How to call JSON filesystem structure in HTML file and javascript - filesystems
I have the following JSON file. I am wondering how can I call the files in the HTML file specially that there is a documents in some documents.
{
"d_type": "directory",
"name": ".",
"size": 1020,
"subdirectory": [
{
"d_type": "file",
"name": ".DS_Store",
"size": 15364
},
{
"d_type": "file",
"name": "animation.py",
"size": 928
},
{
"d_type": "file",
"name": "cat.png",
"size": 12574
},
{
"d_type": "file",
"name": "courses.json",
"size": 714
},
{
"d_type": "file",
"name": "courses.py",
"size": 827
},
{
"d_type": "file",
"name": "Courses.xlsx",
"size": 37894
},
{
"d_type": "file",
"name": "data.json",
"size": 356
},
{
"d_type": "file",
"name": "directoryTree.py",
"size": 452
},
{
"d_type": "file",
"name": "excelMani.py",
"size": 753
},
{
"d_type": "file",
"name": "exceltojson.py",
"size": 699
},
{
"d_type": "file",
"name": "filesystem.json",
"size": 0
},
{
"d_type": "file",
"name": "guess.py",
"size": 621
},
{
"d_type": "file",
"name": "hello.py",
"size": 107
},
{
"d_type": "file",
"name": "imtools.py",
"size": 115
},
{
"d_type": "file",
"name": "input.py",
"size": 184
},
{
"d_type": "file",
"name": "jsonFileOut.py",
"size": 705
},
{
"d_type": "file",
"name": "listFilesAndDirectories.py",
"size": 521
},
{
"d_type": "file",
"name": "memoryGame.py",
"size": 9368
},
{
"d_type": "file",
"name": "os.py",
"size": 156
},
{
"d_type": "file",
"name": "osout.py",
"size": 185
},
{
"d_type": "file",
"name": "output.json",
"size": 28816
},
{
"d_type": "file",
"name": "output.txt",
"size": 42385
},
{
"d_type": "file",
"name": "pygameDrawing.py",
"size": 1001
},
{
"d_type": "file",
"name": "pygameHello.py",
"size": 304
},
{
"d_type": "file",
"name": "rsa.py",
"size": 198
},
{
"d_type": "directory",
"name": "simplejson-3.6.5",
"size": 578,
"subdirectory": [
{
"d_type": "file",
"name": ".DS_Store",
"size": 12292
},
{
"d_type": "directory",
"name": "build",
"size": 170,
"subdirectory": [
{
"d_type": "directory",
"name": "bdist.macosx-10.9-intel",
"size": 68,
"subdirectory": []
},
{
"d_type": "directory",
"name": "lib.macosx-10.9-intel-2.7",
"size": 102,
"subdirectory": [
{
"d_type": "directory",
"name": "simplejson",
"size": 374,
"subdirectory": [
{
"d_type": "file",
"name": "__init__.py",
"size": 23137
},
{
"d_type": "file",
"name": "_speedups.so",
"size": 92976
},
{
"d_type": "file",
"name": "compat.py",
"size": 1036
},
{
"d_type": "file",
"name": "decoder.py",
"size": 14721
},
{
"d_type": "file",
"name": "encoder.py",
"size": 25280
},
{
"d_type": "file",
"name": "ordered_dict.py",
"size": 3370
},
{
"d_type": "file",
"name": "scanner.py",
"size": 4653
},
{
"d_type": "directory",
"name": "tests",
"size": 986,
"subdirectory": [
{
"d_type": "file",
"name": "__init__.py",
"size": 3029
},
{
"d_type": "file",
"name": "test_bigint_as_string.py",
"size": 2238
},
{
"d_type": "file",
"name": "test_bitsize_int_as_string.py",
"size": 2297
},
{
"d_type": "file",
"name": "test_check_circular.py",
"size": 917
},
{
"d_type": "file",
"name": "test_decimal.py",
"size": 2544
},
{
"d_type": "file",
"name": "test_decode.py",
"size": 4021
},
{
"d_type": "file",
"name": "test_default.py",
"size": 221
},
{
"d_type": "file",
"name": "test_dump.py",
"size": 4999
},
{
"d_type": "file",
"name": "test_encode_basestring_ascii.py",
"size": 2337
},
{
"d_type": "file",
"name": "test_encode_for_html.py",
"size": 1022
},
{
"d_type": "file",
"name": "test_errors.py",
"size": 1549
},
{
"d_type": "file",
"name": "test_fail.py",
"size": 6346
},
{
"d_type": "file",
"name": "test_float.py",
"size": 1430
},
{
"d_type": "file",
"name": "test_for_json.py",
"size": 2767
},
{
"d_type": "file",
"name": "test_indent.py",
"size": 2568
},
{
"d_type": "file",
"name": "test_item_sort_key.py",
"size": 1127
},
{
"d_type": "file",
"name": "test_namedtuple.py",
"size": 4004
},
{
"d_type": "file",
"name": "test_pass1.py",
"size": 1746
},
{
"d_type": "file",
"name": "test_pass2.py",
"size": 386
},
{
"d_type": "file",
"name": "test_pass3.py",
"size": 482
},
{
"d_type": "file",
"name": "test_recursion.py",
"size": 1679
},
{
"d_type": "file",
"name": "test_scanstring.py",
