How to get data into Neo4j?

This is the second post in the series of From relational databases to databases with relations. Check the link to find all the other entries, too. The source code for everything, including the relational dataset is on GitHub:
The post has also been featured in This Week in Neo4j.

To feel comfortable in a talk, I need to have a domain in front of me I’m familiar with. Neo4j just announced the Graph Gallery which is super nice to explore Graphs, but I wanted to have something of my own and I keep continue using the idea of tracking my musical habits. In the end I want to have knowledge base in addition to my chart applications (both linked above).

There are several ways to get data into your Neo4j database. One is a simple Cypher statement like this

CREATE (artist:Artist {name: 'Queen'}) RETURN artist

which creates a node with the label Artist and a property name for one of my favorite bands of all time. I can use the MERGE statement, which ensures the existence of a whole pattern (note only a node), too. More on that later, though. In any case, that would be a real effort todo manually. Therefor, let’s have a look how to import data. I found several options:

I find it quite fascinating in how many ways CSV data can be processed from within Neo4j itself. It reads through CSV and provides each row in a way that you can interact with from a Cypher statement like it comes from the graph itself. Those CSV files can be put into a dedicated import folder in the database instance but can also be retrieved from a URL. You can come a long way with that import if you already have CSV or a willing to massage your data a bit so that it fits a plain structure. Rik did this with a beer related data, check it out here: Fun with Beer – and Graphs in Part 1: Getting Beer into Neo4j.

JSON is an option if you want to work agains the many nice APIs out there, but my data sits in a relational database. It looks like this:

This tables store all tracks I have listened to, their artists and genres and in a time series table all plays. If you read the previous post closely, you’ll notice that this database, dubbed statsdb is only in NF2. There is no separate relation for the albums of an artist. They are stored with the tracks. I actually forgot why I modeled it that way. I vaguely remember that I was enjoyed that album names are not necessarily unique and I could find a good business primary key. Anyway, the schema is still running and gives me each month something like that which btw takes only three SQL queries:

In my property graph model I wanted to have separate albums nodes, though, so I had to massage and aggregate my data a bit before creating nodes. As I didn’t want to do this based on CSV, I looked at all the options JDBC. First, Neo4j ETL Tool:

Neo4j ETL Tool

The Neo4j ETL Tool can be downloaded through Neo4j Desktop and needs a free activation key. The link guides you through all the steps necessary to connect the tool to both the Graph database as well as the relational database. The nice thing here: You don’t have to install a driver for popular databases like MySQL or PostgreSQL as it comes with our tool.

I ran the tool agains my database and stopped here:

The ETL Tool recognized my tables and also the foreign keys and proposed a node and relationship structure. This is nice, but doesn’t fit exactly what I want. The relationships can easily be renamed and so can the node labels, but I don’t want the structure. I could just ran this and than transform everything via Cypher again, but that feels weird.

What I want is something like this (plus the play counts, for sure):

MATCH path1=(album:Album {name: 'Jazz ist anders'}) - [:RELEASED_BY] -> (:Artist)
MATCH path2=(album) - [:RELEASED_IN] -> (:Year) - [:PART_OF] -> (:Decade)
MATCH (album) - [:CONTAINS] -> (tracks:Track) 
RETURN path1, path2, tracks

Getting all the artists is simple. It’s basically a “select * from artists” and the corresponding Cypher. This is what Apoc does:

Importing data with JDBC into Neo4j

APOC is not only the name of a technician in the famous Movie “The Matrix”, but also an acronym for “Awesome Procedures on Cypher”. Neo4j is extensible via custom, stored procedures and functions much like PostgreSQL and Oracle. I have been a friend of those for years, I even read and generated Microsoft Excel files from within an Oracle Database. Now I realize, that Michael Hunger does the same for Neo4j 🙂

Anyway. APOC offers “Load JDBC” and Michael has a nice introduction at hand.

To use APOC you have to find the plugin folder of your Neo4j installation. Download the APOC release matching your database version from the above GitHub link and add it to the plugin folder. APOC doesn’t distribute the JDBC drivers itself, those have to be installed as well. As my stats db is PostgreSQL, I grabbed the PostgreSQL JDBC Driver and put it into the plugin folder as well.

