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3 |
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69 |
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48 |
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23 |
6 |
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74% |
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23 |
6 |
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74% |
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3 |
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20 |
10 |
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34 |
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3 |
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3 |
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38 |
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0 |
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10 |
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38 |
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44 |
30 |
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32% |
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119 |
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10 |
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28 |
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3 |
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38 |
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5 |
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9 |
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3 |
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33 |
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265 |
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2 |
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8 |
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276 |
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64 |
42 |
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0 |
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72 |
40 |
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39 |
22 |
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179 |
139 |
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42 |
18 |
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57% |
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61 |
32 |
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6 |
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384 |
308 |
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106 |
61 |
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42% |
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19 |
12 |
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37% |
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256 |
248 |
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3% |
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27 |
20 |
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26% |
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68 |
54 |
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1 |
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0 |
100% |
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79 |
34 |
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5 |
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50 |
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75 |
49 |
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207 |
157 |
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| /usr/local/lib/python2.7/dist-packages/keyring/backends/OS_X |
70 |
49 |
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30% |
| /usr/local/lib/python2.7/dist-packages/keyring/backends/SecretService |
54 |
32 |
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41% |
| /usr/local/lib/python2.7/dist-packages/keyring/backends/Windows |
125 |
68 |
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46% |
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0 |
0 |
0 |
100% |
| /usr/local/lib/python2.7/dist-packages/keyring/backends/_win_crypto |
41 |
39 |
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5% |
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156 |
105 |
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54 |
29 |
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69 |
45 |
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41 |
29 |
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142 |
102 |
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28% |
| /usr/local/lib/python2.7/dist-packages/keyring/core |
84 |
39 |
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54% |
| /usr/local/lib/python2.7/dist-packages/keyring/credentials |
30 |
14 |
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53% |
| /usr/local/lib/python2.7/dist-packages/keyring/errors |
22 |
1 |
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95% |
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6 |
3 |
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50% |
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29 |
9 |
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69% |
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14 |
0 |
0 |
100% |
| /usr/local/lib/python2.7/dist-packages/keyring/util/__init__ |
18 |
2 |
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89% |
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29 |
16 |
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45% |
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23 |
8 |
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65% |
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13 |
3 |
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77% |
| /usr/local/lib/python2.7/dist-packages/netaddr/__init__ |
18 |
1 |
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94% |
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44 |
23 |
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48% |
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80 |
48 |
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40% |
| /usr/local/lib/python2.7/dist-packages/netaddr/eui/__init__ |
327 |
251 |
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23% |
| /usr/local/lib/python2.7/dist-packages/netaddr/ip/__init__ |
853 |
647 |
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24% |
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136 |
117 |
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270 |
205 |
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| /usr/local/lib/python2.7/dist-packages/netaddr/ip/nmap |
51 |
43 |
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16% |
| /usr/local/lib/python2.7/dist-packages/netaddr/ip/rfc1924 |
26 |
16 |
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38% |
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225 |
180 |
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| /usr/local/lib/python2.7/dist-packages/netaddr/strategy/__init__ |
113 |
90 |
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| /usr/local/lib/python2.7/dist-packages/netaddr/strategy/eui48 |
132 |
70 |
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47% |
| /usr/local/lib/python2.7/dist-packages/netaddr/strategy/eui64 |
72 |
40 |
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44% |
| /usr/local/lib/python2.7/dist-packages/netaddr/strategy/ipv4 |
108 |
54 |
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50% |
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106 |
47 |
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56% |
| /usr/local/lib/python2.7/dist-packages/networkx/__init__ |
31 |
2 |
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94% |
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48 |
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100% |
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5 |
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100% |
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40 |
33 |
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18% |
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62 |
49 |
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66 |
53 |
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21 |
16 |
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24% |
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46 |
38 |
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17% |
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6 |
0 |
0 |
100% |
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81 |
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54 |
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57 |
43 |
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112 |
102 |
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9% |
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20 |
14 |
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30% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/bipartite/spectral |
32 |
26 |
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19% |
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41 |
36 |
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17 |
13 |
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24% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/centrality/__init__ |
20 |
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100% |
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149 |
134 |
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83 |
71 |
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14% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/centrality/closeness |
24 |
19 |
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21% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/centrality/communicability_alg |
115 |
92 |
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20% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/centrality/current_flow_betweenness |
114 |
105 |
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8% |
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74 |
66 |
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11% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/centrality/current_flow_closeness |
46 |
39 |
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15% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/centrality/degree_alg |
22 |
16 |
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27% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/centrality/eigenvector |
53 |
47 |
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11% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/centrality/flow_matrix |
96 |
73 |
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24% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/centrality/katz |
72 |
63 |
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13% |
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82 |
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10% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/chordal/__init__ |
1 |
0 |
0 |
100% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/chordal/chordal_alg |
117 |
101 |
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14% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/clique |
242 |
226 |
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7% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/cluster |
95 |
82 |
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14% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/community/__init__ |
1 |
0 |
0 |
100% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/community/kclique |
29 |
23 |
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21% |
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5 |
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0 |
100% |
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22 |
14 |
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36% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/components/biconnected |
74 |
63 |
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15% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/components/connected |
34 |
25 |
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26% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/components/strongly_connected |
126 |
81 |
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36% |
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47 |
38 |
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19% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/connectivity/__init__ |
2 |
0 |
0 |
100% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/connectivity/connectivity |
157 |
142 |
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10% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/connectivity/cuts |
100 |
89 |
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11% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/core |
63 |
54 |
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14% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/cycles |
123 |
86 |
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30% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/dag |
95 |
85 |
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11% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/distance_measures |
43 |
34 |
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21% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/distance_regular |
44 |
38 |
0 |
14% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/euler |
37 |
31 |
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16% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/flow/__init__ |
2 |
0 |
0 |
100% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/flow/maxflow |
94 |
83 |
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12% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/flow/mincost |
208 |
194 |
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7% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/graphical |
152 |
138 |
0 |
9% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/hierarchy |
9 |
4 |
0 |
56% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/isolate |
8 |
2 |
0 |
75% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/isomorphism/__init__ |
3 |
0 |
0 |
100% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/isomorphism/isomorph |
50 |
39 |
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22% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/isomorphism/isomorphvf2 |
353 |
326 |
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8% |
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133 |
100 |
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25% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/isomorphism/vf2userfunc |
50 |
36 |
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28% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/link_analysis/__init__ |
2 |
0 |
0 |
100% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/link_analysis/hits_alg |
114 |
104 |
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9% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/link_analysis/pagerank_alg |
132 |
123 |
0 |
7% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/matching |
405 |
400 |
0 |
1% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/mis |
21 |
15 |
0 |
29% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/mst |
55 |
46 |
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16% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/operators/__init__ |
4 |
0 |
0 |
100% |
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33 |
23 |
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116 |
91 |
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22% |
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118 |
102 |
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14% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/operators/unary |
16 |
10 |
0 |
38% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/richclub |
34 |
29 |
0 |
15% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/shortest_paths/__init__ |
5 |
0 |
0 |
100% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/shortest_paths/astar |
43 |
35 |
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19% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/shortest_paths/dense |
46 |
38 |
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17% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/shortest_paths/generic |
92 |
83 |
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10% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/shortest_paths/unweighted |
116 |
105 |
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9% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/shortest_paths/weighted |
208 |
191 |
0 |
8% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/simple_paths |
56 |
50 |
0 |
11% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/smetric |
5 |
3 |
0 |
40% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/swap |
82 |
74 |
0 |
10% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/traversal/__init__ |
4 |
0 |
0 |
100% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/traversal/breadth_first_search |
32 |
24 |
0 |
25% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/traversal/depth_first_search |
69 |
58 |
0 |
16% |
| /usr/local/lib/python2.7/dist-packages/networkx/algorithms/vitality |
29 |
24 |
0 |
17% |
| /usr/local/lib/python2.7/dist-packages/networkx/classes/__init__ |
5 |
0 |
0 |
100% |
| /usr/local/lib/python2.7/dist-packages/networkx/classes/digraph |
267 |
145 |
0 |
46% |
| /usr/local/lib/python2.7/dist-packages/networkx/classes/function |
104 |
53 |
0 |
49% |
| /usr/local/lib/python2.7/dist-packages/networkx/classes/graph |
302 |
218 |
0 |
28% |
| /usr/local/lib/python2.7/dist-packages/networkx/classes/multidigraph |
173 |
149 |
0 |
14% |
| /usr/local/lib/python2.7/dist-packages/networkx/classes/multigraph |
182 |
161 |
0 |
12% |
| /usr/local/lib/python2.7/dist-packages/networkx/convert |
281 |
261 |
0 |
7% |
| /usr/local/lib/python2.7/dist-packages/networkx/drawing/__init__ |
15 |
3 |
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About
Lorem Ipsum
Events
View the underlying events
All configuration events Events for an aggregateDocs (DAOs)
List the docs (DAOs) that are loaded. The names will match the 'type's and 'interfaces's defined in the GraphQL schema
View all docsLorem Ipsum
View the underlying events
All configuration events Events for an aggregateList the docs (DAOs) that are loaded. The names will match the 'type's and 'interfaces's defined in the GraphQL schema
View all docs


