├── .DS_Store
├── LICENSE
├── README.md
├── astra.json
├── images
├── badge_data_modeling.png
├── create_astra_db_button.png
├── illustrations.png
├── kdm_01.png
├── kdm_02.png
├── kdm_03.png
├── killrcoda_01.png
├── killrcoda_02.png
└── splash.png
├── materials
├── kdm_sensor_data.xml
└── kdm_sensor_data_2.xml
└── slides
└── slides.pdf
/.DS_Store:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/datastaxdevs/workshop-cassandra-data-modeling/d7a1422b262e044512e85b0daea0fcd9f4e19652/.DS_Store
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/LICENSE:
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/README.md:
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1 | # 🎓🎓 Apache Cassandra® Data Modelling
2 |
3 | Welcome to the **Apache Cassandra® Data Modelling** workshop! In this two-hour workshop, we show the methodology to build an effective data model with the distributed `NoSQL database Apache Cassandra™`.
4 |
5 | Using **Astra DB**, the cloud based _Cassandra-as-a-Service_ platform delivered by DataStax, we will cover the process for every developer who wants to build an application: list the use cases and build an effective data model.
6 |
7 | 
8 |
9 | It doesn't matter if you join our workshop live or you prefer to do at your own pace, we have you covered. In this repository, you'll find everything you need for this workshop:
10 |
11 | > [🔖 Accessing HANDS-ON](#-start-hands-on)
12 |
13 | ## 📋 Table of content
14 |
15 | 
16 |
17 | 1. [Objectives](#1-objectives)
18 | 2. [Frequently Asked Questions](#2-frequently-asked-questions)
19 | 3. [Materials for the Session](#3-materials-for-the-session)
20 | 4. [Create Your Astra DB Instance](#4-create-your-astra-db-instance)
21 | 5. [Tables with Single-Row and Multi-Row Partitions](#5-tables-with-single-row-and-multi-row-partitions)
22 | 6. [Dynamic Bucketing](#6-dynamic-bucketing)
23 | 7. [Working with Data Types](#7-working-with-data-types)
24 | 8. [KDM Data Modeling Tool](#8-kdm-data-modeling-tool)
25 | 9. [Sensor Data Modeling](#9-sensor-data-modeling)
26 | 10. [Homework](#10-homework)
27 | 11. [What's NEXT ](#11-whats-next-)
28 |
29 |
30 | ## 1. Objectives
31 |
32 | 1️⃣ **Understand how data is distributed and organized in Apache Cassandra®**
33 |
34 | 2️⃣ **Learn how primary, partition, and clustering keys are defined in Apache Cassandra®**
35 |
36 | 3️⃣ **Become familiar with CQL data types in Apache Cassandra®**
37 |
38 | 4️⃣ **Learn about the data modeling methodology for Apache Cassandra®**
39 |
40 | 🚀 **Have fun with an interactive session**
41 |
42 | ## 2. Frequently Asked Questions
43 |
44 |
45 |
46 | 1️⃣ Can I run this workshop on my computer?
47 |
48 | There is nothing preventing you from running the workshop on your own machine. If you do so, you will need the following:
49 |
50 | - git installed on your local system
51 |
52 |
53 | In this readme, we try to provide instructions for local development as well - but keep in mind that the main focus is development on Gitpod, hence we can't guarantee live support about local development in order to keep on track with the schedule. However, we will do our best to give you the info you need to succeed.
54 |
55 |
56 |
57 | 2️⃣ What other prerequisites are required?
58 |
59 |
60 | - You will need enough "real estate" on screen, we will ask you to open a few windows and it would not fit on mobiles (tablets should be OK)
61 |
- You will need an Astra account: don't worry, we'll work through that in the following
62 |
- As "Intermediate level" we expect you to know what java and Spring are.
63 |
64 |
65 |
66 |
67 |
68 | 3️⃣ Do I need to pay for anything for this workshop?
69 |
70 | No. All tools and services we provide here are FREE. FREE not only during the session but also after.
71 |
72 |
73 |
74 | 4️⃣ Will I get a certificate if I attend this workshop?
75 |
76 | Attending the session is not enough. You need to complete the homework detailed below and you will get a nice badge that you can share on LinkedIn or anywhere else.