Things are super easy from there on. With the following Cypher statement, the Neo4j instances connects agains the PostgreSQL instance, executes a select * from artists and returns each row. YIELD is a subclause to CALL and selects all nodes, relationships or properties returned from the procedure for further processing as bound variables in a Cypher query. I then used them to merge all artists. In case they already existed, I updated some auditing attributes. The associations between some artists have already been there, so merge didn’t remove that:

WITH "jdbc:postgresql://localhost:5432/bootiful-music?currentSchema=dev&user=statsdb-dev&password=dev" as url
CALL apoc.load.jdbc(url,"artists") YIELD row
MERGE (artist:Artist {name:})
   ON CREATE SET artist.createdAt = localdatetime()
   ON MATCH SET artist.updatedAt = localdatetime()
RETURN artist

The merge-clause is really fascinating. It matches a pattern and the merge is applied to the full pattern, meaning it must exist as a whole. In the case above, the pattern is simple, it’s just a single node. If you want to create or update an album released by an artist and use something like this MERGE (album:Album {name: $someAlbumName}) - [:RELEASED_BY] -> (artist:Artist {name: $someNewArtistName}), the statement will always create a new artist node, regardless wether the artist already exists or not as the whole pattern is checked. You have to do those things in separate steps like this:

MERGE (artist:Artist {name: $someNewArtistName})
MERGE (album:Album {name: $someAlbumName}) - [:RELEASED_BY] -> (artist)

First, merge the artist. Then, merge the album and the relationship to the existing artist node. For me, coming from with SQL background, this feels unusual at first, much like I can use something in an imperative AND declarative way.

Now, the above example is still not what I want. apoc.load.jdbc basically gives me the whole table and I have to do every post-processing in afterwards. Luckily, that second parameter can also be a full SQL statement. Now, to create a graph structure like above (minus the tracks though), I did something like this. Behold, all the SQL and Cypher in one statement:

WITH "jdbc:postgresql://localhost:5432/bootiful-music?currentSchema=dev&user=statsdb-dev&password=dev" as url,
     "SELECT DISTINCT as artist_name, t.album, as genre_name, t.year
      FROM tracks t JOIN artists a ON = t.artist_id JOIN genres g ON = t.genre_id
      WHERE t.compilation = 'f'" as sql
CALL apoc.load.jdbc(url,sql) YIELD row
MERGE (decade:Decade {value: row.year-row.year%10})
MERGE (year:Year {value: row.year})
MERGE (year) - [:PART_OF] -> (decade)
MERGE (artist:Artist {name: row.artist_name})
   ON CREATE SET artist.createdAt = localdatetime()
   ON MATCH SET artist.updatedAt = localdatetime()	 
MERGE (album:Album {name: row.album}) - [:RELEASED_BY] -> (artist)
   ON CREATE SET album.createdAt = localdatetime()
   ON MATCH SET album.updatedAt = localdatetime()
MERGE (genre:Genre {name: row.genre_name})
   ON CREATE SET genre.createdAt = localdatetime()
   ON MATCH SET genre.updatedAt = localdatetime()
MERGE (album) - [:HAS] -> (genre)
MERGE (album) - [:RELEASED_IN] -> (year)

Read: Bind the JDBC url and a SQL statement to variables. The sql selects a distinct set of all artists and album, including genre and year, thus denormalizing my relational schema by joining the artists and genres. The resulting tuples are then used to create decades and years, merge artists, genres and albums and finally relating them to each other. I was deeply impressed after I ran that.

First: It’s amazing how well Neo4j integrates with other stuff and secondly, it’s actually pretty readable I think.

In my talk, I will dig deeper in the Cypher used. Also, the statements for generating the track data in the above Graph are bit more complex (you find them here). But in the end, I could have stopped here. And probably, I would have done so, if I had started with APOC in the beginning.

But me being the mad scientist I am, started somewhere else:

Writing your own tool

I did this right after my first look at the ETL tool. Neo4j can be extended with procedures and functions. Procedures stream a result with several attributes, functions return single values. When I joined Neo4j, my dear colleague Gerrit impressed me with knowledge about that stuff and I wanted to keep up with his pace, so I saw a good learning opportunity here.

