrieve the cast of each movie in our database you could use this statement:\n```\nMATCH (m:Movie)<-[:ACTED_IN]-(a:Person)\nRETURN m.title as movie, collect(a.name) as cast, count(*) as actors\n```\nThe lists created by collect can either be used from the client consuming the Cypher results or directly within a statement with any of the collection functions or predicates.\n\n**Is this page helpful?**\n\n## Learn\n- [Sandbox](https:\/\/neo4j.com\/sandbox\/?ref=developer-footer)\n- [Neo4j Community Site](https:\/\/community.neo4j.com\/?ref=developer-footer)\n- [Neo4j Developer Blog](https:\/\/medium.com\/neo4j)\n- [Neo4j Videos](https:\/\/www.youtube.com\/neo4j)\n- [GraphAcademy](https:\/\/neo4j.com\/graphacademy\/?ref=developer-footer)\n- [Neo4j Labs](https:\/\/neo4j.com\/labs\/?ref=developer-footer)\n\n## Social\n- [Twitter](https:\/\/twitter.com\/neo4j)\n- [Meetups](https:\/\/www.meetup.com\/Neo4j-Online-Meetup\/)\n- [Github](https:\/\/github.com\/neo4j\/neo4j)\n- [Stack Overflow](https:\/\/stackoverflow.com\/questions\/tagged\/neo4j)\n- [Want to Speak?](https:\/\/docs.google.com\/forms\/d\/e\/1FAIpQLSdEcNnMruES5iwvOVYovmS1D_P1ZL_HdUOitFrwrvruv5PZvA\/viewform)\n\n## [Contact Us →](https:\/\/neo4j.com\/contact-us\/?ref=footer)\n- US: 1-855-636-4532\n- Sweden +46 171 480 113\n- UK: +44 20 3868 3223\n- France: +33 (0) 1 88 46 13 20\n\n© 2026 Neo4j, Inc.  \n [Terms](https:\/\/neo4j.com\/terms\/) \\| [Privacy](https:\/\/neo4j.com\/privacy-policy\/)  \\| [Sitemap](https:\/\/neo4j.com\/sitemap\/)\n\nNeo4j®, Neo Technology®, Cypher®, Neo4j® Bloom™ and Neo4j® Aura™ are registered trademarks of Neo4j, Inc. All other marks are owned by their respective companies.","attrs_readable_markdown":"Let’s first get some data in to retrieve results from:\n```\nCREATE (matrix:Movie {title:\"The Matrix\",released:1997})\nCREATE (cloudAtlas:Movie {title:\"Cloud Atlas\",released:2012})\nCREATE (forrestGump:Movie {title:\"Forrest Gump\",released:1994})\nCREATE (keanu:Person {name:\"Keanu Reeves\", born:1964})\nCREATE (robert:Person {name:\"Robert Zemeckis\", born:1951})\nCREATE (tom:Person {name:\"Tom Hanks\", born:1956})\nCREATE (tom)-[:ACTED_IN {roles:[\"Forrest\"]}]->(forrestGump)\nCREATE (tom)-[:ACTED_IN {roles:['Zachry']} ]->(cloudAtlas)\nCREATE (robert)-[:DIRECTED]->(forrestGump)\n```\nThis is the data we will start out with:\n```\nNodes created: 6\nRelationships created: 3\nProperties set: 14\nLabels added: 6\n```\n## Filtering Results\nSo far we’ve matched patterns in the graph and always returned all results we found. Quite often there are conditions in play for what we want to see. Similar to in *SQL* those filter conditions are expressed in a `WHERE` clause. This clause allows to use any number of boolean expressions (predicates) combined with `AND`, `OR`, `XOR` and `NOT`. The simplest predicates are comparisons, especially equality.\n```\nMATCH (m:Movie)\nWHERE m.title = \"The Matrix\"\nRETURN m\n```\nFor equality on one or more properties, a more compact syntax can be used as well:\n```\nMATCH (m:Movie {title: \"The Matrix\"})\nRETURN m\n```\nOther options are numeric comparisons, matching regular expressions and checking the existence of values within a collection.\n\nThe `WHERE` clause below includes a regular expression match, a greater than comparison and a test to see if a value exists in a collection.\n```\nMATCH (p:Person)-[r:ACTED_IN]->(m:Movie)\nWHERE p.name =~ \"K.+\"\n   OR m.released > 2000\n   OR \"Neo\" IN r.roles\nRETURN p,r,m\n```\nOne aspect that might be a little surprising is that you can even use patterns as predicates. Where `MATCH` expands the number and shape of patterns matched, a pattern predicate restricts the current result set. It only allows the paths to pass that satisfy the additional patterns as well (or `NOT`).