Paper Reading: ClickHouse - Lightning Fast Analytics for Everyone

Session led by [Dharin Shah](https://www.linkedin.com/in/dharinshah/)

Link to Paper: [**https://www.vldb.org/pvldb/vol17/p3731-schulze.pdf**](https://www.vldb.org/pvldb/vol17/p3731-schulze.pdf)

This paper presents an overview of ClickHouse, a popular opensource OLAP database designed for high-performance analytics over petabyte-scale data sets with high ingestion rates. Its storage layer combines a data format based on traditional log-structured merge (LSM) trees with novel techniques for continuous transformation (e.g. aggregation, archiving) of historical data in the background. Queries are written in a convenient SQL dialect and processed by a state-of-the-art vectorized query execution engine with optional code compilation. ClickHouse makes aggressive use of pruning techniques to avoid evaluating irrelevant data in queries. Other data management systems can be integrated at the table function, table engine, or database engine level. Real-world benchmarks demonstrate that ClickHouse is amongst the fastest analytical databases on the market.\
\
We will go into the details of why and how Clickhouse is one of the fastest OLAP database, its underlying storage engine, and the overall philosophy for performance.

Paper Reading: Llama 2: Open Foundation and Fine-Tuned Chat Models

Session led by [Mike A](https://www.linkedin.com/in/malik-mike-a-32b63818b/)

Join us for the second installment of our three-part paper reading series on the LLaMA series of Large Language Models (LLMs).

In this session, we will dive into **Llama 2**, the second iteration in the LLaMA family of models, introduced by Meta AI in mid-2023. This follow-up to the original LLaMA models brings significant advancements, offering foundational and fine-tuned versions with parameter counts ranging from 7B to 70B. Llama 2 models set new benchmarks in helpfulness and safety, showcasing impressive capabilities in dialogue-based tasks and outperforming many open-source alternatives.

We will explore the innovations behind the **Llama 2** models, focusing on their **pretraining methodology**, the **fine-tuning process** that incorporates Reinforcement Learning with Human Feedback (RLHF), and the novel techniques Meta AI used to enhance model safety and alignment. Particular emphasis will be placed on the **Llama 2-Chat models**, optimized for multi-turn conversations, along with references to their performance on real-world safety benchmarks.

This session will be an insightful discussion of how the Llama 2 models have helped to close the gap between open-source and closed-source systems like ChatGPT, examining their architecture, training data, and the steps taken to ensure responsible release and deployment. We'll highlight key takeaways from Meta AI's comprehensive evaluation of these models and discuss how they are still reshaping the landscape of open-source AI.

Whether you're a researcher, developer, or AI enthusiast, this paper reading will offer you an in-depth understanding of the **Llama 2** models and their contributions to the ongoing development of LLMs. This is a unique opportunity to review cutting-edge advancements in **LLM safety**, **dialogue optimization**, and their impact on the future of open-source AI systems.

Mark your calendars for an informative hour of "Llama 2: Open Foundation and Fine-Tuned Chat Models," as we continue to chart the progress of AI language models and explore the innovations that continue to shape the future of the field!

[https://lmarena.ai/\
https://github.com/meta-llama/llama](https://lmarena.ai/￼https://github.com/meta-llama/llama)

Paper Reading: Adaptive Radix Tree - ARTful Indexing for Main-Memory Databases

**Session Led by:[ Dharin Shah](https://www.linkedin.com/in/dharinshah/)**

**Link to Paper:[ ](https://db.in.tum.de/%5C~leis/papers/ART.pdf)[https://db.in.tum.de/\\\~leis/papers/ART.pdf](https://db.in.tum.de/~leis/papers/ART.pdf)**

This paper reading session, we will deep dive into a in-memory optimized index structure, that leverages modern CPU caches to make IOPS faster, also allowing range queries.

Abstract—Main memory capacities have grown up to a point where most databases fit into RAM. For main-memory database systems, index structure performance is a critical bottleneck. Traditional in-memory data structures like balanced binary search trees are not efficient on modern hardware, because they do not optimally utilize on-CPU caches. Hash tables, also often used for main-memory indexes, are fast but only support point queries.

What Order Should You Study Data Structures & Algorithms?

Instead of doing random problems, you need to structure your DSA prep to tackle concepts in the right order.