"size": 7311
},
{
"d_type": "file",
"name": "test_separators.py",
"size": 942
},
{
"d_type": "file",
"name": "test_speedups.py",
"size": 1011
},
{
"d_type": "file",
"name": "test_tool.py",
"size": 2853
},
{
"d_type": "file",
"name": "test_tuple.py",
"size": 1922
},
{
"d_type": "file",
"name": "test_unicode.py",
"size": 6996
}
]
},
{
"d_type": "file",
"name": "tool.py",
"size": 1136
}
]
}
]
},
{
"d_type": "directory",
"name": "temp.macosx-10.9-intel-2.7",
"size": 102,
"subdirectory": [
{
"d_type": "directory",
"name": "simplejson",
"size": 102,
"subdirectory": [
{
"d_type": "file",
"name": "_speedups.o",
"size": 268464
}
]
}
]
}
]
},
{
"d_type": "file",
"name": "CHANGES.txt",
"size": 17448
},
{
"d_type": "file",
"name": "conf.py",
"size": 5681
},
{
"d_type": "directory",
"name": "dist",
"size": 102,
"subdirectory": [
{
"d_type": "file",
"name": "simplejson-3.6.5-py2.7-macosx-10.9-intel.egg",
"size": 143523
}
]
},
{
"d_type": "file",
"name": "index.rst",
"size": 38920
},
{
"d_type": "file",
"name": "LICENSE.txt",
"size": 10375
},
{
"d_type": "file",
"name": "MANIFEST.in",
"size": 82
},
{
"d_type": "file",
"name": "PKG-INFO",
"size": 2735
},
{
"d_type": "file",
"name": "README.rst",
"size": 1412
},
{
"d_type": "directory",
"name": "scripts",
"size": 102,
"subdirectory": [
{
"d_type": "file",
"name": "make_docs.py",
"size": 420
}
]
},
{
"d_type": "file",
"name": "setup.cfg",
"size": 59
},
{
"d_type": "file",
"name": "setup.py",
"size": 3811
},
{
"d_type": "directory",
"name": "simplejson",
"size": 408,
"subdirectory": [
{
"d_type": "file",
"name": ".DS_Store",
"size": 6148
},
{
"d_type": "file",
"name": "__init__.py",
"size": 23137
},
{
"d_type": "file",
"name": "_speedups.c",
"size": 106976
},
{
"d_type": "file",
"name": "compat.py",
"size": 1036
},
{
"d_type": "file",
"name": "decoder.py",
"size": 14721
},
{
"d_type": "file",
"name": "encoder.py",
"size": 25280
},
{
"d_type": "file",
"name": "ordered_dict.py",
"size": 3370
},
{
"d_type": "file",
"name": "scanner.py",
"size": 4653
},
{
"d_type": "directory",
"name": "tests",
"size": 1020,
"subdirectory": [
{
"d_type": "file",
"name": ".DS_Store",
"size": 12292
},
{
"d_type": "file",
"name": "__init__.py",
"size": 3029
},
{
"d_type": "file",
"name": "test_bigint_as_string.py",
"size": 2238
},
{
"d_type": "file",
"name": "test_bitsize_int_as_string.py",
"size": 2297
},
{
"d_type": "file",
"name": "test_check_circular.py",
"size": 917
},
{
"d_type": "file",
"name": "test_decimal.py",
"size": 2544
},
{
"d_type": "file",
"name": "test_decode.py",
"size": 4021
},
{
"d_type": "file",
"name": "test_default.py",
"size": 221
},
{
"d_type": "file",
"name": "test_dump.py",
"size": 4999
},
{
"d_type": "file",
"name": "test_encode_basestring_ascii.py",
"size": 2337
},
{
"d_type": "file",
"name": "test_encode_for_html.py",
"size": 1022
},
{
"d_type": "file",
"name": "test_errors.py",
"size": 1549
},
{
"d_type": "file",
"name": "test_fail.py",
"size": 6346
},
{
"d_type": "file",
"name": "test_float.py",
"size": 1430
},
{
"d_type": "file",
"name": "test_for_json.py",
"size": 2767
},
{
"d_type": "file",
"name": "test_indent.py",
"size": 2568
},
{
"d_type": "file",
"name": "test_item_sort_key.py",
"size": 1127
},
{
"d_type": "file",
"name": "test_namedtuple.py",
"size": 4004
},
{
"d_type": "file",
"name": "test_pass1.py",
"size": 1746
},
{
"d_type": "file",
"name": "test_pass2.py",
"size": 386
},
{
"d_type": "file",
"name": "test_pass3.py",
"size": 482
},
{
"d_type": "file",
"name": "test_recursion.py",
"size": 1679
},
{
"d_type": "file",
"name": "test_scanstring.py",
"size": 7311
},
{
"d_type": "file",
"name": "test_separators.py",
"size": 942
},
{
"d_type": "file",
"name": "test_speedups.py",
"size": 1011
},
{
"d_type": "file",
"name": "test_tool.py",
"size": 2853
},
{
"d_type": "file",
"name": "test_tuple.py",
"size": 1922
},
{
"d_type": "file",
"name": "test_unicode.py",
"size": 6996
}
]
},
{
"d_type": "file",
"name": "tool.py",
"size": 1136
}
]
},
{
"d_type": "directory",
"name": "simplejson.egg-info",
"size": 204,
"subdirectory": [
{
"d_type": "file",
"name": "dependency_links.txt",
"size": 1
},
{
"d_type": "file",
"name": "PKG-INFO",