\n```\nMATCH (p:Person)-[:ACTED_IN]->(m)\nWHERE NOT (p)-[:DIRECTED]->()\nRETURN p,m\n```\nHere we find actors, because they sport an `ACTED_IN` relationship but then skip those that ever `DIRECTED` any movie.\n\nThere are also more advanced ways of filtering like collection-predicates which we will look at later on.\n\n## Returning Results\nSo far we’ve returned only nodes, relationships, or paths directly via their identifiers. But the `RETURN` clause can actually return any number of expressions. But what are actually expressions in Cypher?\n\nThe simplest expressions are literal values like numbers, strings and arrays as `[1,2,3]`, and maps like `{name:\"Tom Hanks\", born:1964, movies:[\"Forrest Gump\", …], count:13}`. You can access individual properties of any node, relationship, or map with a dot-syntax like `n.name`. Individual elements or slices of arrays can be retrieved with subscripts like `names[0]` or `movies[1..-1]`. Each function evaluation like `length(array)`, `toInt(\"12\")`, `substring(\"2014-07-01\",0,4)`, or `coalesce(p.nickname,\"n\/a\")` is also an expression.\n\nPredicates that you’d use in `WHERE` count as boolean expressions.\n\nOf course simpler expressions can be composed and concatenated to form more complex expressions.\n\nBy default the expression itself will be used as label for the column, in many cases you want to alias that with a more understandable name using `expression AS alias`. You can later on refer to that column using its alias.\n```\nMATCH (p:Person)\nRETURN p, p.name AS name, upper(p.name), coalesce(p.nickname,\"n\/a\") AS nickname, {name: p.name, label:head(labels(p))} AS person\n```\nIf you’re interested in unique results you can use the `DISTINCT` keyword after `RETURN` to indicate that.\n\n## Aggregating Information\nIn many cases you want to aggregate or group the data that you encounter while traversing patterns in your graph. In Cypher aggregation happens in the `RETURN` clause while computing your final results. Many common aggregation functions are supported, e.g. `count`, `sum`, `avg`, `min`, and `max`, but there are several more.\n\nCounting the number of people in your database could be achieved by this:\n```\nMATCH (:Person)\nRETURN count(*) as people\n```\nPlease note that `NULL` values are skipped during aggregation. For aggregating only unique values use `DISTINCT`, like in `count(DISTINCT role)`.\n\nAggregation in Cypher just works. You specify which result columns you want to aggregate and *Cypher will use all non-aggregated columns as grouping keys*.\n\nAggregation affects which data is still visible in ordering or later query parts.\n\nTo find out how often an actor and director worked together, you’d run this statement:\n```\nMATCH (actor:Person)-[:ACTED_IN]->(movie:Movie)<-[:DIRECTED]-(director:Person)\nRETURN actor,director,count(*) as collaborations\n```\nFrequently you want to sort and paginate after aggregating a `count(x)`.\n\n## Ordering and Pagination\nOrdering works like in other query languages, with an `ORDER BY expression [ASC|DESC]` clause. The expression can be any expression discussed before as long as it is computable from the returned information.\n\nSo for instance if you return `person.name` you can still `ORDER BY person.age` as both are accessible from the `person` reference. You cannot order by things that you can’t infer from the information you return. This is especially important with aggregation and `DISTINCT` return values as both remove the visibility of data that is aggregated.\n\nPagination is a straightforward use of `SKIP {offset} LIMIT {count}`.\n\nA common pattern is to aggregate for a count (score or frequency), order by it and only return the top-n entries.\n\nFor instance to find the most prolific actors you could do:\n```\nMATCH (a:Person)-[:ACTED_IN]->(m:Movie)\nRETURN a,count(*) as appearances\nORDER BY appearances DESC\nLIMIT 10;\n```\n## Collecting Aggregation\nThe most helpful aggregation function is `collect`, which, as the name says, collects all aggregated values into a *real* array or list. This comes very handy in many situations as you don’t loose the detail information while aggregating.