An 3D arrow object for visualizing directions.
const dir = new THREE.Vector3( 1, 2, 0 );
//normalize the direction vector (convert to vector of length 1)
dir.normalize();
const origin = new THREE.Vector3( 0, 0, 0 );
const length = 1;
const hex = 0xffff00;
const arrowHelper = new THREE.ArrowHelper( dir, origin, length, hex );
scene.add( arrowHelper );
[example:webgl_shadowmesh WebGL / shadowmesh]
[page:Vector3 dir] -- direction from origin. Must be a unit vector.
[page:Vector3 origin] -- Point at which the arrow starts.
[page:Number length] -- length of the arrow. Default is `1`.
[page:Number hex] -- hexadecimal value to define color. Default is
0xffff00.
[page:Number headLength] -- The length of the head of the arrow. Default
is `0.2` * length.
[page:Number headWidth] -- The width of the head of the arrow. Default is
`0.2` * headLength.
See the base [page:Object3D] class for common properties.
Contains the line part of the arrowHelper.
Contains the cone part of the arrowHelper.
See the base [page:Object3D] class for common methods.
color -- The desired color.
Sets the color of the arrowHelper.
length -- The desired length.
headLength -- The length of the head of the arrow.
headWidth -- The width of the head of the arrow.
Sets the length of the arrowhelper.
dir -- The desired direction. Must be a unit vector.
Sets the direction of the arrowhelper.
Frees the GPU-related resources allocated by this instance. Call this method whenever this instance is no longer used in your app.
[link:https://github.com/mrdoob/three.js/blob/master/src/[path].js src/[path].js]


{% extends "app/layout3.html" %} {% block content %} THE REQUEST HAS BEEN CLOSED SUCCESSFULLY ! {% endblock %}


Reactor Kafka is a reactive API for Apache Kafka based on Project Reactor. Reactor Kafka API enables messages to be published to Kafka topics and consumed from Kafka topics using functional APIs with non-blocking back-pressure and very low overheads. This enables applications using Reactor to use Kafka as a message bus or streaming platform and integrate with other systems to provide an end-to-end reactive pipeline.
sender.send(Flux.range(1, 10).map(i -> SenderRecord.create(new ProducerRecord<>(topic, i, "Message_" + i)), i)) .doOnNext(r -> log.debug("Message #{} result: {}", r.correlationMetadata(), r.recordMetadata())) .subscribe();
receiver.receive() .subscribe(r -> { log.info("Received message {} ", r); r.receiverOffset().acknowledge(); });
More detailed documentation is available in the Reference Guide.