"size": 2735
},
{
"d_type": "file",
"name": "SOURCES.txt",
"size": 1357
},
{
"d_type": "file",
"name": "top_level.txt",
"size": 11
}
]
}
]
},
{
"d_type": "file",
"name": "test.xlsx",
"size": 34682
},
{
"d_type": "directory",
"name": "xlrd-0.9.3",
"size": 340,
"subdirectory": [
{
"d_type": "file",
"name": ".DS_Store",
"size": 6148
},
{
"d_type": "directory",
"name": "build",
"size": 136,
"subdirectory": [
{
"d_type": "directory",
"name": "lib",
"size": 102,
"subdirectory": [
{
"d_type": "directory",
"name": "xlrd",
"size": 476,
"subdirectory": [
{
"d_type": "file",
"name": "__init__.py",
"size": 21105
},
{
"d_type": "file",
"name": "biffh.py",
"size": 17511
},
{
"d_type": "file",
"name": "book.py",
"size": 56979
},
{
"d_type": "file",
"name": "compdoc.py",
"size": 21235
},
{
"d_type": "file",
"name": "formatting.py",
"size": 45335
},
{
"d_type": "file",
"name": "formula.py",
"size": 94301
},
{
"d_type": "file",
"name": "info.py",
"size": 22
},
{
"d_type": "file",
"name": "licences.py",
"size": 3406
},
{
"d_type": "file",
"name": "sheet.py",
"size": 107386
},
{
"d_type": "file",
"name": "timemachine.py",
"size": 1773
},
{
"d_type": "file",
"name": "xldate.py",
"size": 7895
},
{
"d_type": "file",
"name": "xlsx.py",
"size": 31809
}
]
}
]
},
{
"d_type": "directory",
"name": "scripts-2.7",
"size": 102,
"subdirectory": [
{
"d_type": "file",
"name": "runxlrd.py",
"size": 16300
}
]
}
]
},
{
"d_type": "file",
"name": "PKG-INFO",
"size": 994
},
{
"d_type": "file",
"name": "README.html",
"size": 4672
},
{
"d_type": "directory",
"name": "scripts",
"size": 102,
"subdirectory": [
{
"d_type": "file",
"name": "runxlrd.py",
"size": 16304
}
]
},
{
"d_type": "file",
"name": "setup.py",
"size": 1887
},
{
"d_type": "directory",
"name": "tests",
"size": 918,
"subdirectory": [
{
"d_type": "file",
"name": "__init__.py",
"size": 0
},
{
"d_type": "file",
"name": "base.py",
"size": 118
},
{
"d_type": "file",
"name": "Formate.xls",
"size": 10752
},
{
"d_type": "file",
"name": "formula_test_names.xls",
"size": 7680
},
{
"d_type": "file",
"name": "formula_test_sjmachin.xls",
"size": 16896
},
{
"d_type": "file",
"name": "issue20.xls",
"size": 6144
},
{
"d_type": "file",
"name": "merged_cells.xlsx",
"size": 9275
},
{
"d_type": "file",
"name": "picture_in_cell.xls",
"size": 6656
},
{
"d_type": "file",
"name": "profiles.xls",
"size": 33792
},
{
"d_type": "file",
"name": "ragged.xls",
"size": 6656
},
{
"d_type": "file",
"name": "reveng1.xlsx",
"size": 16683
},
{
"d_type": "file",
"name": "test_biffh.py",
"size": 574
},
{
"d_type": "file",
"name": "test_cell.py",
"size": 2328
},
{
"d_type": "file",
"name": "test_comments_excel.xlsx",
"size": 15803
},
{
"d_type": "file",
"name": "test_comments_gdocs.xlsx",
"size": 4187
},
{
"d_type": "file",
"name": "test_formats.py",
"size": 3039
},
{
"d_type": "file",
"name": "test_formulas.py",
"size": 2323
},
{
"d_type": "file",
"name": "test_open_workbook.py",
"size": 1294
},
{
"d_type": "file",
"name": "test_sheet.py",
"size": 4238
},
{
"d_type": "file",
"name": "test_workbook.py",
"size": 1483
},
{
"d_type": "file",
"name": "test_xldate.py",
"size": 2384
},
{
"d_type": "file",
"name": "test_xldate_to_datetime.py",
"size": 6128
},
{
"d_type": "file",
"name": "test_xlsx_comments.py",
"size": 1517
},
{
"d_type": "file",
"name": "text_bar.xlsx",
"size": 8196
},
{
"d_type": "file",
"name": "xf_class.xls",
"size": 23040
}
]
},
{
"d_type": "directory",
"name": "xlrd",
"size": 850,
"subdirectory": [
{
"d_type": "file",
"name": "__init__.py",
"size": 21105
},
{
"d_type": "file",
"name": "__init__.pyc",
"size": 3397
},
{
"d_type": "file",
"name": "biffh.py",
"size": 17511
},
{
"d_type": "file",
"name": "biffh.pyc",
"size": 15336
},
{
"d_type": "file",
"name": "book.py",
"size": 56979
},
{
"d_type": "file",
"name": "book.pyc",
"size": 37567
},
{
"d_type": "file",
"name": "compdoc.py",
"size": 21235
},
{
"d_type": "file",
"name": "compdoc.pyc",
"size": 13729
},
{
"d_type": "file",
"name": "xldate.pyc",
"size": 5654
},
{
"d_type": "file",
"name": "xlsx.py",
"size": 31809
}
]
}
]
}
]
}
Related
Pan and Zoom Tree in Vega
Looking to be able to Pan and Zoom a tree diagram in Vega.