77 |
78 |
79 |
80 | ## 3. Materials for the Session
81 |
82 | It doesn't matter if you join our workshop live or you prefer to work at your own pace,
83 | we have you covered. In this repository, you'll find everything you need for this workshop:
84 |
85 | - [Slide deck](/slides/slides.pdf)
86 | - [Discord chat](https://dtsx.io/discord)
87 | - [Questions and Answers](https://community.datastax.com/)
88 |
89 | ----
90 |
91 | # 🏁 Start Hands-on
92 |
93 | ## 4. Create Your Astra DB Instance
94 |
95 | _**`ASTRA DB`** is the simplest way to run Cassandra with zero operations at all - just push the button and get your cluster. No credit card required, 40M read/write operations and about 80GB storage monthly for free - sufficient to run small production workloads. If you end your credits the databases will pause, no charge_
96 |
97 | Leveraging [Database creation guide](https://awesome-astra.github.io/docs/pages/astra/create-instance/#c-procedure) create a database. *Right-Click the button* with *Open in a new TAB.*
98 |
99 | 
100 |
101 | |Field|Value|
102 | |---|---|
103 | |**Database Name**| `workshops`|
104 | |**Keyspace Name**| `sensor_data`|
105 | |**Regions**| Select `GOOGLE CLOUD`, then an Area close to you, then a region with no LOCKER 🔒 icons, those are the region you can use for free. |
106 |
107 | > **ℹ️ Note:** If you already have a database `workshops`, simply add a keyspace `sensor_data` using the `Add Keyspace` button on the bottom right hand corner of db dashboard page.
108 |
109 | While the database is being created, you will also get a **Security token**:
110 | save it somewhere safe, as it will be needed to later in other workshops (In particular the string starting with `AstraCS:...`.)
111 |
112 | > **⚠️ Important**
113 | > ```
114 | > The instructor will show you on screen how to create a token
115 | > but will have to destroy to token immediately for security reasons.
116 | > ```
117 |
118 | The status will change from `Pending` to `Active` when the database is ready, this will only take 2-3 minutes. You will also receive an email when it is ready.
119 |
120 | [🏠 Back to Table of Contents](#-table-of-content)
121 |
122 | ## 5. Tables with Single-Row and Multi-Row Partitions
123 |
124 | A [GitHub](https://github.com) account may be required to run this hands-on lab in Gitpod.
125 |
126 | ### ✅ Part 1: [Tables with Single-Row Partitions](https://gitpod.io/#https://github.com/DataStax-Academy/workshop-cassandra-data-modeling-tables-single-row-partitions/)
127 |
128 | ### ✅ Part 2: [Tables with Multi-Row Partitions](https://gitpod.io/#https://github.com/DataStax-Academy/workshop-cassandra-data-modeling-tables-multi-row-partitions/)
129 |
130 | [🏠 Back to Table of Contents](#-table-of-content)
131 |
132 | ## 6. Dynamic Bucketing
133 |
134 |
135 | 📌 Homework 1
136 |
137 | Consider the table that supports query `Find all sensors in a specified network`:
138 | ```sql
139 | CREATE TABLE sensors_by_network_2 (
140 | network TEXT,
141 | sensor TEXT,
142 | PRIMARY KEY ((network), sensor)
143 | );
144 | ```
145 |
146 | Assume that a network may have none to millions of sensors. With dynamic bucketing, we can introduce artificial buckets to store sensors. A network with a few sensors may only need one bucket. A network with many sensors may need many buckets. Once buckets belonging to a particular network get filled with sensors, we can dynamically assign new buckets to store new sensors of this network.