\n\nCollect is well suited for retrieving the typical parent-child structures, where one core entity (parent, root or head) is returned per row with all it’s dependent information in associated collections created with `collect`. This means there’s no need to repeat the parent information per each child-row or even running 1+n statements to retrieve the parent and its children individually.\n\nTo retrieve the cast of each movie in our database you could use this statement:\n```\nMATCH (m:Movie)<-[:ACTED_IN]-(a:Person)\nRETURN m.title as movie, collect(a.name) as cast, count(*) as actors\n```\nThe lists created by collect can either be used from the client consuming the Cypher results or directly within a statement with any of the collection functions or predicates.","meta_canonical":null}

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Boilerpipe Text	Let’s first get some data in to retrieve results from: CREATE (matrix:Movie {title:"The Matrix",released:1997}) CREATE (cloudAtlas:Movie {title:"Cloud Atlas",released:2012}) CREATE (forrestGump:Movie {title:"Forrest Gump",released:1994}) CREATE (keanu:Person {name:"Keanu Reeves", born:1964}) CREATE (robert:Person {name:"Robert Zemeckis", born:1951}) CREATE (tom:Person {name:"Tom Hanks", born:1956}) CREATE (tom)-[:ACTED_IN {roles:["Forrest"]}]->(forrestGump) CREATE (tom)-[:ACTED_IN {roles:['Zachry']} ]->(cloudAtlas) CREATE (robert)-[:DIRECTED]->(forrestGump) This is the data we will start out with: Nodes created: 6 Relationships created: 3 Properties set: 14 Labels added: 6 Filtering Results So far we’ve matched patterns in the graph and always returned all results we found. Quite often there are conditions in play for what we want to see. Similar to in SQL those filter conditions are expressed in a WHERE clause. This clause allows to use any number of boolean expressions (predicates) combined with AND , OR , XOR and NOT . The simplest predicates are comparisons, especially equality. MATCH (m:Movie) WHERE m.title = "The Matrix" RETURN m For equality on one or more properties, a more compact syntax can be used as well: MATCH (m:Movie {title: "The Matrix"}) RETURN m Other options are numeric comparisons, matching regular expressions and checking the existence of values within a collection. The WHERE clause below includes a regular expression match, a greater than comparison and a test to see if a value exists in a collection. MATCH (p:Person)-[r:ACTED_IN]->(m:Movie) WHERE p.name =~ "K.+" OR m.released > 2000 OR "Neo" IN r.roles RETURN p,r,m One aspect that might be a little surprising is that you can even use patterns as predicates. Where MATCH expands the number and shape of patterns matched, a pattern predicate restricts the current result set. It only allows the paths to pass that satisfy the additional patterns as well (or NOT ). MATCH (p:Person)-[:ACTED_IN]->(m) WHERE NOT (p)-[:DIRECTED]->() RETURN p,m Here we find actors, because they sport an ACTED_IN relationship but then skip those that ever DIRECTED any movie. There are also more advanced ways of filtering like collection-predicates which we will look at later on. Returning Results So far we’ve returned only nodes, relationships, or paths directly via their identifiers. But the RETURN clause can actually return any number of expressions. But what are actually expressions in Cypher? The simplest expressions are literal values like numbers, strings and arrays as [1,2,3] , and maps like {name:"Tom Hanks", born:1964, movies:["Forrest Gump", …], count:13} . You can access individual properties of any node, relationship, or map with a dot-syntax like n.name . Individual elements or slices of arrays can be retrieved with subscripts like names[0] or movies[1..