I have the example code working for my data but there is too much of it to be able to reasonably display in even a large chart. Therefore I am looking for a way to pan and zoom.
I have searched Google and SO, but there are zero posts about panning and zooming a tree.
Code used:
"$schema": "https://vega.github.io/schema/vega/v5.json",
"data": [
{
"name": "default",
"transform": [{
"type": "nest",
"generate": true,
"keys": ["$dimension1"]
},
{
"type": "tree",
"method": "tidy",
"size": [{"signal": "height"}, {"signal": "width - 20"}],
"separation": true,
"as": ["y", "x", "depth", "children"]
}
]
},
{
"name": "links",
"source": "default",
"transform": [
{ "type": "treelinks" },
{
"type": "linkpath",
"orient": "horizontal",
"shape": "diagonal"
}
]
}
],
"scales": [
{
"name": "color",
"type": "ordinal",
"domain": {"data": "default", "field": "depth"},
"range": {"scheme": "datastudio20"}
}
],
"marks": [
{
"type": "path",
"from": {"data": "links"},
"encode": {
"update": {
"path": {"field": "path"},
"stroke": {"value": "#ccc"}
}
}
},
{
"type": "symbol",
"from": {"data": "default"},
"encode": {
"enter": {
"size": {"value": 100},
"stroke": {"value": "#fff"}
},
"update": {
"x": {"field": "x"},
"y": {"field": "y"},
"fill": {"scale": "color", "field": "depth"}
}
}
},
{
"type": "text",
"from": {"data": "default"},
"encode": {
"enter": {
"text": {"signal":"datum.$dimension0 || datum.key || 'Customer'"},
"fontSize": {"value": 12},
"baseline": {"value": "middle"}
},
"update": {
"x": {"field": "x"},
"y": {"field": "y"},
"dx": {"signal": "datum.children ? -7 : 7"},
"align": {"signal": "datum.children ? 'right' : 'left'"},
"opacity": 1
}
}
}
]
}```
Here is an example for you:
Editor
{
"$schema": "https://vega.github.io/schema/vega/v5.json",
"description": "An example of Cartesian layouts for a node-link diagram of hierarchical data.",
"width": 600,
"height": 600,
"padding": 5,
"autosize": {"type": "none", "resize": false},
"signals": [
{
"name": "hover",
"on": [
{"events": "*:mouseover", "encode": "hover"},
{"events": "*:mouseout", "encode": "leave"},
{"events": "*:mousedown", "encode": "select"},
{"events": "*:mouseup", "encode": "release"}
]
},
{"name": "xrange", "update": "[0, width]"},
{"name": "yrange", "update": "[height, 0]"},
{
"name": "down",
"value": null,
"on": [{"events": "mousedown", "update": "xy()"}]
},
{
"name": "xcur",
"value": null,
"on": [{"events": "mousedown", "update": "slice(xdom)"}]
},
{
"name": "ycur",
"value": null,
"on": [{"events": "mousedown", "update": "slice(ydom)"}]
},
{
"name": "delta",
"value": [0, 0],
"on": [
{
"events": [
{
"source": "window",
"type": "mousemove",
"consume": true,
"between": [
{"type": "mousedown"},
{"source": "window", "type": "mouseup"}
]
}
],
"update": "down ? [down[0]-x(), y()-down[1]] : [0,0]"
}
]
},
{
"name": "anchor",
"value": [0, 0],
"on": [
{
"events": "wheel",
"update": "[invert('xscale', x()), invert('yscale', y())]"
}
]
},
{
"name": "zoom",
"value": 1,
"on": [
{
"events": "wheel!",
"force": true,
"update": "pow(1.001, event.deltaY * pow(16, event.deltaMode))"
}
]
},
{
"name": "xdom",
"update": "slice(xext)",
"on": [
{
"events": {"signal": "delta"},
"update": "[xcur[0] + span(xcur) * delta[0] / width, xcur[1] + span(xcur) * delta[0] / width]"
},
{
"events": {"signal": "zoom"},
"update": "[anchor[0] + (xdom[0] - anchor[0]) * zoom, anchor[0] + (xdom[1] - anchor[0]) * zoom]"
}
]
},
{
"name": "ydom",
"update": "slice(yext)",
"on": [
{
"events": {"signal": "delta"},
"update": "[ycur[0] + span(ycur) * delta[1] / height, ycur[1] + span(ycur) * delta[1] / height]"
},
{
"events": {"signal": "zoom"},
"update": "[anchor[1] + (ydom[0] - anchor[1]) * zoom, anchor[1] + (ydom[1] - anchor[1]) * zoom]"
}
]
},
{"name": "size", "update": "clamp(20 / span(xdom), 1, 1000)"}
],
"data": [
{
"name": "tree",
"url": "data/flare.json",
"transform": [
{"type": "stratify", "key": "id", "parentKey": "parent"},
{
"type": "tree",
"method": {"signal": "'tidy'"},
"size": [{"signal": "height"}, {"signal": "width+100"}],
"separation": {"signal": "true"},
"as": ["y", "x", "depth", "children"]
},