fun ResolveNameOperator(str: String?): EnumOperatorToken?
resolves the name of the operator by the typed operator


Step through time to see how New Horizons and others changed our view of Pluto.
View on date...
Show atlas view
Rotate globe
Increase light
Hide markers
Pluto as seen by New Horizons
Textures courtesy of Björn Jónsson, Marc W. Buie (Southwest Research Institute), and NASA.
Use the ← and → keys to step through time.


pyfor.collection module¶
-
class
pyfor.collection.
CloudDataFrame
(*args, **kwargs)¶ Bases:
geopandas.geodataframe.GeoDataFrame
Implements a data frame structure for processing and managing multiple
Cloud
objects. It is recommended to initialize using thefrom_dir()
function.-
__init__
(*args, **kwargs)¶ Initialize self. See help(type(self)) for accurate signature.
-
property
bounding_box
¶ - Returns
A tuple (minx, miny, maxx, maxy) of the bounding box for the entire collection.
-
create_index
()¶ For each file in the collection, creates .lax files for spatial indexing using the default values.
-
property
indexed
¶ - Returns
True if all files have an equivalent .lax present, otherwise False.
-
map_poly
(las_path, polygon)¶
-
par_apply
(func, indexed=True, by_file=False)¶ Apply a function to the collection in parallel. There are two major use cases:
1. Buffered Tiles: In the case of buffered tiles, the func argument should contain a function that takes two arguments, the first being an aggregated cloud.Cloud object, and the second being a shapely.geometry.Polygon that describes the bounding box of the aggregated tile. For this case, set by_file to False (this is the default).
2. Raw Files: In the case of processing raw tiles in parallel, the func argument should contain a function that takes only one argument, the absolute file path to the tile at that iteration. For this case, set by_file to True.
- Parameters
func – A function used to process each tile or raw file (see above).
indexed – Determines if .lax files will be leveraged to reduce memory consumption.
by_file – Forces par_apply to operate on raw files only if True.
-
plot
(**kwargs)¶ Plots the bounding boxes of the Cloud objects.
- Parameters
**kwargs –
Keyword arguments to
geopandas.GeoDataFrame.plot()
.
-
plot_metrics
(heightbreak, index=None)¶ Retrieves a set of 29 standard metrics, including height percentiles and other summaries. Intended for use on plot-level point clouds. :param index: An iterable of indices to set as the output dataframe index. :return: A pandas dataframe of standard metrics.
-
reset_tiles
()¶ Reset the tiles to describe the bounding boxes of each .las file in place.
-
retile_raster
(cell_size, target_tile_size, buffer=0)¶ A retiling operation that creates raster-compatible sized tiles. Important for creating project-level rasters. Changes self.tiles in place. Note that the target tile size is approximate, and is rounded to the neareast size that is compatible with the defined cell size.
- Parameters
cell_size – The target cell size of the output raster.
target_tile_size – The target tile size of the retiling operation.
buffer – The amount to buffer each input tile.
-
-
class
pyfor.collection.
Retiler
(cdf)¶ Bases:
object
-
__init__
(cdf)¶ Retiles a CloudDataFrame. Generally used to create tiles such that rasters generated from tiles are properly aligned.
-
retile_buffer
(buffer)¶ A simple buffering operation.
- Returns
A list of buffered shapely polygons.
-
retile_raster
(target_cell_size, original_tile_size, buffer=0)¶ Creates a retiling grid for a specified target cell size. This creates a list of polygons such that if a raster is constructed from a polygon it will exactly fit inside given the specified target cell size. Useful for creating project level rasters.
- Parameters
target_cell_size – The desired output cell size
original_tile_size – The original tile size of the project
buffer – The distance to buffer each new tile to prevent edge effects.
- Returns
A list of shapely polygons that correspond to the new grid.
-
-
pyfor.collection.
from_dir
(las_dir, **kwargs)¶ Constructs a CloudDataFrame from a directory of las files.
- Parameters
las_dir – The directory of las or .laz files.
glob_str – A glob string to select files from the directory. For example: “*.laz” to select .laz files only.
- Returns
A CloudDataFrame constructed from the directory of las files.


AWS Billing and Cost Management Budget CostTypes
The
CostTypes
property type specifies what costs, such as tax or subscriptions, are included in
a Billing and Cost Management budget.
CostTypes
is a property of the AWS Billing and Cost Management Budget BudgetData property type.
Syntax
To declare this entity in your AWS CloudFormation template, use the following syntax:
JSON
{ "IncludeSupport" :
Boolean
, "IncludeOtherSubscription" :Boolean
, "IncludeTax" :Boolean
, "IncludeSubscription" :Boolean
, "UseBlended" :Boolean
, "IncludeUpfront" :Boolean
, "IncludeDiscount" :Boolean
, "IncludeCredit" :Boolean
, "IncludeRecurring" :Boolean
, "UseAmortized" :Boolean
, "IncludeRefund" :Boolean
}
YAML
IncludeSupport:
Boolean
IncludeOtherSubscription:Boolean
IncludeTax:Boolean
IncludeSubscription:Boolean
UseBlended:Boolean
IncludeUpfront:Boolean
IncludeDiscount:Boolean
IncludeCredit:Boolean
IncludeRecurring:Boolean
UseAmortized:Boolean
IncludeRefund:Boolean
Properties
IncludeSupport
-
Specifies whether a budget includes support subscription fees.
Required: No
Type: Boolean
Update requires: No interruption
IncludeOtherSubscription
-
Specifies whether a budget includes non-RI subscription costs.
Required: No
Type: Boolean
Update requires: No interruption
IncludeTax
-
Specifies whether a budget includes taxes.
Required: No
Type: Boolean
Update requires: No interruption
IncludeSubscription
-
Specifies whether a budget includes subscriptions.
Required: No
Type: Boolean
Update requires: No interruption
UseBlended
-
Specifies whether a budget uses blended rate.
Required: No
Type: Boolean
Update requires: No interruption
IncludeUpfront
-
Specifies whether a budget includes upfront RI costs.
Required: No
Type: Boolean
Update requires: No interruption
IncludeDiscount
-
Specifies whether a budget includes discounts.
Required: No
Type: Boolean
Update requires: No interruption
IncludeCredit
-
Specifies whether a budget includes credits.
Required: No
Type: Boolean
Update requires: No interruption
IncludeRecurring
-
Specifies whether a budget includes recurring fees such as monthly RI fees.
Required: No
Type: Boolean
Update requires: No interruption
UseAmortized
-
Specifies whether a budget uses the amortized rate.
Required: No
Type: Boolean
Update requires: No interruption
IncludeRefund
-
Specifies whether a budget includes refunds.
Required: No
Type: Boolean
Update requires: No interruption