-1] . Each function evaluation like length(array) , toInt("12") , substring("2014-07-01",0,4) , or coalesce(p.nickname,"n/a") is also an expression. Predicates that you’d use in WHERE count as boolean expressions. Of course simpler expressions can be composed and concatenated to form more complex expressions. By default the expression itself will be used as label for the column, in many cases you want to alias that with a more understandable name using expression AS alias . You can later on refer to that column using its alias. MATCH (p:Person) RETURN p, p.name AS name, upper(p.name), coalesce(p.nickname,"n/a") AS nickname, {name: p.name, label:head(labels(p))} AS person If you’re interested in unique results you can use the DISTINCT keyword after RETURN to indicate that. Aggregating Information In many cases you want to aggregate or group the data that you encounter while traversing patterns in your graph. In Cypher aggregation happens in the RETURN clause while computing your final results. Many common aggregation functions are supported, e.g. count , sum , avg , min , and max , but there are several more. Counting the number of people in your database could be achieved by this: MATCH (:Person) RETURN count() as people Please note that NULL values are skipped during aggregation. For aggregating only unique values use DISTINCT , like in count(DISTINCT role) . Aggregation in Cypher just works. You specify which result columns you want to aggregate and Cypher will use all non-aggregated columns as grouping keys . Aggregation affects which data is still visible in ordering or later query parts. To find out how often an actor and director worked together, you’d run this statement: MATCH (actor:Person)-[:ACTED_IN]->(movie:Movie)<-[:DIRECTED]-(director:Person) RETURN actor,director,count() as collaborations Frequently you want to sort and paginate after aggregating a count(x) . Ordering and Pagination Ordering works like in other query languages, with an ORDER BY expression [ASC\|DESC] clause. The expression can be any expression discussed before as long as it is computable from the returned information. So for instance if you return person.name you can still ORDER BY person.age as both are accessible from the person reference. You cannot order by things that you can’t infer from the information you return. This is especially important with aggregation and DISTINCT return values as both remove the visibility of data that is aggregated. Pagination is a straightforward use of SKIP {offset} LIMIT {count} . A common pattern is to aggregate for a count (score or frequency), order by it and only return the top-n entries. For instance to find the most prolific actors you could do: MATCH (a:Person)-[:ACTED_IN]->(m:Movie) RETURN a,count() as appearances ORDER BY appearances DESC LIMIT 10; Collecting Aggregation The most helpful aggregation function is collect , which, as the name says, collects all aggregated values into a real array or list. This comes very handy in many situations as you don’t loose the detail information while aggregating. Collect is well suited for retrieving the typical parent-child structures, where one core entity (parent, root or head) is returned per row with all it’s dependent information in associated collections created with collect . This means there’s no need to repeat the parent information per each child-row or even running 1+n statements to retrieve the parent and its children individually. To retrieve the cast of each movie in our database you could use this statement: MATCH (m:Movie)<-[:ACTED_IN]-(a:Person) RETURN m.title as movie, collect(a.name) as cast, count() as actors The lists created by collect can either be used from the client consuming the Cypher results or directly within a statement with any of the collection functions or predicates.