{"type": "extent", "field": "x", "signal": "xext"},
{"type": "extent", "field": "y", "signal": "yext"}
]
},
{
"name": "links",
"source": "tree",
"transform": [
{"type": "treelinks", "signal": "upstream"},
{
"type": "linkpath",
"orient": "horizontal",
"shape": {"signal": "'diagonal'"},
"sourceY": {"expr": "scale('yscale', datum.source.y)"},
"sourceX": {"expr": "scale('xscale', datum.source.x)"},
"targetY": {"expr": "scale('yscale', datum.target.y)"},
"targetX": {"expr": "scale('xscale', datum.target.x)"}
}
]
}
],
"scales": [
{
"name": "color",
"type": "linear",
"range": {"scheme": "magma"},
"domain": {"data": "tree", "field": "depth"},
"zero": true
},
{
"name": "xscale",
"zero": false,
"domain": {"signal": "xdom"},
"range": {"signal": "xrange"}
},
{
"name": "yscale",
"zero": false,
"domain": {"signal": "ydom"},
"range": {"signal": "yrange"}
}
],
"marks": [
{
"type": "path",
"from": {"data": "links"},
"encode": {
"update": {"path": {"field": "path"}, "stroke": {"value": "#ccc"}}
}
},
{
"type": "symbol",
"from": {"data": "tree"},
"clip": true,
"encode": {
"enter": {"size": {"value": 100}, "stroke": {"value": "#fff"}},
"update": {
"x": {"scale": "xscale", "field": "x"},
"y": {"scale": "yscale", "field": "y"},
"fill": {"scale": "color", "field": "depth"}
}
}
},
{
"type": "text",
"from": {"data": "tree"},
"clip": true,
"encode": {
"enter": {
"text": {"field": "name"},
"fontSize": {"value": 9},
"baseline": {"value": "middle"}
},
"update": {
"x": {"scale": "xscale", "field": "x"},
"y": {"scale": "yscale", "field": "y"},
"dx": {"signal": "datum.children ? -7 : 7"},
"align": {"signal": "datum.children ? 'right' : 'left'"},
"opacity": {"signal": "1"}
}
}
}
]
}
In Avro schema, how to wrap the array to an object and put it as a union instead of putting just array as union
I have the following data and I want to wrap the OPTIONAL array as an object and then put in as a union but since I am new to this, I am not sure how to do this.
This is how I have done so far so could someone help me correct the below structure in the expected output. Note that this dataRefs is an optional field and this entire structure may or may not be present.
{
"name": "dataRefs",
"default": null,
"type": ["null",
{
"type": "array",
"items": {
"name": "dataRef",
"type": "record",
"fields": [
{
"name": "dataId",
"type": "string",
"avro.java.string": "String"
},
{
"name": "email",
"type": ["null" ,"string"],
"avro.java.string": "String"
},
{
"name": "phone",
"type": ["null" ,"string"],
"avro.java.string": "String"
},
{
"name": "userName",
"type": ["null" ,"string"],
"avro.java.string": "String"
},
{
"name": "addressRef",
"default": null,
"type": ["null", {
"name": "addressRefRecord",
"type": "record",
"fields": [
{
"name": "addrRefId",
"type": ["null","string"],
"avro.java.string": "String"
},
{
"name": "addrType",
"type": ["null","string"],
"avro.java.string": "String"
},
{
"name": "addressLine1",
"type": ["null","string"],
"avro.java.string": "String"
},
{
"name": "addressLine2",
"type": ["null","string"],
"avro.java.string": "String"
},
{
"name": "city",
"type": ["null","string"],
"avro.java.string": "String"
},
{
"name": "province",
"type": ["null","string"],
"avro.java.string": "String"
},
{
"name": "country",
"type": ["null","string"],
"avro.java.string": "String"
},
{
"name": "postalCode",
"type": ["null","string"],
"avro.java.string": "String"
}
]
}
]
}
]
}
}
]
}
My JSON data that I intend to map to above schema looks like follows:
"dataRefs": [{
"addressRef": {
"addrRefId": "0",
"addrType": "ADDRESS",
"addressLine1": "DA 81",
"addressLine2": "",
"city": "Amsterdam",
"country": "Netherlands",
"postalCode": "xxxx LN",
"province": ""
},
"dataId": "0",
"email": "xyz#abc.com"
}],
This is how I was able to do so:
{
"name": "dataRefs",
"type": [
"null",
{
"type": "record",
"name": "dataRefsObject",
"fields": [
{
"name": "dataRefsArray",
"type": {
"type": "array",
"items": {
"name": "dataRef",
"type": "record",
"fields": [
{
"name": "dataId",
"type": ["null", "string"],
"avro.java.string": "String"
},
{
"name": "userName",
"type": [
"null",
"string"
],
"avro.java.string": "String"
},
....