A class used to represent the FIND behaviour
of the robot
Attributes
-----
@param from_track: Variable that represent if the transition has been done from TRACK state
@type from_track: bool
Methods
-----
object_detector_client():
Makes a request to detector server and wait for the response
execute(userdata)
It launches the EXPLORE LITE package and checks for a ball.
If there is a ball, not previously detected, it changes the state to TRACK.
After some times ,if no ball is detected, it changes state to PLAY
Definition at line 251 of file command_manager.py.
| def command_manager.find.execute |
( |
|
self, |
|
|
|
userdata |
|
) |
| |
It launches the EXPLORE LITE package and checks for a ball.
If there is a ball, not previously detected, it changes the state to TRACK.
After some times ,if no ball is detected, it changes state to PLAY
@param userdata: used to pass data between states
@type userdata: list
@returns: transition value
@rtype: String
Definition at line 299 of file command_manager.py.
299 def execute(self, userdata):
302 It launches the EXPLORE LITE package and checks for a ball.
303 If there is a ball, not previously detected, it changes the state to TRACK.
304 After some times ,if no ball is detected, it changes state to PLAY
306 @param userdata: used to pass data between states
309 @returns: transition value
312 rospy.loginfo(
'Executing state FIND')
314 uuid = roslaunch.rlutil.get_or_generate_uuid(
None,
False)
315 roslaunch.configure_logging(uuid)
316 launch = roslaunch.parent.ROSLaunchParent(uuid, [path_exp])
318 start = rospy.get_time()
321 self.from_track = userdata.fromTrack
322 transition_time = random.randint(min_transition_find_play,max_transition_find_play)
325 resp = self.object_detector_client()
326 ball = resp.object.split()
327 center = float(ball[1])
328 radius = float(ball[2])
329 now = rospy.get_time()
330 if((self.from_track ==
True)
and (center == -1)
and (radius == -1)):
331 self.from_track =
False
332 elif ((self.from_track ==
False)
and (center != -1)
and (radius != -1)):
334 userdata.ballType = userdata.ballTypeFromPlay
336 if ((now-start) >= transition_time):


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Don't just tell us what you want, but explain us why you want it. Together, we identify key features and determine priorities based on business value and risk. Features with little value can be postponed or cut away. This way, we can come to a valuable solution faster and cheaper, and one that needs less features than initially you think you need.
We use short development cycles (sprints) of typically 2 weeks. Prior to the start of each cycle, you can reconsider the features and reshuffle their priority based on their current value for you. At the end of each cycle, we show you working software that is fully tested.
We work on a sprint-per-sprint basis. We go as far as you want us to go. After each sprint, you can evaluate whether enough value is realised to release the software or whether you are ready to take over the ownership.
Optionally, you can have your people participate in our team. They'll receive immersive training on our agile way-of-working and can confidently take over the software ownership after the project.
The Agile Software Builders team is passionate about building great software, delivering a quality software version fast.


