Elasticsearch, a decentralized and open source search and analysis engine. I think there are some people who are interested in it these days and plan to use it for business. In such a case
If you look at it, it's okay! !!
Speaking of (official), it's definitely true,
――The hurdle is high if the starting point is English ――For the time being, I want to grasp only the main points and use it hard
I think that there are many people who will be, so I tried to summarize it assuming that I will pick up a good article for beginners and actually move it.
The emphasis is on creating an environment and actually moving it. My main environment and language are as described above, so please be patient. For people in other environments, please see around 3 and 4 of "Main subject" for reference.
Curl, a tool for exchanging data with servers. Even though it's Elasticsearch, why is it curl? It seems like that, but it is essential for command-based operations. And unfortunately Windows doesn't include curl by default. You need to download it.
Download → Unzip and set the path to the bin folder in the environment variable (user environment variable). Then, execute the following command at the command prompt to prevent garbled characters even in Japanese.
UTF character code-Change to 8
> chcp 65001
If you don't understand curl in the first place and want to know the basic usage,
-curl command usage memo | Qiita -Frequently used curl command options | Qiita
Is very helpful, so it's a good idea to read it.
Now let's build an Elasticsearch environment! You don't have to have Kibana,
――Easy operation of Elasticsearch, --You can visualize the data
It's a useful tool, so be sure to include it as well.
-[Introduction to Elasticsearch] Environment Construction for Windows | Qiita
If you refer to, the environment on Windows will be ready ♪ ・ ・ I'm sorry, but it's really downloadable! !! !! It takes a long time (I took about 3.5 hours: Kibana is not so much), so people who can use Docker reasonably
-First Elasticsearch with Docker | Qiita -Run Elasticsearch + Kibana with Docker Compose | Qiita -Persist Elasticsearch data on Docker container | Qiita
You may build the environment quickly by referring to the surroundings. (It took less than an hour to review the settings.)
On the official website,
There is a description in, so please refer to this as well.
Well then! Let's move it right away! Before that, it is important to understand the basic concept. In the first place, let's put that in front of the environment, and the contents that we want to keep at a minimum are as follows.
I think it's easier to get an image of terms that often appear when compared to RDB. If you look it up, there are various theories, but the most suitable combination is as follows.
| RDB | Elasticsearch |
|---|---|
| Database | Cluster |
| table | Index |
| table definition | Mapping |
| record(line) | Document |
| Column | Fields |
| Primary key (primary key) | Document ID |
Of course there are different parts, so for details
-Elasticsearch to learn by making clients | iPRIDE
Please refer to the.
Then, analyzer. An analyzer is a text analysis that performs the process of converting to the best format for searching. The analyzer is roughly divided into the following three parts.
| name | Contents |
|---|---|
| Character filters | Perform necessary processing (addition, deletion, change) before dividing the character string with Tokenizer. Pre-processing position, optional use. |
| Tokenizer | It has the role of dividing the character string at the word level. Required item. |
| Token filters | Perform necessary processing (addition, deletion, change) for the contents divided by Tokenizer. Post-processing position, optional use. |
On the official website
There is a description in, so let's check it once.
Finally, aggregations. Aggregate the data based on the search query.
On the official website
Refer to the area.
Let's finally operate Elasticsearch ~!
For general content,
-First Elasticsearch | Qiita -[Elasticsearch] List of frequently used commands | Qiita
Please refer to the area.
From the perspective of comparison with SQL
-Comparison of queries between SQL and Elasticsearch | Qiita -Understanding while comparing Elasticsearch and SQL | Qiita
The area is very easy to understand ♪
First of all, referring to the above articles, on a command basis,
--Creating an index --Data input & update --Query execution
It's a good idea to go through the area.
If you have the spare capacity, it is a good idea to go through analyzer and aggregations.
For analyzer (using Japanese),
-In Elasticsearch, _search the query that has been divided (kuromoji: morphological analysis). | Qiita -Summary of settings for using Elasticsearch in Japanese | Qiita -[Elasticsearch] Try to improve search accuracy with Kuromoji and ngram | Qiita
Please refer to the area.
For aggregations,
-Realization of facets using Elasticsearch Aggregations | Qiita -Replace SQL "Group by" with Elasticsearch "Aggregations" | Qiita
The area will be helpful.
You can also operate using a programming language!
According to Elasticsearch, it supports Java, JavaScript, Go, .NET, PHP, Perl, Python, Ruby and 8 other programming languages, but as I mentioned at the beginning, my main environment is Since it is Java, this time it is Java.
For Japanese articles
Is very well organized.
The top page of the original book is below.
The following page is the one that I personally wanted to keep in mind.
It's a good idea to actually write the code here as well and see how it works.
It's hard to get at first, Elasticsearch Konoyaro! !! However, as I used it, I became able to understand it a lot, and I became more attached to it. The range of customization is large, and it is also good that it seems to be worth raising.
It's been about two weeks since I started touching it as of 09/03/2020 at the time of writing this article. It's still a long way to go, but I'd like to get used to it rather than learn it!
| date | Contents |
|---|---|
| 2020/09/03 | First edition post |
| 2020/09/04 | analyzer,Added content about aggregations |
These articles are not included in the text, but have been very helpful.
-Elasticsearch Mapping | Hello! Elasticsearch. -Summary of Amazon Elasticsearch Service | Qiita
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