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[Show source](https://portal.graphgist.org/graph_gists/getting-the-results-you-want/source) - [Download as Jupyter Notebook](https://neo4j.com/graphgists/_attachments/getting-the-results-you-want.ipynb) - Open in Neo4j Browser Connection URL [Open]() Let’s first get some data in to retrieve results from: ``` CREATE (matrix:Movie {title:"The Matrix",released:1997}) CREATE (cloudAtlas:Movie {title:"Cloud Atlas",released:2012}) CREATE (forrestGump:Movie {title:"Forrest Gump",released:1994}) CREATE (keanu:Person {name:"Keanu Reeves", born:1964}) CREATE (robert:Person {name:"Robert Zemeckis", born:1951}) CREATE (tom:Person {name:"Tom Hanks", born:1956}) CREATE (tom)-[:ACTED_IN {roles:["Forrest"]}]->(forrestGump) CREATE (tom)-[:ACTED_IN {roles:['Zachry']} ]->(cloudAtlas) CREATE (robert)-[:DIRECTED]->(forrestGump) ``` This is the data we will start out with: ``` Nodes created: 6 Relationships created: 3 Properties set: 14 Labels added: 6 ``` # Filtering Results So far we’ve matched patterns in the graph and always returned all results we found. Quite often there are conditions in play for what we want to see. Similar to in SQL those filter conditions are expressed in a `WHERE` clause. This clause allows to use any number of boolean expressions (predicates) combined with `AND`, `OR`, `XOR` and `NOT`. The simplest predicates are comparisons, especially equality. ``` MATCH (m:Movie) WHERE m.title = "The Matrix" RETURN m ``` For equality on one or more properties, a more compact syntax can be used as well: ``` MATCH (m:Movie {title: "The Matrix"}) RETURN m ``` Other options are numeric comparisons, matching regular expressions and checking the existence of values within a collection. The `WHERE` clause below includes a regular expression match, a greater than comparison and a test to see if a value exists in a collection. ``` MATCH (p:Person)-[r:ACTED_IN]->(m:Movie) WHERE p.name =~ "K.+" OR m.released > 2000 OR "Neo" IN r.roles RETURN p,r,m ``` One aspect that might be a little surprising is that you can even use patterns as predicates. Where `MATCH` expands the number and shape of patterns matched, a pattern predicate restricts the current result set. It only allows the paths to pass that satisfy the additional patterns as well (or `NOT`). ``` MATCH (p:Person)-[:ACTED_IN]->(m) WHERE NOT (p)-[:DIRECTED]->() RETURN p,m ``` Here we find actors, because they sport an `ACTED_IN` relationship but then skip those that ever `DIRECTED` any movie. There are also more advanced ways of filtering like collection-predicates which we will look at later on. # Returning Results So far we’ve returned only nodes, relationships, or paths directly via their identifiers. But the `RETURN` clause can actually return any number of expressions. But what are actually expressions in Cypher? The simplest expressions are literal values like numbers, strings and arrays as `[1,2,3]`, and maps like `{name:"Tom Hanks", born:1964, movies:["Forrest Gump", …], count:13}`. You can access individual properties of any node, relationship, or map with a dot-syntax like `n.name`. Individual elements or slices of arrays can be retrieved with subscripts like `names[0]` or `movies[1..-1]`. Each function evaluation like `length(array)`, `toInt("12")`, `substring("2014-07-01",0,4)`, or `coalesce(p.nickname,"n/a")` is also an expression. Predicates that you’d use in `WHERE` count as boolean expressions. Of course simpler expressions can be composed and concatenated to form more complex expressions. By default the expression itself will be used as label for the column, in many cases you want to alias that with a more understandable name using `expression AS alias`. You can later on refer to that column using its alias. ``` MATCH (p:Person) RETURN p, p.name AS name, upper(p.name), coalesce(p.nickname,"n/a") AS nickname, {name: p.name, label:head(labels(p))} AS person ``` If you’re interested in unique results you can use the `DISTINCT` keyword after `RETURN` to indicate that. # Aggregating Information In many cases you want to aggregate or group the data that you encounter while traversing patterns in your graph. In Cypher aggregation happens in the `RETURN` clause while computing your final results. Many common aggregation functions are supported, e.g. `count`, `sum`, `avg`, `min`, and `max`, but there are several more. Counting the number of people in your database could be achieved by this: ``` MATCH (:Person) RETURN count() as people ``` Please note that `NULL` values are skipped during aggregation. For aggregating only unique values use `DISTINCT`, like in `count(DISTINCT role)`. Aggregation in Cypher just works. You specify which result columns you want