Build a Scalable Application
Scalability of an application is equally important as its features and user interface. It becomes even more important if your app is going to serve more than a million users in the future. In this blog, you will learn how to scale your app up to 1 million users on AWS.
Suppose you’ve built a web application and started getting few customers. After some feedback and suggestions, you are ready with the full-fledged product. Now, your marketing team shares your app on product hunt to acquire new customers. Suddenly, thousands of visitors are using your app and at one point they are unable to use your app.
You’ve tested your app and it is working fine. So what happened?
“This is not a bug but a problem of scalability. Your cloud architecture is not designed to scale with increasing load.”
I’ve seen many companies which usually focus more on features and less on scalability. Creating applications that are both resilient and scalable is an essential part of any application architecture. In this blog, you will learn how to build highly scalable web application architecture that can scale with increasing load.
What is a Scalable Application?
Scalability refers to the ability of a system to give a reasonable performance under growing demands (This can be larger data-sets, higher request rates, the combination of size and velocity, etc). It should work well with 1 user or 1 million users and handles spikes in traffic automatically. By adding and removing the resources only when needed, scalable apps consume only the resources necessary to meet demand.
When talking about scalability in cloud computing, you will often hear about two main ways of scaling – horizontal or vertical. Let’s look deeper into these terms.
Vertical scaling (Scaling Up)
Scaling up or vertical scaling refers to resource maximization of a single unit to expand its ability to handle the increasing load. In hardware terms, this includes adding processing power and memory to the physical machine running the server. In software terms, scaling up may include optimizing algorithms and application code.
Horizontal scaling (Scaling Out)
Scaling out or horizontal scaling refers to resource increment by the addition of units to the app’s cloud architecture. This means adding more units of smaller capacity instead of adding a single unit of larger capacity. The requests for resources are then spread across multiple units thus reducing the excess load on a single machine.
Characteristics of the Scalable Application
What does scalability look like? There are some areas where an app needs to excel to be considered scalable.
Performance
First and foremost, the application must operate well under stress with low latency. The speed of a website affects usage and user satisfaction, as well as search engine rankings, a factor that directly correlates to revenue and retention. As a result, creating a scalable web application architecture that is optimized for fast responses and low latency is key.
Availability and Reliability
These are closely related and equally necessary. Scalable apps rarely if ever go down under stress. They need to reliably produce data upon request and not lose stored information.
Manageability
The manageability of the cloud architecture equates to the scalability of operations: maintenance and updates. Things to consider for manageability are the ease of diagnosing and understanding problems when they occur, ease of making updates or modifications, and how simple the system is to operate. (i.e., does it routinely operate without failure or exceptions?)
Cost
Highly scalable applications don’t have to be unreasonably expensive to build, maintain, or scale. Planning for scalability during development allows the app to expand as demand increases without causing undue expenses.
You’ve plenty of options to choose the cloud provider while building the high-performance web application architecture. The three leading cloud computing vendors, AWS, Microsoft Azure and Google Cloud, each have their own strengths and weaknesses that make them ideal for different use cases.
In this blog, I’ve chosen AWS to show you how to build web scalable application. AWS is a subsidiary of the renowned company, Amazon, it provides different services that are cloud-centered for various requirements. AWS holds the highest 33% market share of cloud computing. They provide excellent documentation on each of their services, helpful guides and white papers, reference architectures for common apps.
Steps to build a scalable application based on increasing users from 1 to 1 million
- Step1 : Initial Setup of Cloud Architecture
- Step2 : Create a Multiple Hosts and Choose Database
- Step3 : Store database on Amazon RDS
- Step4 : Create a multiple avialibility zones
- Step5 : Move Static content to Object-based Storage
- Step6 : Auto Scaling
- Step7 : Service Oriented Architecture(SOA)
1 User
### Initial Setup of Cloud Architecture
- Amazon Machine Images (AMI)
- Amazon EC2
- Amazon VPC
- Amazon Route 53
Amazon Machine Images (AMI)
Amazon Machine Image (AMI) gives the information required to launch an instance, which is a virtual server in the cloud. You can specify an AMI during the launch an instance. An AMI includes a template for the root volume for the instance, launch permissions that control which AWS accounts can use the AMI to launch instances and a block device mapping that specifies the volumes to attach to the instance when it’s launched.
Amazon Elastic Compute Cloud (Amazon EC2)
Amazon Elastic Compute Cloud provides the scalable computing capacity in the AWS cloud. This eliminates the hardware upfront so that you can develop and deploy applications faster.
Amazon Virtual Private Cloud (Amazon VPC)
Amazon Virtual Private Cloud gives a provision to launch AWS resources in a virtual network. It gives complete control over the virtual networking environment including a selection of IP address range, subnet creation, the configuration of route tables and network gateways.
Amazon Route 53
Amazon Route 53 is a highly available and scalable cloud DNS web service. Amazon Route 53 effectively connects user requests to infrastructure running in AWS – such as Amazon EC2 instances, Elastic Load Balancing load balancers or Amazon S3 buckets.
Here you need a bigger box. You can simply choose the larger instance type which is called vertical scaling. At the initial stage, vertical scaling is enough but we can’t scale vertically indefinitely. Eventually, you’ll hit the wall. Also, it doesn’t address failover and redundancy.
USERS > 10
Create multiple hosts and choose the database
First, you need to choose the database as users are increasing and generating data. It’s advisable to start with SQL Database first because of the following reasons:
- Established and well-worn technology.
- Community support and latest tools.
- We aren’t going to break SQL DBs in our first 10 million users.
Note, you can choose the NoSQL database if your users are going to generate a large volume of data in various forms.
At this stage, you have everything in a single bucket. This architecture is harder to scale and complex to manage in the long run. It’s time to introduce the multi-tier architecture to separate the database from the application.
USERS > 100
Store database on Amazon RDS to ease the operations
When users increase to 100, Database deployment is the first thing which needs to be done. There are two general directions to deploy a database on AWS. The foremost option is to use a managed database service such as Amazon Relational Database Service (Amazon RDS) or Amazon Dynamo DB and the second step is to host your own database software on Amazon EC2.
- Amazon RDS
- Amazon DynamoDB
Amazon RDS
Amazon Relational Database Service (Amazon RDS) makes it easy to set up, operate, and scale a relational database in the cloud. Amazon RDS provides six familiar database engines to choose from, including Amazon Aurora, Oracle, Microsoft SQL Server, PostgreSQL, MySQL and MariaDB.
User > 1000
Create multiple availability zones to improve availability