One does not have to extend from a dedicated class or have to implement an interface for writing a Neo4j stored procedure. It’s enough to annotate the methods that should be exposed with either @Procedure or @UserFunction. It’s necessary that org.neo4j:neo4j is a provided dependency on the class path.

Neo4j provides detailed information how to create a package that just can be dropped into Neo4js plugin folder, find the instructions here. They worked as promised.

What I did was to apply one of my favorite libraries again, run jOOQ against my stats database, generating a type safe DSL dedicated to my domain, packaged it up and than used it in a Neo4j stored procedure. In the following listing you’ll find that I use Neo4js @Context to get hold of the graph database service in which the procedure is running, pass in user name, password and JDBC url to my procedure. Those are used to connect against PostgresSQL (try (var connection = DriverManager.getConnection(url, userName, password);) {} (yeah, that’s Java 11 code), open a Neo4j transaction and than just executing my SQL and manually creating nodes.

public class StatsIntegration {
	public GraphDatabaseService db;
	public Log log;
	@Procedure(name = "stats.loadPlayCounts", mode = Mode.WRITE)
	@Description("Loads the play counts for tracks. Note: All the tracks and albums need to be there, use loadAlbumData before.")
	public void loadPlayCounts(
			@Name("userName") final String userName,
			@Name("password") final String password,
			@Name("url") final String url) {
		try (var connection = DriverManager.getConnection(url, userName, password);
			 var neoTransaction = db.beginTx()) {
			var statsDb = DSL.using(connection);
			var isNoCompilation = TRACKS.COMPILATION.eq("f");
			final String columnNameYear = "yearValue";
			final String columnNameMonth = "monthValue";
			final String columnNameNewPlayCount = "newPlayCount";
			var year = extract(PLAYS.PLAYED_ON, DatePart.YEAR).as(columnNameYear);
			var month = extract(PLAYS.PLAYED_ON, DatePart.MONTH).as(columnNameMonth);"Deleting existing playcounts");
			executeQueryAndLogResults("MATCH (:Track) - [playCount:HAS_BEEN_PLAYED_IN] -> () DETACH DELETE playCount", Map.of());"Loading playcounts from statsdb");
							year, month,
					.groupBy(ARTISTS.NAME, TRACKS.NAME, year, month)
					.forEach(r -> {
						var parameters = Map.of(
								"artistName", r.get(ARTISTS.NAME),
								"trackName", r.get(TRACKS.NAME),
								columnNameYear, r.get(columnNameYear),
								columnNameMonth, r.get(columnNameMonth),
								columnNameNewPlayCount, r.get(columnNameNewPlayCount)
						executeQueryAndLogResults(CREATE_PLAY_COUNTS, parameters);
					});"Adding weight to artists");
			executeQueryAndLogResults(WEIGHT_ARTISTS_BY_PLAYCOUNT, Map.of());
		} catch (Exception e) {

The complete sources for that exercise are in the etl module of my project. As interesting as the stored procedure itself is the integration test, that needs both a Neo4j instance and PostgreSQL. For the first I use our own test harness for the later one, Testcontainers.


As much as the last exercise was fun and I learned a ton about extending Neo, working directly with the engine and so on, it’s not only slower (probably due to context switches and the relatively large transaction), but it’s hard to read, by magnitudes. As with a relational database, declarative languages like Cypher and SQL beat explicit programming hand down. If you ever have a need to migrate or aggregate and duplicate data from a relational database into Neo4j, have a look at apoc.load.jdbc. Select the stuff you need in one single context switch and then process as needed, either in a single transaction or also in batches and parallel.

For my music project, I’ll probably keep the ETL Tool around as I like the structure I created for the tool itself, but will rewrite the process from relational to graph based on APOC and plain SQL and Cypher statements, if it ever sees production of any kind.

Overall verdict: It took me a while to find a graph model I like and from which I am convinced I can actually draw some conclusions about the music I like, but after the graph started to resonate with me, it felt very natural and enjoyable.

Next step: Creating an Neo4j-OGM model for the different nodes.

Images in this post: Myself. Featured image – Markus Spiske.