147 |
148 | 📘 **Implement dynamic bucketing in Astra DB**
149 | ```sql
150 | -- Table to manage buckets
151 | CREATE TABLE buckets_by_network (
152 | network TEXT,
153 | bucket TIMEUUID,
154 | PRIMARY KEY ((network), bucket)
155 | ) WITH CLUSTERING ORDER BY (bucket DESC);
156 |
157 | -- Table to store sensors
158 | CREATE TABLE sensors_by_bucket (
159 | bucket TIMEUUID,
160 | sensor TEXT,
161 | PRIMARY KEY ((bucket), sensor)
162 | );
163 |
164 |
165 | -- Sample data
166 | INSERT INTO buckets_by_network (network, bucket) VALUES ('forest-net', 49171ffe-0d12-11ed-861d-0242ac120002);
167 | INSERT INTO buckets_by_network (network, bucket) VALUES ('forest-net', 74a13ede-0d12-11ed-861d-0242ac120002);
168 |
169 | INSERT INTO sensors_by_bucket (bucket, sensor) VALUES (49171ffe-0d12-11ed-861d-0242ac120002, 's1001');
170 | INSERT INTO sensors_by_bucket (bucket, sensor) VALUES (49171ffe-0d12-11ed-861d-0242ac120002, 's1002');
171 |
172 | INSERT INTO sensors_by_bucket (bucket, sensor) VALUES (74a13ede-0d12-11ed-861d-0242ac120002, 's1003');
173 | ```
174 |
175 | 📘 **Add a new sensor to a network**
176 |
177 | 1. Get the latest bucket.
178 | ```sql
179 | SELECT bucket FROM buckets_by_network WHERE network = 'forest-net' LIMIT 1;
180 | ```
181 |
182 | 2. Check the number of sensors in the bucket.
183 | ```sql
184 | SELECT COUNT(*) AS sensors
185 | FROM sensors_by_bucket WHERE bucket = 74a13ede-0d12-11ed-861d-0242ac120002;
186 | ```
187 |
188 | 3. Depending on the sensors-per-bucket threshold, insert a new sensor into the existing bucket, or create a new bucket and insert into the new bucket.
189 | ```sql
190 | INSERT INTO sensors_by_bucket (bucket, sensor) VALUES (74a13ede-0d12-11ed-861d-0242ac120002, 's1004');
191 | ```
192 |
193 | 📘 **Retrieve sensors in a specified network**
194 |
195 | 1. Retrieve the buckets
196 | ```sql
197 | SELECT bucket FROM buckets_by_network WHERE network = 'forest-net';
198 | ```
199 |
200 | 2. Retrieve the sensors
201 | ```sql
202 | SELECT sensor
203 | FROM sensors_by_bucket
204 | WHERE bucket IN (74a13ede-0d12-11ed-861d-0242ac120002, 49171ffe-0d12-11ed-861d-0242ac120002);
205 | ```
206 |
207 |
208 |
209 | [🏠 Back to Table of Contents](#-table-of-content)
210 |
211 | ## 7. Working with Data Types
212 |
213 |
214 | 📌 Homework 2
215 |
216 | ### ✅ Step 7a. `List` Collections
217 |
218 | 📘 **Command to execute**
219 |
220 | ```sql
221 | // Definition
222 | CREATE TABLE IF NOT EXISTS table_with_list (
223 | uid uuid,
224 | items list,
225 | PRIMARY KEY (uid)
226 | );
227 |
228 | // Insert
229 | INSERT INTO table_with_list(uid,items)
230 | VALUES (c7133017-6409-4d7a-9479-07a5c1e79306, ['a', 'b', 'c']);
231 |
232 | // Replace
233 | UPDATE table_with_list SET items = ['d', 'e']
234 | WHERE uid = c7133017-6409-4d7a-9479-07a5c1e79306;
235 |
236 | // Show result
237 | SELECT * FROM table_with_list ;
238 |
239 | // Append to list
240 | UPDATE table_with_list SET items = items + ['f']
241 | WHERE uid = c7133017-6409-4d7a-9479-07a5c1e79306;
242 |
243 | // Replace an element (not available in Astra because read before write)
244 | UPDATE table_with_list SET items[0] = ['g']
245 | WHERE uid = c7133017-6409-4d7a-9479-07a5c1e79306;
246 | ```
247 |
248 | ### ✅ Step 7b. `Set` Collections
249 |
250 | 📘 **Command to execute**
251 |
252 | ```sql
253 | // Definition
254 | CREATE TABLE IF NOT EXISTS table_with_set (
255 | uid uuid,
256 | animals set,
257 | PRIMARY KEY (uid)
258 | );
259 |
260 | // Insert
261 | INSERT INTO table_with_set(uid,animals)
262 | VALUES (87fad746-4adf-4107-9858-df8643564186, {'spider', 'cat', 'dog'});
263 |
264 | // Replace