As per current architecture, you may face availability issues. If the host for your web app fails then it may go down. So you need another web instance in another Availability Zone where you will put the slave database to RDS.
- Elastic Load Balancer (ELB)
- Multi – AZ Deployment
Here you’ve to use Elastic Load Balancer (ELB) to balance the load between the two web host instances in the two AZs.
Elastic Load Balancer (ELB)
ELB distributes the incoming application traffic across EC2 instances. It is horizontally scaled, imposes no bandwidth limit, supports SSL termination, and performs health checks so that only healthy instances receive traffic.
This configuration has 2 instances behind the ELB. We can have 1000s of instances behind the ELB. This is Horizontal Scaling.
At this stage, you’ve multiple EC2 instances to serve thousands of users which ultimately increases your cloud cost. To reduce the cost, you have to optimize instances’ usage based on varying load.
Users: 10,000s – 100,000
Move static content to object-based storage for better performance
To improve performance and efficiency, you’ll need to add more read replicas to RDS. This will take load off the write master database. Furthermore, you can reduce the load from web servers by moving static content to Amazon S3 and Amazon CloudFront.
- Amazon S3
- Amazon CloudFront
- Amazon DynamoDB
- Amazon ElastiCache
Amazon S3
Amazon S3 is object-based storage. It is not attached to EC2 instance which makes it best suitable to store static content, like javascript, CSS, images, videos. It is designed for 99.999999999% of durability and can store multiple petabytes of data.
Amazon CloudFront
Amazon CloudFront is a Content Delivery Network(CDN). It retrieves data from Amazon S3 bucket and distributes it to multiple data center locations. It caches content at the edge locations to provide our users with the lowest latency access.
Furthermore, to reduce the load from database servers, you can use DynamoDB(managed NoSQL database) to store session state. For caching data from the database, you can use Amazon ElastiCache.
Amazon DynamoDB
Amazon DynamoDB is a fast and flexible NoSQL database service for applications that need consistent, single-digit millisecond latency. It is a completely managed cloud database and supports document and key-value store models.
Amazon ElastiCache
Amazon ElastiCache is a Caching-as-a-Service. It removes the complexity associated with deploying and managing a distributed cache environment. It’s a self-healing infrastructure if nodes fail new nodes are started automatically.
Users > 500,000
Setting up Auto Scaling to meet the varying demand automatically
At this stage, your architecture is quite complex to be managed by the small team and without proper monitoring, analyzing it’s difficult to move forward.
- Amazon CloudWatch
- AWS Elastic Beanstalk
- AWS OpsWorks
- AWS Cloud Formation
- AWS
Now that the web tier is much more lightweight, it’s time for Auto Scaling!
“Auto Scaling is nothing but an automatic resizing of compute clusters based on demand.”
Auto Scaling enables “just-in-time provisioning,” allowing users to scale infrastructure dynamically as load demands. It can launch or terminate EC2 instances automatically based on Spikes in Traffic. You pay only for the resources which are enough to handle the load.
For monitoring you can use the following AWS services:
Amazon CloudWatch
AWS CloudWatch provides a rich set of tools to monitor the health and resource utilization of various AWS services. The metrics collected by CloudWatch can be used to set up alarms, send notifications, and trigger actions upon alarms firing. Amazon EC2 sends metrics to CloudWatch that describe your Auto Scaling instances.
The autoscaling group can include multiple AZs, up to as many as are in the same region. Instances can pop up in multiple AZs not just for scalability, but for availability.
We need to add monitoring, metrics, and logging to optimize Auto Scaling efficiently.
- Host-level metrics. Look at a single CPU instance within an autoscaling group and figure out what’s going wrong.
- Aggregate level metrics. Look at metrics on the Elastic Load Balancer to understand the performance of the entire set of instances.
- Log analysis. Look at what the application is telling you using CloudWatch Logs. CloudTrail helps you analyze and manage logs. If you have set up region-specific configurations in CloudWatch, it is not easy to combine metrics from different regions within an AWS monitoring tool. In that case you can use Loggly, a log management tool. You can send logs and metrics from CloudWatch and CloudTrail to Loggly and unify these logs with other data for a better understanding of your infrastructure and applications.
Squeeze as much performance as you can from your configuration. Auto Scaling can help with that. You don’t want systems that are at 20% CPU utilization.
The infrastructure is getting big, it can scale to 1000s of instances. We have read replicas, we have horizontal scaling, but we need some automation to help manage it all, we don’t want to manage each individual instance. Here some automation tools:
AWS Elastic Beanstalk
AWS Elastic Beanstalk is a service that allows users to deploy code written in Java, .NET, PHP, Node.js, Python, Ruby, Go, and Docker on familiar servers such as Apache, NGINX, Passenger, and IIS.
AWS OpsWorks
AWS OpsWorks provides a unique approach to application management. Additionally, AWS OpsWorks auto-heals application stack, giving scaling based on time or workload demand and generates metrics to facilitate monitoring.
AWS Cloud Formation
AWS Cloud Formation provides resources using a template in JSON format. You have the option to choose from a collection of sample templates to get started on common tasks.
AWS CodeDeploy
AWS Code Deploy is a platform service for automating code deployment to Amazon EC2 instances and instances running on premises.
Users > 1 million
Use Service Oriented Architecture(SOA) for better flexibility
To serve more than 1 million users you need to use Service Oriented Architecture(SOA) while designing large scale web applications.
- Amazon Simple Queue Service (SQS)
- Amazon Simple Notification Service (SNS)
- AWS Lambda
In SOA, we need to separate each component from the respective tiers and create separate services. The individual services can then be scaled independently. Web and application tiers will have different resource requirements and different services. This gives you a lot of flexibility for scaling and high availability.
AWS provides a host of generic services to help you build SOA infrastructure quickly. They are:
Amazon Simple Queue Service (SQS)
It is a simple and cost-effective service to decouple and coordinate the components of a cloud application. Using SQS sending, storing, and receiving messages can be executed easily between software components of any size.
Amazon Simple Notification Service (SNS)
With SNS you can send messages to a large number of subscribers. The benefits are easy setup, smooth operation, and high reliability to send notifications to all endpoints.
AWS Lambda
AWS Lambda is a compute service that lets you run code without provisioning or managing servers. AWS Lambda executes your code only when needed and scales automatically, from a few requests per day to thousands per second. You pay only for the compute time you consume – there is no charge when your code is not running. You can also build serverless architecture composed of functions that are triggered by events.
Conclusion
The decision about how to approach scaling should be made up front because you never know when you are going to get popular! Also, crashing (or even just slow) pages leave your users unhappy and your app with a bad reputation. It ultimately affects your revenue.
Learning how to build scalable websites takes time, a lot of practice, and a good amount of money. For companies that have a need and struggling to get this done, We are your best option for such a project. We’ve created Scalable Web Apps for clients having millions of users. Contact us for a consultation and price quote.