Course: <https://www.jointaro.com/course/crash-course-data-structures-and-algorithms-concepts/>

How Do I Know If I'm Making Progress With Leetcode?

It's demoralizing to get stuck on similar problems repeatedly. How do you know if you're making progress?

Course: <https://www.jointaro.com/course/crash-course-data-structures-and-algorithms-concepts/>

How To Apply The 80/20 Rule To Leetcode Prep

Even Alvin, who has mastery over Data Structures and Algorithms, would not pass all the tech interviews!

Course: <https://www.jointaro.com/course/crash-course-data-structures-and-algorithms-concepts/>

DSA Crash Course [Part 87] - Outro

If you need additional support taking these DSA skills and actually applying them, take [**Alvin's**](https://www.linkedin.com/in/alvin-zablan-b73a92117/) *complete* data structures and algorithms course on [**Structy**](https://structy.net/?utm_source=taro). You can try out the concepts yourself in their interactive code editor and learn advanced DSA patterns like stack exhaustive recursion.

Use [**this link to get 20% off**](https://structy.net/purchase?promo_code=taro20) the entire Structy DSA learning experience.

Follow Alvin on LinkedIn: <https://www.linkedin.com/in/alvin-zablan-b73a92117/>

DSA Crash Course [Part 86] - Array Stepper Walkthrough

In this lesson, Alvin walks through the python implementation for the *array_stepper* problem.

Code along and test your work in the following editor workspace: <https://structy.net/problems/array-stepper?language=py>

If you need additional support taking these DSA skills and actually applying them, take [**Alvin's**](https://www.linkedin.com/in/alvin-zablan-b73a92117/) *complete* data structures and algorithms course on [**Structy**](https://structy.net/?utm_source=taro). You can try out the concepts yourself in their interactive code editor and learn advanced DSA patterns like stack exhaustive recursion.

Use [**this link to get 20% off**](https://structy.net/purchase?promo_code=taro20) the entire Structy DSA learning experience.

Follow Alvin on LinkedIn: [**https://www.linkedin.com/in/alvin-zablan-b73a92117/**](https://www.linkedin.com/in/alvin-zablan-b73a92117/)

DSA Crash Course [Part 85] - Array Stepper Approach

In this lesson, Alvin explores the strategy to solving the following interview problem:

> Write a function, *array_stepper*, that takes in a list of numbers as an argument. You start at the first position of the list. The function should return a boolean indicating whether or not it is possible to reach the last position of the list. When situated at some position of the list, you may take a maximum number of steps based on the number at that position.
>
> For example, given:
>
> - idx =  0  1  2  3  4  5
> - numbers = \[2, 4, 2, 0, 0, 1\]
>
> The answer is True.
>
> We start at idx 0, we could take 1 step or 2 steps forward.
>
> The correct choice is to take 1 step to idx 1.
>
> Then take 4 steps forward to the end at idx 5.

```
array_stepper([2, 4, 2, 0, 0, 1]) # -> True
```

```
array_stepper([2, 3, 2, 0, 0, 1]) # -> False
```

```
array_stepper([3, 1, 3, 1, 0, 1]) # -> True
```

```
array_stepper([4, 1, 5, 1, 1, 1, 0, 4]) # -> True
```

```
array_stepper([4, 1, 2, 1, 1, 1, 0, 4]) # -> False
```

If you need additional support taking these DSA skills and actually applying them, take [**Alvin's**](https://www.linkedin.com/in/alvin-zablan-b73a92117/) *complete* data structures and algorithms course on [**Structy**](https://structy.net/?utm_source=taro). You can try out the concepts yourself in their interactive code editor and learn advanced DSA patterns like stack exhaustive recursion.

Use [**this link to get 20% off**](https://structy.net/purchase?promo_code=taro20) the entire Structy DSA learning experience.

Follow Alvin on LinkedIn: [**https://www.linkedin.com/in/alvin-zablan-b73a92117/**](https://www.linkedin.com/in/alvin-zablan-b73a92117/)

DSA Crash Course [Part 84] - Counting Change Walkthrough

In this lesson, Alvin walks through the python implementation for the *counting_change* problem.

Code along and test your work in the following editor workspace: <https://structy.net/problems/counting-change?language=py>

If you need additional support taking these DSA skills and actually applying them, take [**Alvin's**](https://www.linkedin.com/in/alvin-zablan-b73a92117/) *complete* data structures and algorithms course on [**Structy**](https://structy.net/?utm_source=taro). You can try out the concepts yourself in their interactive code editor and learn advanced DSA patterns like stack exhaustive recursion.