265 | UPDATE table_with_set SET animals = {'pangolin', 'bat'}
266 | WHERE uid = 87fad746-4adf-4107-9858-df8643564186;
267 |
268 | // Show result
269 | SELECT * FROM table_with_set;
270 |
271 | // Append to Set
272 | UPDATE table_with_set SET animals = animals + {'sheep'}
273 | WHERE uid = 87fad746-4adf-4107-9858-df8643564186;
274 | ```
275 |
276 | ### ✅ Step 7c. `Map` Collections
277 |
278 | 📘 **Command to execute**
279 |
280 |
281 | ```sql
282 | // Definition
283 | CREATE TABLE IF NOT EXISTS table_with_map (
284 | uid text,
285 | dictionary map,
286 | PRIMARY KEY (uid)
287 | );
288 |
289 | // Insert
290 | INSERT INTO table_with_map(uid, dictionary)
291 | VALUES ('fr_en', {'fromage':'cheese', 'vin':'wine', 'pain':'bread'});
292 |
293 | // Replace
294 | UPDATE table_with_map SET dictionary = {'saucisse': 'sausage'}
295 | WHERE uid = 'fr_en';
296 |
297 | // Show result
298 | SELECT * FROM table_with_map;
299 |
300 | // Append to Map
301 | UPDATE table_with_map SET dictionary = dictionary + {'frites':'fries'}
302 | WHERE uid = 'fr_en';
303 | ```
304 |
305 | ### ✅ Step 7d. User-Defined Types
306 |
307 | 📘 **Command to execute**
308 |
309 | ```sql
310 | // Definition
311 | CREATE TYPE IF NOT EXISTS udt_address (
312 | street text,
313 | city text,
314 | state text,
315 | );
316 |
317 | // Use the UDT in a table
318 | CREATE TABLE IF NOT EXISTS table_with_udt (
319 | uid text,
320 | address udt_address,
321 | PRIMARY KEY (uid)
322 | );
323 |
324 | // INSERT (not quote on field names like street)
325 | INSERT INTO table_with_udt(uid, address)
326 | VALUES ('superman', {street:'daily planet',city:'metropolis',state:'CA'});
327 |
328 | // Replace
329 | UPDATE table_with_udt
330 | SET address = {street:'pingouin alley',city:'antarctica',state:'melting'}
331 | WHERE uid = 'superman';
332 |
333 | // Replace a single field
334 | UPDATE table_with_udt
335 | SET address.state = 'melt'
336 | WHERE uid = 'superman';
337 | ```
338 |
339 | ### ✅ Step 7e. Counters
340 |
341 | 📘 **Command to execute**
342 |
343 | ```sql
344 | // Definition
345 | CREATE TABLE IF NOT EXISTS table_with_counters (
346 | handle text,
347 | following counter,
348 | followers counter,
349 | notifications counter,
350 | PRIMARY KEY (handle)
351 | );
352 |
353 | // You have a new follower
354 | UPDATE table_with_counters SET followers = followers + 1
355 | WHERE handle = 'clunven';
356 |
357 | // Some counters are... null
358 | SELECT * from table_with_counters;
359 |
360 | // Set to 0... but set is not valid
361 | UPDATE table_with_counters
362 | SET following = following + 0, notifications = notifications + 0
363 | WHERE handle = 'clunven';
364 |
365 | // Following someone
366 | UPDATE table_with_counters SET following = following + 1
367 | WHERE handle = 'clunven';
368 |
369 | // You have a new message
370 | UPDATE table_with_counters SET notifications = notifications + 1
371 | WHERE handle = 'clunven';
372 |
373 | ```
374 |
375 |
376 |
377 | [🏠 Back to Table of Contents](#-table-of-content)
378 |
379 | ## 8. KDM Data Modeling Tool
380 |
381 |
382 | 🍿 Demo
383 |
384 | ### ✅ Download [the project XML file](https://raw.githubusercontent.com/datastaxdevs/workshop-cassandra-data-modeling/main/materials/kdm_sensor_data.xml).
385 |
386 | ### ✅ Open [the KDM tool](http://kdm.kashliev.com/).
387 |
388 | ### ✅ Import the project by selecting `Import Project` from the menu and specifying file `kdm_sensor_data.xml`.
389 |
390 | 
391 |
392 | 
393 |
394 | ### ✅ Explore the five data modeling steps supported by KDM. Note that the conceptual data model in Step 1 and queries in Step 2 are already defined.