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From relational databases to databases with relations

In the summer of 2018, I joined Neo4j. This seems odd at first, my “love” for relational databases and SQL is known. I didn’t have this slide in my jOOQ presentation for two years now without reason. But: Looking at the jOOQ and SQL talks from the perspective of early 2000s, they also seemed odd at first. Back then, I never thought doing that much with databases. That changed a lot.

My experience is that all the data your deal with usually has a much longer lifetime than any of your applications sitting on top of that. Knowing one or more database management systems is essential. Being able to query them even more: What Neo4j and relational databases have in common: A great, declarative way to tell them which data to return and not how to return them. In the case of a relational database, this is obviously SQL, which had quite the renaissance for a few years now. Neo4j’s lingua franca is Cypher.

I get to play with Cypher a lot, but in the end this is not what I am working on at Neo4j. My work is focused on our Object Graph Mapper, Neo4j-OGM and the related Spring Data Module, Spring Data Neo4j. We have written about that a bit on medium. Given my experience with Spring, Spring Data and Spring Boot, the role suddenly makes much more sense.

People who entered the IT-conference circus may know the merry-go-round (or should I say “trap”?) of “talks stress me out a lot” – “hey, this is great, just enter another CfP”. I fell for it again and proposed a talk with the above title to several conferences. Now, I have to come up with something.

Back in 2016, when I first held my talk Database centric applications with Spring Boot and jOOQ, I started to write my story down. Back then, it helped me a lot, so here we go again. Join me on my way from relational databases to databases with relations.


What are we talking about here?

The domain will be music. I have been tracking my musical habits for more than 10 years now at my side project Daily Fratze and I enjoy looking back to what I listened like this months but 5 years ago.

Edgar F. Codd

The guy on the left, Edgar F. Codd invented the relational model back in the 1970s. A relation in this model doesn’t describe a relation like the one between two people or a musician associated with a band who released a new track. A relation in the relational model is a table itself. Foreign keys between tables ensure referential integrity, but cannot define relations themselves. I put down some thoughts a while back in this German deck. This is what a relation looks like in a relational database:

One kind of sport to do with relational databases is the process of normalizing data. There are several normal forms. Their goal is to keep a database redundancy free, for several reasons. Back in the 1970, disk space being one of them. First normal form (1NF): All attributes should be atomic. 2NF is 1NF plus no functional dependencies on parts of any candidate keys. That is: There must not be a pair of attributes appearing twice in a relation’s tuples. 3NF forbids transitive dependencies (“Nothing but the key, so help me Codd”) and it gets complicated from there on. I have to say though, that normalization up to 3NF is still relevant today in a relational systems, at least if you’re a friend of (strong) consistent data.

However, as my colleague Rik van Bruggen points out in his book “Learning Neo4j”, relational databases are quite anti-relational.

Why is this? Relations in NF can be queried in many, many ways. Each query send via SQL returns a new relation, what’s the problem? It depends. In a strictly analytical use case, there’s often not a problem. Recreating object hierarchies however, joining things back together, is. A handful of joins is not hard to understand, even without a tool, but self referential joins or a sheer, huge amount, is. It also gets increasingly hard on the database management system.

This is where graphs can come into play. Graphs are another mathematical concept, this time from Graph theory. Sometimes
people call a chart graph by coincident, but this is wrong. A graph is a set of objects with pairs of objects being related. In mathematical terms, those objects are vertices and the relations between them, edges. We call them nodes and relationships in the Neo4j database.

Neo4j is a Property Graph. A property graph adds labels and properties to both nodes and relationships:

The above picture is from our excellent post What is a Graph Database?.

One takeaway from that post is: Neo4j is referred to as a native graph database because it efficiently implements the property graph model down to the storage level. Or in technical terms: Neo4j employs so called index-free adjacency, which is the most efficient means of processing data in a graph because connected nodes physically point to each other in the database. This obliterates the needs for complex joins, either directly or via intersection tables. One just can tell the database to retrieve all nodes connected to another node.

This is not only super nice for simple aggregations of things, but especially for many graph algorithms.

So what has this todo with my talk? My SQL talk was all about doing analytics. That is, retrieving data like in this image from a relational database with build-in analytic functions. Computing running totals, differences from previous windows and so on (read more here).