Use [**this link to get 20% off**](https://structy.net/purchase?promo_code=taro20) the entire Structy DSA learning experience.

Follow Alvin on LinkedIn: [**https://www.linkedin.com/in/alvin-zablan-b73a92117/**](https://www.linkedin.com/in/alvin-zablan-b73a92117/)

DSA Crash Course [Part 83] - Counting Change Approach

In this lesson, Alvin explores the strategy to solving the following interview problem:

> Write a function, *counting_change*, that takes in an amount and a list of coins. The function should return the number of different ways it is possible to make change for the given amount using the coins.
>
> You may reuse a coin as many times as necessary.
>
> For example,
>
> counting_change(4, \[1,2,3\]) -&gt; 4
>
> There are four different ways to make an amount of 4:
>
> 1\. 1 + 1 + 1 + 1
>
> 2\. 1 + 1 + 2
>
> 3\. 1 + 3
>
> 4\. 2 + 2

```
counting_change(4, [1, 2, 3]) # -> 4
```

```
counting_change(8, [1, 2, 3]) # -> 10
```

```
counting_change(24, [5, 7, 3]) # -> 5
```

If you need additional support taking these DSA skills and actually applying them, take [**Alvin's**](https://www.linkedin.com/in/alvin-zablan-b73a92117/) *complete* data structures and algorithms course on [**Structy**](https://structy.net/?utm_source=taro). You can try out the concepts yourself in their interactive code editor and learn advanced DSA patterns like stack exhaustive recursion.

Use [**this link to get 20% off**](https://structy.net/purchase?promo_code=taro20) the entire Structy DSA learning experience.

Follow Alvin on LinkedIn: [**https://www.linkedin.com/in/alvin-zablan-b73a92117/**](https://www.linkedin.com/in/alvin-zablan-b73a92117/)

DSA Crash Course [Part 82] - Summing Squares Walkthrough

In this lesson, Alvin walks through the python implementation for the *summing_squares* problem.

Code along and test your work in the following editor workspace: <https://structy.net/problems/summing-squares?language=py>

If you need additional support taking these DSA skills and actually applying them, take [**Alvin's**](https://www.linkedin.com/in/alvin-zablan-b73a92117/) *complete* data structures and algorithms course on [**Structy**](https://structy.net/?utm_source=taro). You can try out the concepts yourself in their interactive code editor and learn advanced DSA patterns like stack exhaustive recursion.

Use [**this link to get 20% off**](https://structy.net/purchase?promo_code=taro20) the entire Structy DSA learning experience.

Follow Alvin on LinkedIn: [**https://www.linkedin.com/in/alvin-zablan-b73a92117/**](https://www.linkedin.com/in/alvin-zablan-b73a92117/)

DSA Crash Course [Part 81] - Summing Squares Approach

In this lesson, Alvin explores the strategy to solving the following interview problem:

> Write a function, *summing_squares*, that takes a target number as an argument. The function should return the minimum number of perfect squares that sum to the target. A perfect square is a number of the form (i\*i) where i &gt;= 1.
>
> For example: 1, 4, 9, 16 are perfect squares, but 8 is not a perfect square.
>
> Given 12:
>
> summing_squares(12) -&gt; 3
>
> The minimum squares required for 12 is three, by doing 4 + 4 + 4.
>
> Another way to make 12 is 9 + 1 + 1 + 1, but that requires four perfect squares.

```
summing_squares(8) # -> 2
```

```
summing_squares(9) # -> 1
```

```
summing_squares(12) # -> 3
```

```
summing_squares(1) # -> 1
```

```
summing_squares(31) # -> 4
```

If you need additional support taking these DSA skills and actually applying them, take [**Alvin's**](https://www.linkedin.com/in/alvin-zablan-b73a92117/) *complete* data structures and algorithms course on [**Structy**](https://structy.net/?utm_source=taro). You can try out the concepts yourself in their interactive code editor and learn advanced DSA patterns like stack exhaustive recursion.

Use [**this link to get 20% off**](https://structy.net/purchase?promo_code=taro20) the entire Structy DSA learning experience.