Parking IOT
Inspired back the movement started in South Korea of using balloons to indicate unoccupied stalls
Visit Parking Lot


Blue: Temperature is below 32° F
Purple: Temperature is between 32 and 49° F
Yellow: Temperature is between 50 and 69° F
Orange: Temperature is between 70 and 89° F
Red: Temperature is above 90° F


This is an example of the visible directive. Like the ng-show standard directive, it can show/hide an element but using the visibility CSS property. Source code
Good morning, my name is Bond... James Bond.


--- title: Analytics ---
Open the browser debugger to see the exceptionHandler send a normalized message to the analytics server.
exceptionHandler
Create Error


Benchmark Research
When we began this project we (my team at Hak Studio) were hired as branding specialists, our client wanted a friendlier brand message than the leading ride-share companies at the time.
Looking at the existing marketplace, and brands which matched our client goals, we established brand and market guidelines with colours and images which matched the target consumers. We built each system from the ground up, keeping customer experience at the core of our efforts.
Our product had two user groups, Drivers and Ryders (passengers), which meant we built two apps and had two distinct user-journeys to start using InstaRyde.
"Driving in Toronto can be frustrating."
"There are no locally made ride-share companies in Canada."
"Ride-share drivers want a bigger cut of the profit."
"Ride-share drivers need to be able to use multiple platforms at once."
"There are no reward systems in place for ride-shares."
These points informed the company values and app functionality.


AirPlaineIcon
Paper Airplane free vector icons designed by Puppets. (n.d.). Retrieved April 10, 2018, from https://www.flaticon.com/free-icon/paper-airplane_68630/
Facebbok Icon
https://n6-img-fp.akamaized.net/free-icon/facebook-square-social-logo_318-74909.jpg?size=338&ext=jpg
Instagram Icon
https://image.flaticon.com/icons/svg/61/61164.svg
Jquery Download
https://n6-img-fp.akamaized.net/free-icon/facebook-square-social-logo_318-74909.jpg?size=338&ext=jpg
Smooth scroll down learned from:
https://www.w3schools.com/jquery/jquery_get_started.asp


College of Maritime Science and Management
Department of Merchant Marine
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College of Maritime Science and Management
Department of Shipping and Transportation Management
|
College of Engineering
Department of Systems Engineering and Naval Architecture
|
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College of Engineering
Department of Mechanical and Mechatronic Engineering
|
|
College of Engineering
Department of Harbor and River Engineering
|
|
|
College of Maritime Science and Management
Department of Transportation Science
|
|
College of Maritime Science and Management
Department of Marine Engineering
|
|
|
College of Electrical Engineering and Computer Science
Department of Electrical Engineering
|
|
College of Electrical Engineering and Computer Science
Department of Computer Science and Engineering
|
|
College of Electrical Engineering and Computer Science
Department of Communications, Navigation and Control Engineering
|
College of Ocean Science and Resource
Department of Environmental Biology and Fisheries Science
|
College of Ocean Science and Resource
Department of Marine Environmental Informatics
|
College of Life Sciences
Department of Food Science
|
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College of Life Sciences
Department of Aquaculture
|
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College of Life Sciences
Department of Bioscience and Biotechnology
|
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Tax Chart
| Item Name | Type of Item | Cost |
|---|---|---|
| Tires | Uber Expense | 400.95 |
| Hair Products | Cosmotology Expense | 359.56 |
| Gas | 18-Wheeler Company Truck | 150.00 |
| Lawncare Products | Rental House Expense | 199.99 |
| House Taxes | State Taxes | 2000.59 |
| Church Tithes | Church | 50.00 |
| Item Name | Type of Item | Cost |
|---|---|---|
| Tires | Uber Expense | 400.95 |
| Hair Products | Cosmotology Expense | 359.56 |
| Gas | 18-Wheeler Company Truck | 150.00 |
| Lawncare Products | Rental House Expense | 199.99 |
| House Taxes | State Taxes | 2000.59 |
| Church Tithes | Church | 50.00 |


AuToJo0m3R
AutoJoomer will help you join classes on time (IIIT Kottayam 2020). And here's the team who made it possible.
Vishal
Owner
Sumanth
Maintaner/Contributor
Rishabh
Contributor
Nikhil
Maintaner/Contributor
Abhijit
Contributor
Aditya
Maintaner/Contributor
Ashutosh
Contributor
Aditi
Contributor