Accessing item in array of json object
The following is my json response from one of my APIs. I am trying to print image URL on console. You will see response is list of requests with each request having an array of images (json objects)
[
{
"image": [
{
"_id": "5fbfdc75aea26e00484a787a",
"name": "WhatsApp Image 2020-10-23 at 11.55.25 AM.jpeg",
"alternativeText": "",
"caption": "",
"hash": "Whats_App_Image_2020_10_23_at_11_55_25_AM_773f08e059",
"ext": ".jpeg",
"mime": "image/jpeg",
"size": 50.67,
"width": 590,
"height": 576,
"url": "/uploads/Whats_App_Image_2020_10_23_at_11_55_25_AM_773f08e059.jpeg",
"formats": {
"thumbnail": {
"name": "thumbnail_WhatsApp Image 2020-10-23 at 11.55.25 AM.jpeg",
"hash": "thumbnail_Whats_App_Image_2020_10_23_at_11_55_25_AM_773f08e059",
"ext": ".jpeg",
"mime": "image/jpeg",
"width": 160,
"height": 156,
"size": 5.97,
"path": null,
"url": "/uploads/thumbnail_Whats_App_Image_2020_10_23_at_11_55_25_AM_773f08e059.jpeg"
},
"small": {
"name": "small_WhatsApp Image 2020-10-23 at 11.55.25 AM.jpeg",
"hash": "small_Whats_App_Image_2020_10_23_at_11_55_25_AM_773f08e059",
"ext": ".jpeg",
"mime": "image/jpeg",
"width": 500,
"height": 488,
"size": 41.95,
"path": null,
"url": "/uploads/small_Whats_App_Image_2020_10_23_at_11_55_25_AM_773f08e059.jpeg"
}
},
"provider": "local",
"related": [
"5fbfdc7eaea26e00484a787b"
],
"createdAt": "2020-11-26T16:48:53.715Z",
"updatedAt": "2020-11-26T16:49:02.626Z",
"__v": 0,
"id": "5fbfdc75aea26e00484a787a"
}
],
"_id": "5fbfdc7eaea26e00484a787b",
"title": "I want an center table",
"description": "I want a 3x3 center table with Indian marble top",
"createdAt": "2020-11-26T16:49:02.619Z",
"updatedAt": "2020-11-26T17:23:40.655Z",
"__v": 0,
"category": {
"_id": "5fbfe21d0bbf020065dd0b1e",
"category_name": "Furniture",
"createdAt": "2020-11-26T17:13:01.743Z",
"updatedAt": "2020-11-26T17:13:01.754Z",
"__v": 0,
"id": "5fbfe21d0bbf020065dd0b1e"
},
"id": "5fbfdc7eaea26e00484a787b"
},
{
"image": [
{
"_id": "5fc073aaf41381008738f53a",
"name": "1RK-Apartment-for-Sale-in-Nalasopara-East-Mumbai-at-Sai-Krupa-Chawl-Listing-Photo_Kitchen.jpeg",
"alternativeText": "",
"caption": "",
"hash": "1_RK_Apartment_for_Sale_in_Nalasopara_East_Mumbai_at_Sai_Krupa_Chawl_Listing_Photo_Kitchen_90475427e3",
"ext": ".jpeg",
"mime": "image/jpeg",
"size": 12.22,
"width": 300,
"height": 300,
"url": "/uploads/1_RK_Apartment_for_Sale_in_Nalasopara_East_Mumbai_at_Sai_Krupa_Chawl_Listing_Photo_Kitchen_90475427e3.jpeg",
"formats": {
"thumbnail": {
"name": "thumbnail_1RK-Apartment-for-Sale-in-Nalasopara-East-Mumbai-at-Sai-Krupa-Chawl-Listing-Photo_Kitchen.jpeg",
"hash": "thumbnail_1_RK_Apartment_for_Sale_in_Nalasopara_East_Mumbai_at_Sai_Krupa_Chawl_Listing_Photo_Kitchen_90475427e3",
"ext": ".jpeg",
"mime": "image/jpeg",
"width": 156,
"height": 156,
"size": 6.45,
"path": null,
"url": "/uploads/thumbnail_1_RK_Apartment_for_Sale_in_Nalasopara_East_Mumbai_at_Sai_Krupa_Chawl_Listing_Photo_Kitchen_90475427e3.jpeg"
}
},
"provider": "local",
"related": [
"5fc07407f41381008738f53c"
],
"createdAt": "2020-11-27T03:34:02.177Z",
"updatedAt": "2020-11-27T03:35:35.325Z",
"__v": 0,