to aggregate and Cypher will use all non-aggregated columns as grouping keys. Aggregation affects which data is still visible in ordering or later query parts. To find out how often an actor and director worked together, you’d run this statement: ``` MATCH (actor:Person)-[:ACTED_IN]->(movie:Movie)<-[:DIRECTED]-(director:Person) RETURN actor,director,count() as collaborations ``` Frequently you want to sort and paginate after aggregating a `count(x)`. # Ordering and Pagination Ordering works like in other query languages, with an `ORDER BY expression [ASC\|DESC]` clause. The expression can be any expression discussed before as long as it is computable from the returned information. So for instance if you return `person.name` you can still `ORDER BY person.age` as both are accessible from the `person` reference. You cannot order by things that you can’t infer from the information you return. This is especially important with aggregation and `DISTINCT` return values as both remove the visibility of data that is aggregated. Pagination is a straightforward use of `SKIP {offset} LIMIT {count}`. A common pattern is to aggregate for a count (score or frequency), order by it and only return the top-n entries. For instance to find the most prolific actors you could do: ``` MATCH (a:Person)-[:ACTED_IN]->(m:Movie) RETURN a,count() as appearances ORDER BY appearances DESC LIMIT 10; ``` # Collecting Aggregation The most helpful aggregation function is `collect`, which, as the name says, collects all aggregated values into a real* array or list. This comes very handy in many situations as you don’t loose the detail information while aggregating. Collect is well suited for retrieving the typical parent-child structures, where one core entity (parent, root or head) is returned per row with all it’s dependent information in associated collections created with `collect`. This means there’s no need to repeat the parent information per each child-row or even running 1+n statements to retrieve the parent and its children individually. To retrieve the cast of each movie in our database you could use this statement: ``` MATCH (m:Movie)<-[:ACTED_IN]-(a:Person) RETURN m.title as movie, collect(a.name) as cast, count() as actors ``` The lists created by collect can either be used from the client consuming the Cypher results or directly within a statement with any of the collection functions or predicates. 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Readable Markdown	Let’s first get some data in to retrieve results from: ``` CREATE (matrix:Movie {title:"The Matrix",released:1997}) CREATE (cloudAtlas:Movie {title:"Cloud Atlas",released:2012}) CREATE (forrestGump:Movie {title:"Forrest Gump",released:1994}) CREATE (keanu:Person {name:"Keanu Reeves", born:1964}) CREATE (robert:Person {name:"Robert Zemeckis", born:1951}) CREATE (tom:Person {name:"Tom Hanks", born:1956}) CREATE (tom)-[:ACTED_IN {roles:["Forrest"]}]->(forrestGump) CREATE (tom)-[:ACTED_IN {roles:['Zachry']} ]->(cloudAtlas) CREATE (robert)-[:DIRECTED]->(forrestGump) ``` This is the data we will start out with: ``` Nodes created: 6 Relationships created: 3 Properties set: 14 Labels added: 6 ``` ## Filtering Results So far we’ve matched patterns in the graph and always returned all results we found. Quite often there are conditions in play for what we want to see. Similar to in SQL those filter conditions are expressed in a `WHERE` clause. This clause allows to use any number of boolean expressions (predicates) combined with `AND`, `OR`, `XOR` and `NOT`. The simplest predicates are comparisons, especially equality. ``` MATCH (m:Movie) WHERE m.title = "The Matrix" RETURN m ``` For equality on one or more properties, a more compact syntax can be used as well: ``` MATCH (m:Movie {title: "The Matrix"}) RETURN m ``` Other options are numeric comparisons, matching regular expressions and checking the existence of values within a collection. The `WHERE` clause below includes a regular expression match, a greater than comparison and a test to see if a value exists in a collection. ``` MATCH (p:Person)-[r:ACTED_IN]->(m:Movie) WHERE p.name =~ "K.