Follow Alvin on LinkedIn: [**https://www.linkedin.com/in/alvin-zablan-b73a92117/**](https://www.linkedin.com/in/alvin-zablan-b73a92117/)

DSA Crash Course [Part 80] - Non Adjacent Sum Walkthrough

In this lesson, Alvin walks through the python implementation for the *non_adjacent_sum* problem.

Code along and test your work in the following editor workspace: <https://structy.net/problems/non-adjacent-sum?language=py>

If you need additional support taking these DSA skills and actually applying them, take [**Alvin's**](https://www.linkedin.com/in/alvin-zablan-b73a92117/) *complete* data structures and algorithms course on [**Structy**](https://structy.net/?utm_source=taro). You can try out the concepts yourself in their interactive code editor and learn advanced DSA patterns like stack exhaustive recursion.

Use [**this link to get 20% off**](https://structy.net/purchase?promo_code=taro20) the entire Structy DSA learning experience.

Follow Alvin on LinkedIn: [**https://www.linkedin.com/in/alvin-zablan-b73a92117/**](https://www.linkedin.com/in/alvin-zablan-b73a92117/)

DSA Crash Course [Part 79] - Non Adjacent Sum Approach

In this lesson, Alvin explores the strategy to solving the following interview problem:

> Write a function, *non_adjacent_sum*, that takes in a list of numbers as an argument. The function should return the maximum sum of non-adjacent items in the list. There is no limit on how many items can be taken into the sum as long as they are not adjacent.
>
> For example, given: \[2, 4, 5, 12, 7\]
>
> The maximum non-adjacent sum is 16, because 4 + 12. 4 and 12 are not adjacent in the list.

```
nums = [2, 4, 5, 12, 7]
non_adjacent_sum(nums) # -> 16
```

```
nums = [7, 5, 5, 12]
non_adjacent_sum(nums) # -> 19
```

```
nums = [7, 5, 5, 12, 17, 29]
non_adjacent_sum(nums) # -> 48
```

```
nums = [
72, 62, 10,  6, 20, 19, 42,
46, 24, 78, 30, 41, 75, 38,
23, 28, 66, 55, 12, 17, 9,
12, 3, 1, 19, 30, 50, 20
]

non_adjacent_sum(nums) # -> 488
```

If you need additional support taking these DSA skills and actually applying them, take [**Alvin's**](https://www.linkedin.com/in/alvin-zablan-b73a92117/) *complete* data structures and algorithms course on [**Structy**](https://structy.net/?utm_source=taro). You can try out the concepts yourself in their interactive code editor and learn advanced DSA patterns like stack exhaustive recursion.

Use [**this link to get 20% off**](https://structy.net/purchase?promo_code=taro20) the entire Structy DSA learning experience.

Follow Alvin on LinkedIn: [**https://www.linkedin.com/in/alvin-zablan-b73a92117/**](https://www.linkedin.com/in/alvin-zablan-b73a92117/)

DSA Crash Course [Part 78] - Max Path Sum Walkthrough

In this lesson, Alvin walks through the python implementation for the *max_path_sum* problem.

Code along and test your work in the following editor workspace: <https://structy.net/problems/max-path-sum?language=py>

If you need additional support taking these DSA skills and actually applying them, take [**Alvin's**](https://www.linkedin.com/in/alvin-zablan-b73a92117/) *complete* data structures and algorithms course on [**Structy**](https://structy.net/?utm_source=taro). You can try out the concepts yourself in their interactive code editor and learn advanced DSA patterns like stack exhaustive recursion.

Use [**this link to get 20% off**](https://structy.net/purchase?promo_code=taro20) the entire Structy DSA learning experience.

Follow Alvin on LinkedIn: [**https://www.linkedin.com/in/alvin-zablan-b73a92117/**](https://www.linkedin.com/in/alvin-zablan-b73a92117/)

DSA Crash Course [Part 77] - Max Path Sum Approach

In this lesson, Alvin explores the strategy to solving the following interview problem:

> Write a function, *max_path_sum*, that takes in a grid as an argument. The function should return the maximum sum possible by traveling a path from the top-left corner to the bottom-right corner. You may only travel through the grid by moving down or right.
>
> You can assume that all numbers are non-negative.

```
grid = [
[1, 3, 12],
[5, 1, 1],
[3, 6, 1],
]

max_path_sum(grid) # -> 18
```

```
grid = [
[1, 2, 8,  1],
[3, 1, 12, 10],
[4, 0, 6,  3],
]

max_path_sum(grid) # -> 36
```

```
grid = [
[1, 2, 8, 1],
[3, 10, 12, 10],
[4, 0, 6, 3],
]

max_path_sum(grid) # -> 39
```

If you need additional support taking these DSA skills and actually applying them, take [**Alvin's**](https://www.linkedin.com/in/alvin-zablan-b73a92117/) *complete* data structures and algorithms course on [**Structy**](https://structy.net/?utm_source=taro). You can try out the concepts yourself in their interactive code editor and learn advanced DSA patterns like stack exhaustive recursion.