"id": "5fc073aaf41381008738f53a"
},
{
"_id": "5fc073fdf41381008738f53b",
"name": "images.jpeg",
"alternativeText": "",
"caption": "",
"hash": "images_4ba1796688",
"ext": ".jpeg",
"mime": "image/jpeg",
"size": 9.55,
"width": 351,
"height": 144,
"url": "/uploads/images_4ba1796688.jpeg",
"formats": {
"thumbnail": {
"name": "thumbnail_images.jpeg",
"hash": "thumbnail_images_4ba1796688",
"ext": ".jpeg",
"mime": "image/jpeg",
"width": 245,
"height": 101,
"size": 6.44,
"path": null,
"url": "/uploads/thumbnail_images_4ba1796688.jpeg"
}
},
"provider": "local",
"related": [
"5fc07407f41381008738f53c"
],
"createdAt": "2020-11-27T03:35:25.211Z",
"updatedAt": "2020-11-27T03:35:35.325Z",
"__v": 0,
"id": "5fc073fdf41381008738f53b"
}
],
"_id": "5fc07407f41381008738f53c",
"title": "pest control के लिए",
"description": "हमारा घर 700 वर्गफुट का है",
"createdAt": "2020-11-27T03:35:35.313Z",
"updatedAt": "2020-11-27T03:36:20.592Z",
"__v": 0,
"category": {
"_id": "5fc07424f41381008738f53f",
"category_name": "Pest Control",
"createdAt": "2020-11-27T03:36:04.739Z",
"updatedAt": "2020-11-27T03:36:04.748Z",
"__v": 0,
"id": "5fc07424f41381008738f53f"
},
"id": "5fc07407f41381008738f53c"
}
]
I now want to show Image URL and referring it in following manner in React Native.
Following is React Native code for view
<View>
{isLoading ? <ActivityIndicator/> : (
<FlatList
data={data}
keyExtractor={({ id }, index) => id}
renderItem={ renderItem }
/>
)}
</View>
and renderItem is defined as following
function renderItem({ item }) {
console.log(item.image[0].name);
}
and I get following error
undefined is not an object (evaluating 'item.image[0]')
Can you point where am I going wrong?
You're not reading data properly. As you're using nested JSON object so you can access data using the following approaches:
For Single Image:
item[0].image[0].url
For all the Images:
item.forEach(function(value, key) {
value.image.map(image => console.log(image.url));
});
I have tried your code in react native expo snack it's working fine Expo Snack Link
angular ui tree for complex json
How to create angular ui tree for complex json?
[
{
"name": "bmw",
"model": "x3",
"specs": [
{
"name": "bmw",
"maxspeed": 350,
"minspeed": 0,
"model": "x3",
"type": "car"
},
{
"value": 30,
"name": "bmw",
"maxspeed": 350,
"minspeed": 0,
"model": "x3",
"type": "car"
}
],
"available": true,
"price": 50
},
{
"name": "audi",
"model": "r8",
"specs": [
{
"color": [
{
"value": "red",
"price": 100
},
{
"value": [
"white",
"black"
],
"price": 200
}
],
"maxspeed": 330,
"minspeed": 0,
"type": "car"
},
{
"color": [
{
"value": "blue",
"price": 300
},
{
"value": [
"yellow",
"gold"
],
"price": 500
}
],
"maxspeed": 330,
"minspeed": 0,
"type": "car"
}
],
"available": true
}
]
elasticsearch ngrams results are wrong
I am trying to implement partial matching using ngrams in elasticsearch but not getting the expected results out of it.
I am following this link:-
https://www.elastic.co/guide/en/elasticsearch/guide/current/_index_time_search_as_you_type.html
I have done all the things which are mentioned in this link. My dataset contains 3 fields i.e id,name,age.