+" OR m.released > 2000 OR "Neo" IN r.roles RETURN p,r,m ``` One aspect that might be a little surprising is that you can even use patterns as predicates. Where `MATCH` expands the number and shape of patterns matched, a pattern predicate restricts the current result set. It only allows the paths to pass that satisfy the additional patterns as well (or `NOT`). ``` MATCH (p:Person)-[:ACTED_IN]->(m) WHERE NOT (p)-[:DIRECTED]->() RETURN p,m ``` Here we find actors, because they sport an `ACTED_IN` relationship but then skip those that ever `DIRECTED` any movie. There are also more advanced ways of filtering like collection-predicates which we will look at later on. ## Returning Results So far we’ve returned only nodes, relationships, or paths directly via their identifiers. But the `RETURN` clause can actually return any number of expressions. But what are actually expressions in Cypher? The simplest expressions are literal values like numbers, strings and arrays as `[1,2,3]`, and maps like `{name:"Tom Hanks", born:1964, movies:["Forrest Gump", …], count:13}`. You can access individual properties of any node, relationship, or map with a dot-syntax like `n.name`. Individual elements or slices of arrays can be retrieved with subscripts like `names[0]` or `movies[1..-1]`. Each function evaluation like `length(array)`, `toInt("12")`, `substring("2014-07-01",0,4)`, or `coalesce(p.nickname,"n/a")` is also an expression. Predicates that you’d use in `WHERE` count as boolean expressions. Of course simpler expressions can be composed and concatenated to form more complex expressions. By default the expression itself will be used as label for the column, in many cases you want to alias that with a more understandable name using `expression AS alias`. You can later on refer to that column using its alias. ``` MATCH (p:Person) RETURN p, p.name AS name, upper(p.name), coalesce(p.nickname,"n/a") AS nickname, {name: p.name, label:head(labels(p))} AS person ``` If you’re interested in unique results you can use the `DISTINCT` keyword after `RETURN` to indicate that. ## Aggregating Information In many cases you want to aggregate or group the data that you encounter while traversing patterns in your graph. In Cypher aggregation happens in the `RETURN` clause while computing your final results. Many common aggregation functions are supported, e.g. `count`, `sum`, `avg`, `min`, and `max`, but there are several more. Counting the number of people in your database could be achieved by this: ``` MATCH (:Person) RETURN count() as people ``` Please note that `NULL` values are skipped during aggregation. For aggregating only unique values use `DISTINCT`, like in `count(DISTINCT role)`. Aggregation in Cypher just works. You specify which result columns you want to aggregate and Cypher will use all non-aggregated columns as grouping keys. Aggregation affects which data is still visible in ordering or later query parts. To find out how often an actor and director worked together, you’d run this statement: ``` MATCH (actor:Person)-[:ACTED_IN]->(movie:Movie)<-[:DIRECTED]-(director:Person) RETURN actor,director,count() as collaborations ``` Frequently you want to sort and paginate after aggregating a `count(x)`. ## Ordering and Pagination Ordering works like in other query languages, with an `ORDER BY expression [ASC\|DESC]` clause. The expression can be any expression discussed before as long as it is computable from the returned information. So for instance if you return `person.name` you can still `ORDER BY person.age` as both are accessible from the `person` reference. You cannot order by things that you can’t infer from the information you return. This is especially important with aggregation and `DISTINCT` return values as both remove the visibility of data that is aggregated. Pagination is a straightforward use of `SKIP {offset} LIMIT {count}`. A common pattern is to aggregate for a count (score or frequency), order by it and only return the top-n entries. For instance to find the most prolific actors you could do: ``` MATCH (a:Person)-[:ACTED_IN]->(m:Movie) RETURN a,count() as appearances ORDER BY appearances DESC LIMIT 10; ``` ## Collecting Aggregation The most helpful aggregation function is `collect`, which, as the name says, collects all aggregated values into a real* array or list. This comes very handy in many situations as you don’t loose the detail information while aggregating. Collect is well suited for retrieving the typical parent-child structures, where one core entity (parent, root or head) is returned per row with all it’s dependent information in associated collections created with `collect`. This means there’s no need to repeat the parent information per each child-row or even running 1+n statements to retrieve the parent and its children individually. To retrieve the cast of each movie in our database you could use this statement: ``` MATCH (m:Movie)<-[:ACTED_IN]-(a:Person) RETURN m.title as movie, collect(a.name) as cast, count(*) as actors ``` The lists created by collect can either be used from the client consuming the Cypher results or directly within a statement with any of the collection functions or predicates.
Shard	37 (laksa)
Root Hash	12828843287614304637
Unparsed URL	com,neo4j!/graphgists/getting-the-results-you-want/ s443