Use [**this link to get 20% off**](https://structy.net/purchase?promo_code=taro20) the entire Structy DSA learning experience.

Follow Alvin on LinkedIn: [**https://www.linkedin.com/in/alvin-zablan-b73a92117/**](https://www.linkedin.com/in/alvin-zablan-b73a92117/)

DSA Crash Course [Part 76] - Counts Paths Walkthrough

In this lesson, Alvin walks through the python implementation for the *count_paths* problem.

Code along and test your work in the following editor workspace: <https://structy.net/problems/count-paths?language=py>

If you need additional support taking these DSA skills and actually applying them, take [**Alvin's**](https://www.linkedin.com/in/alvin-zablan-b73a92117/) *complete* data structures and algorithms course on [**Structy**](https://structy.net/?utm_source=taro). You can try out the concepts yourself in their interactive code editor and learn advanced DSA patterns like stack exhaustive recursion.

Use [**this link to get 20% off**](https://structy.net/purchase?promo_code=taro20) the entire Structy DSA learning experience.

Follow Alvin on LinkedIn: [**https://www.linkedin.com/in/alvin-zablan-b73a92117/**](https://www.linkedin.com/in/alvin-zablan-b73a92117/)

DSA Crash Course [Part 75] - Count Paths Approach

In this lesson, Alvin explores the strategy to solving the following interview problem:

> Write a function, *count_paths*, that takes in a grid as an argument. In the grid, 'X' represents walls and 'O' represents open spaces. You may only move down or to the right and cannot pass through walls. The function should return the number of ways possible to travel from the top-left corner of the grid to the bottom-right corner.

```
grid = [
["O", "O"],
["O", "O"],
]

count_paths(grid) # -> 2
```

```
grid = [
["O", "O", "X"],
["O", "O", "O"],
["O", "O", "O"],
]

count_paths(grid) # -> 5
```

```
grid = [
["O", "O", "O"],
["O", "O", "X"],
["O", "O", "O"],
]

count_paths(grid) # -> 3
```

If you need additional support taking these DSA skills and actually applying them, take [**Alvin's**](https://www.linkedin.com/in/alvin-zablan-b73a92117/) *complete* data structures and algorithms course on [**Structy**](https://structy.net/?utm_source=taro). You can try out the concepts yourself in their interactive code editor and learn advanced DSA patterns like stack exhaustive recursion.

Use [**this link to get 20% off**](https://structy.net/purchase?promo_code=taro20) the entire Structy DSA learning experience.

Follow Alvin on LinkedIn: [**https://www.linkedin.com/in/alvin-zablan-b73a92117/**](https://www.linkedin.com/in/alvin-zablan-b73a92117/)

DSA Crash Course [Part 74] - Minimum Change Walkthrough

In this lesson, Alvin walks through the python implementation for the *min_change* problem.

Code along and test your work in the following editor workspace: <https://structy.net/problems/min-change?language=py>

If you need additional support taking these DSA skills and actually applying them, take [**Alvin's**](https://www.linkedin.com/in/alvin-zablan-b73a92117/) *complete* data structures and algorithms course on [**Structy**](https://structy.net/?utm_source=taro). You can try out the concepts yourself in their interactive code editor and learn advanced DSA patterns like stack exhaustive recursion.

Use [**this link to get 20% off**](https://structy.net/purchase?promo_code=taro20) the entire Structy DSA learning experience.

Follow Alvin on LinkedIn: [**https://www.linkedin.com/in/alvin-zablan-b73a92117/**](https://www.linkedin.com/in/alvin-zablan-b73a92117/)

Tech Career Growth Videos

Our instructional videos provide software engineers with insights needed for career growth. Masterclasses go incredibly in-depth on a specific tech topic while case studies are real examples from established engineering leaders.