Here is my mapping and setting of my_index
GET /my_index/_settings
{
"my_index": {
"settings": {
"index": {
"creation_date": "1433249154544",
"uuid": "hKxHVnqaRVmji31xK92pVA",
"number_of_replicas": "1",
"analysis": {
"filter": {
"autocomplete_filter": {
"type": "edge_ngram",
"min_gram": "1",
"max_gram": "20"
}
},
"analyzer": {
"autocomplete": {
"type": "custom",
"filter": [
"lowercase",
"autocomplete_filter"
],
"tokenizer": "standard"
}
}
},
"number_of_shards": "1",
"version": {
"created": "1040499"
}
}
}
}
}
GET /my_index/_mapping/my_type
{
"my_index": {
"mappings": {
"my_type": {
"properties": {
"#timestamp": {
"type": "date",
"format": "dateOptionalTime"
},
"#version": {
"type": "string"
},
"age": {
"type": "long"
},
"host": {
"type": "string"
},
"id": {
"type": "string"
},
"message": {
"type": "string"
},
"name": {
"type": "string",
"index_analyzer": "autocomplete",
"search_analyzer": "standard"
},
"path": {
"type": "string"
},
"type": {
"type": "string"
}
}
}
}
}
}
My query to elastic search is :-
GET /my_index/my_type/_search
{
"query": {
"match": {
"name": {
"query": "raman r"
}
}
}
}
According to me now the results should display only "raman ram" but it is also showing other results as well:-
{
"took": 13,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"hits": {
"total": 9,
"max_score": 2.6631343,
"hits": [
{
"_index": "my_index",
"_type": "my_type",
"_id": "2",
"_score": 2.6631343,
"_source": {
"message": [
"2,raman,23"
],
"#version": "1",
"#timestamp": "2015-06-02T13:07:18.041Z",
"type": "my_type",
"host": "shubham-VirtualBox",
"path": "/home/shubham/sample.csv",
"id": "2",
"name": "raman",
"age": 23
}
},
{
"_index": "my_index",
"_type": "my_type",
"_id": "10",
"_score": 1.8003473,
"_source": {
"message": [
"10,raman ram,43"
],
"#version": "1",
"#timestamp": "2015-06-02T13:11:03.455Z",
"type": "my_type",
"host": "shubham-VirtualBox",
"path": "/home/shubham/sample.csv",
"id": "10",
"name": "raman ram",
"age": 43
}
},
{
"_index": "my_index",
"_type": "my_type",
"_id": "1",
"_score": 0.26245093,
"_source": {
"message": [
"1,Ram,342"
],
"#version": "1",
"#timestamp": "2015-06-02T13:07:18.040Z",
"type": "my_type",
"host": "shubham-VirtualBox",
"path": "/home/shubham/sample.csv",
"id": "1",
"name": "Ram",
"age": 342
}
},
{
"_index": "my_index",
"_type": "my_type",
"_id": "3",
"_score": 0.26245093,
"_source": {
"message": [
"3,ramayan,23"
],
"#version": "1",
"#timestamp": "2015-06-02T13:07:18.041Z",
"type": "my_type",
"host": "shubham-VirtualBox",
"path": "/home/shubham/sample.csv",
"id": "3",
"name": "ramayan",
"age": 23
}
},
{
"_index": "my_index",
"_type": "my_type",
"_id": "4",
"_score": 0.26245093,
"_source": {
"message": [
"4,ramaram,231"
],
"#version": "1",
"#timestamp": "2015-06-02T13:07:18.041Z",
"type": "my_type",
"host": "shubham-VirtualBox",
"path": "/home/shubham/sample.csv",
"id": "4",
"name": "ramaram",
"age": 231
}
},
{
"_index": "my_index",
"_type": "my_type",
"_id": "5",
"_score": 0.26245093,
"_source": {
"message": [
"5,rampy,1"
],
"#version": "1",
"#timestamp": "2015-06-02T13:07:18.041Z",
"type": "my_type",
"host": "shubham-VirtualBox",
"path": "/home/shubham/sample.csv",
"id": "5",
"name": "rampy",
"age": 1
}
},
{
"_index": "my_index",
"_type": "my_type",
"_id": "6",
"_score": 0.26245093,
"_source": {
"message": [
"6,ration,11"
],
"#version": "1",
"#timestamp": "2015-06-02T13:07:18.041Z",
"type": "my_type",
"host": "shubham-VirtualBox",
"path": "/home/shubham/sample.csv",
"id": "6",
"name": "ration",
"age": 11
}
},
{
"_index": "my_index",
"_type": "my_type",
"_id": "7",
"_score": 0.26245093,
"_source": {
"message": [
"7,rita,42"
],
"#version": "1",
"#timestamp": "2015-06-02T13:07:18.042Z",
"type": "my_type",
"host": "shubham-VirtualBox",
"path": "/home/shubham/sample.csv",
"id": "7",
"name": "rita",
"age": 42
}
},
{
"_index": "my_index",
"_type": "my_type",
"_id": "8",
"_score": 0.26245093,
"_source": {
"message": [
"8,roni,45"
],
"#version": "1",
"#timestamp": "2015-06-02T13:07:18.050Z",
"type": "my_type",
"host": "shubham-VirtualBox",
"path": "/home/shubham/sample.csv",
"id": "8",
"name": "roni",
"age": 45
}
}
]
}
}
I get correct results when i use "minimum_should_match": "100%" while querying.
GET /my_index/my_type/_search
{
"query": {
"match": {
"name": {
"query": "raman r",
"minimum_should_match": "100%"
}
}
}
}
Gave me better result although ranking is not right:
{
"took": 4,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 2.6631343,
"hits": [
{
"_index": "my_index",
"_type": "my_type",
"_id": "2",
"_score": 2.6631343,
"_source": {
"message": [
"2,raman,23"
],
"#version": "1",
"#timestamp": "2015-06-02T13:07:18.041Z",
"type": "my_type",
"host": "shubham-VirtualBox",
"path": "/home/shubham/sample.csv",
"id": "2",
"name": "raman",
"age": 23
}
},
{
"_index": "my_index",
"_type": "my_type",
"_id": "10",
"_score": 1.8003473,
"_source": {
"message": [
"10,raman ram,43"
],
"#version": "1",
"#timestamp": "2015-06-02T13:11:03.455Z",
"type": "my_type",
"host": "shubham-VirtualBox",
"path": "/home/shubham/sample.csv",
"id": "10",
"name": "raman ram",
"age": 43
}
}
]
}
}
Don't know whether this approach is correct or not but do tell me if any alternative is there for this