FTSE: The Long And Short Of It - NXT, UTG, EMG, VSVS
Next CFD/DFT (NXT): Since breaking above key resistance around 6,225, prices rallied to 7,000 and are now gently retracing within a tight, corrective channel. Moreover, the 50-day eMA and 50% retracement level are nearby to act as potential support.
Bias remains bullish above 6,225, and for a retest and break of 7,000.
Bulls could look for a break of the retracement line or, for extra confirmation, wait for a break of a swing high such as 6,714.
Whilst yesterday’s bearish engulfing candle is a slight concern over the near-term, we’re looking for a base to build above 6,225 before its next leg high.
IWG is another equity consider as part of a similar setup, although price action on Next is more pleasing to the eye (being a higher liquidity stock).
Unite Group CFD/DFT (UTG): The equity is locked in a strong uptrend, outperformed the FTSE and is on the cusp of outperforming its sector this year. After reaching 1180, it confirmed support at 1100, consolidated and is now eyeing up the 1180 highs once more.
Due to the strength of the underlying trend, the bias is for prices to break to new record highs.
The monthly R1 sits right on the highs, so this makes a likely target for intraday traders.
If trading from the daily timeframe, traders may want to wait for a break of 1180 or wait for prices to consolidate/retrace before seeking long setups.
Man Group (EMG): Since the October peak, Man Group has shed just over 20% which places it within a technical bear market. The initial move towards 141 was relatively direct, only pausing slightly ahead of its break of the bullish trendline. Having retraced towards the 20-day eMA and stalling below 152.50 resistance, bearish momentum has returned and its on the cusp of breaking key support.
The trend remains bearish beneath 151.75.
A break of 140 brings the 128.35 low into focus for bears.
Given positive sentiment for stocks ahead of the open (from Kudlow’s trade headlines) then perhaps support will hold this session. Regardless, bearish momentum since October is far more aggressive then the bullish momentum seen between January and October, so a downside break remains the bias.
Vesuvius CFD/DFT (VSVS): Having pulled back to the 61.8% Fibonacci ratio, a swing high has formed and bearish momentum is returning. The peaks are timely and prices are accelerating away from the trendline, so we think the high is likely in. Ultimately, it appears to be an ideal candidate for bearish swing traders.
The bias remains bearish below 444 and / or beneath the bearish trendline.
Being a round number, 400 could provide support initially although an eventual downside break is favoured.
Whilst the bearish trend hold, we could see this head for the lows around 331-335 or even break beneath them.
Price Action Update
Avast CFD/DFT (AVA): Prices have only risen since breaking out of its bullish triangle. However, prices are now consolidating around 440 (right on the monthly R1 pivot) and extended from their averages, so we’d prefer to see a retracement or new level or support form before reconsidering potential longs.
Aveva Group plc CFD/DFT: The bullish trend remains strong and on our radar, having hit and broken through target. Yet just like Avast, its extended from its moving averages more than we’d like and lacks any sort of compression. So we’ll keep a loose eye on it and hopefully it will provide another entry point as we head towards Santa’s rally.
Greggs CFD/DFT: Key support held and it opted for the counter-bias by breaking back above the 200-day eMA and rallying higher. We may have seen a breakaway gap after completing a 3-wave correction around 720. If so, it should now head for and break to new highs. At time of writing, 2146 resistance stands in its way – we’re just waiting for prices to consolidate a little more before considering its potential for a breakout.


Created a garage project using JS that allows simple garage operations
JS to read a JSON file and outputs to the console to view..
Reads a json file and allows a user to search through a list of kings that is returned in the console.
Lets a user create paragraphs as well as delete them.
A simple JS program that allows for a string to be entered and the out put is the number of times a char is repeated.
FizzBuzz challenge with user edited words for customisation
Program designed to have a user input a number with the console outputting the process of reaching 1 via division.


Double Midlands Media Awards triumph for Star columnist Toby
A Shropshire Star journalist has achieved a rare double at a prestigious awards ceremony.
Toby Neal was named Columnist of the Year at the Midlands Media Awards, held at Villa Park.
And he also picked up the President’s Award for achieving the highest judges’ score among the categories.
Toby is one of the Shropshire Star’s longest-serving journalists and provides regular insights into the history of the county through his nostalgia columns.
But it was his weekly political column that caught the eye of the large panel of judges.
He scored more than 90 per cent of the marks and was praised for his irreverent and often very funny look at the unprecedented goings-on at Westminster.
The Shropshire Star was one of five titles nominated for Daily/Sunday Newspaper of the Year, an accolade picked up by its sister paper the Express & Star.
Other nominations included Jamie Ricketts in the category of Tony Flanagan Photographer of the Year and James Pugh for Business Journalist of the Year.
Feature writer Mark Andrews also received a nomination for Columnist of the Year.
Organised by the Birmingham Press Club, the awards celebrate the best in print, online and broadcast journalism, covering an area from beyond Nottingham in the north to Oxford in the south.
Shropshire Star editor Martin Wright said Toby’s award was recognition for one of Britain’s best writers, whose standard of work is of a consistently high standard.
He said: “Toby’s political columns are a delight to read and have a big following both in-paper and online.
“He has been a loyal servant to the Shropshire Star for many years and for him to receive an extra recognition through the President’s Award is fantastic.
"We were also proud to be nominated in other categories including Best Newspaper, which is recognition for all the hard work put in by all our dedicated staff in Shropshire.”


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public interface Index
The Index
object is used to represent the properties
a URI can contain. This is used so that properties relating to a
file can be quickly extracted from an Indexer
. This
will contain all necessary meta data for a file or resource. With
this the File
reference to a resource as well as the
locale, MIME type, name and other such data can be accessed.
Indexer
| Method Summary | |
|---|---|
java.lang.String |
getContentType()
This allows the MIME type of this Index to
be acquired. |
java.io.File |
getDirectory()
This is used to acquire the File directory
for the index target. |
java.io.File |
getFile()
This is used to acquire the File reference
for the index target. |
java.lang.String |
getName()
This allows the name for this object to be acquired. |
Path |
getPath()
This is used to acquire the Path object that
exposes various parts of the URI path. |
java.lang.String |
getRealPath()
This is used to get the path that this object refers to. |
java.lang.String |
getRequestPath()
This is used to acquire the normalized URI style path for the index target. |
| Method Detail |
|---|
java.lang.String getName()
java.lang.String getContentType()
Index
to
be acquired. The MIME type of a file is retrieved by the
Context.getContentType
method for a specific
request URI. This should have a value and perhaps some
parameters like the charset, "text/html; charset=UTF-8".
java.lang.String getRealPath()
java.lang.String getRequestPath()
Mapper
and other such objects that need
a normalized URI style path to resolve resources.
java.io.File getDirectory()
File
directory
for the index target. This is typically rooted at a
base path, for instance the Context
root
is typically used. This allows resources within the
same directory to be acquired easily.
java.io.File getFile()
File
reference
for the index target. This is typically rooted at a
base path, for instance the Context
root
is typically used. This allows the file to be opened,
deleted, or read should the need arise in a service.
Path getPath()
Path
object that
exposes various parts of the URI path. This can be used
to extract the individual path segments as strings as
well as the file extension and various other details.
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