395 |
396 | 
397 |
398 |
399 |
400 | [🏠 Back to Table of Contents](#-table-of-content)
401 |
402 | ## 9. Sensor Data Modeling
403 |
404 | A [GitHub](https://github.com) account may be required to run this hands-on lab in Gitpod.
405 |
406 | ### ✅ [Sensor Data Modeling](https://gitpod.io/#https://github.com/DataStax-Academy/workshop-cassandra-data-modeling-sensor-data/)
407 |
408 | [🏠 Back to Table of Contents](#-table-of-content)
409 |
410 |
411 | ## 10. Homework
412 |
413 | 1. Complete [Working with Data Types](#7-working-with-data-types). Take a screenshot of the CQL Console showing the rows in tables
414 | `table_with_udt` and `table_with_counters` before _and_ after executing the DELETE statements.
415 |
416 | 2. Complete the mini-course [Time Series Data Modeling](https://www.datastax.com/learn/data-modeling-by-example/time-series-model). Take a screenshot of the final screen of the practice lab, with the console output at the right.
417 |
418 | 3. [Submit your homework](https://forms.gle/Z69y4MM3SpEDg7nt5) and be awarded a nice verifiable badge!
419 |
420 | [🏠 Back to Table of Contents](#-table-of-content)
421 |
422 | ## 11. What's NEXT ?
423 |
424 | We've just scratched the surface of what you can do using Astra DB, built on Apache Cassandra.
425 |
426 | Go take a look at [DataStax for Developers](https://www.datastax.com/dev) to see what else is possible.
427 | There's plenty to dig into!
428 |
429 | Congratulations: you made to the end of today's workshop.
430 |
431 | 
432 |
433 | **... and see you at our next workshop!**
434 |
435 | > Sincerely yours, The DataStax Developers
436 |
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2 | "name": "Apache Cassandra Data Modeling",
3 | "description": "This session looks at how to effectively design a data model for your application. You’ll leave knowing how to create data models that scale effectively as your system grows ",
4 | "duration": "120 minutes",
5 | "skillLevel": "Beginner",
6 | "language":["CQL"],
7 | "stack":["cassandra"],
8 | "githubUrl": "https://github.com/datastaxdevs/workshop-cassandra-data-modeling/",
9 | "badge": "https://media.badgr.com/uploads/badges/5dee8148-ec34-43e2-a4ec-68a501130cfd.png",
10 | "youTubeUrl": [ "https://www.youtube.com/watch?v=xrDfE74QnSE"],
11 | "tags": [
12 | { "name": "workshop" },
13 | { "name": "nosql" },
14 | { "name": "cassandra" },
15 | { "name": "cql" }
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17 | "category": "workshop",
18 | "usecases": []
19 | }
20 |
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/materials/kdm_sensor_data.xml:
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116 | {"records":["Sensor.id,Temperature.timestamp"],"Temperature":["Sensor.id,Temperature.timestamp"]}
117 |
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121 | simple
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128 | Network.name
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154 | Network.region
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166 | Network.number of sensors
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181 | Network.name
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184 | value
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189 | Sensor.id
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195 | Sensor.latitude
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201 | Sensor.longitude
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207 | Temperature.value
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213 | Temperature.date
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216 | DESC
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221 | Temperature.hour
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224 | DESC
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226 |
227 | Find hourly average temperatures for every sensor in a specified network for a given date range; order by date (desc) and hour (desc)
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231 | simple
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237 | Network.name
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249 | Sensor.id
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259 | Sensor.latitude
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269 | Sensor.longitude
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279 | Sensor.characteristics
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295 | Sensor.id
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307 | Temperature.date
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319 | Temperature.timestamp
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331 | Temperature.value
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129 | Network.name
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159 | Network.region
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173 | Network.number of sensors
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190 | Network.name
191 |
199 |
200 | Sensor.id
201 |
207 |
208 | Sensor.latitude
209 |
215 |
216 | Sensor.longitude
217 |
223 |
224 | Temperature.value
225 |
231 |
232 | Temperature.date
233 |
241 |
242 | Temperature.hour
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260 | Network.name
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274 | Sensor.id
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286 | Sensor.latitude
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298 | Sensor.longitude
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310 | Sensor.characteristics
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342 | Temperature.date
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356 | Temperature.timestamp
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368 |
369 |
370 | Temperature.value
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373 |