First of all I’m gonna analyze how to create a graph structure from the very same dataset I used in the SQL talk. There are different tools out there with different approaches. I’ve chosen a technique that resonated for various reasons with me. As I want to enrich the existing dataset, I’ll model a domain around it, with Java, putting Neo4j-OGM to use. I’ll show how Spring Data Neo4j helps me not having to deal with a lot of cruft. In the end, I’ll show that I can build my own music recommendation engine based on 10 years of tracking my musical habits by applying some of the queries and algorithms possible with Neo4j.

Next step: Loading data.

Pictures in this post: Codd and Table – Wikipedia, Property Graph – Neo4j, Featured image – Sarah Cervantes.

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Validate nested Transaction settings with Spring and Spring Boot

The Spring Framework has had an outstanding, declarative Transaction management for years now.
The configurable options maybe overwhelming at first, but important to accommodate many different scenarios.

Three of them stick out: propagation, isolation and to some lesser extend, read-only mode (more on that a bit later)

  • propagation describes what happens if a transaction is to be opened inside the scope of an already existing transaction
  • isolation determines among other whether one transaction can see uncommitted writes from another
  • read-only can be used as a hint when user code only executes reads

I wrote “to some lesser extend” regarding read-only as read-only transactions can be a useful optimization in some cases, such as when you use Hibernate. Some underlying implementations treat them as hints only and don’t actually prevent writes. For a full description of things, have a look at the reference documentation on transaction strategies.

Note: A great discussion on how setting read-only to true can affect performance in a positive way with Spring 5.1 and Hibernate 5.3 can be find in the Spring Ticket SPR-16956.

Some of the transactions settings are contradicting in case of nested transaction scenarios. The documentation says:

By default, a participating transaction joins the characteristics of the outer scope, silently ignoring the local isolation level, timeout value, or read-only flag (if any).

This service here is broken in my perception. It explicitly declare a read-only transaction and than calls a save on a Spring Data repository:

import org.springframework.transaction.annotation.Transactional;
import org.springframework.stereotype.Service;
public class BrokenService {
	private final ThingRepository thingRepository;
	public BrokenService(ThingRepository thingRepository) {
		this.thingRepository = thingRepository;
	@Transactional(readOnly = true)
	public ThingEntity tryToWriteInReadOnlyTx() {
		return ThingEntity("A thing"));

This can be detected by using a PlatformTransactionManager that supports validation of existing transactions. The JpaTransactionManager does this as well as Neo4js Neo4jTransactionManager (both extending AbstractPlatformTransactionManager).

To enable validation for JPA’s transaction manager in a Spring Boot based scenario, just make use of the provided PlatformTransactionManagerCustomizer interface. Spring Boots autoconfiguration calls them with the corresponding transaction manager:

import org.springframework.boot.autoconfigure.transaction.PlatformTransactionManagerCustomizer;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
class TransactionManagerConfiguration {
	public PlatformTransactionManagerCustomizer<AbstractPlatformTransactionManager> transactionManagementConfigurer() {
		return (AbstractPlatformTransactionManager transactionManager) -> transactionManager

In the unlikely scenario you’re not using Spring Boot, you can always let Spring inject an EntityManagerFactory or for example Neo4j’s SessionFactory and create and configure the corresponding transaction manager yourself. My Neo4j tip does cover that as well.

If you try to execute the above service now, it’ll fail with an IllegalTransactionStateException indicating “save is not marked as read-only but existing transaction is”.

The question if a validation is possible arose in a discussion with customers. Funny enough, even working with Spring now for nearly 10 years, I never thought of that and always assumed it would validate those automatically but never tested it. Good to have learned something new, again.

Featured image on this post: Transaction by Nick Youngson CC BY-SA 3.0 Alpha Stock Images.

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Donating to Médecins Sans Frontières (Ärzte ohne Grenzen)

Some weeks ago, my friends Judith and Christian, who write great Steampunk, Fantasy and in the recent time science fiction books for whom I wrote this little Kotlin app had a good idea:

And I was like:

My book Spring Boot 2 has been a bestseller this year and I still got quite a decent revenue from arc42 by example that I co-authored with Gernot Starke and Stefan Zörner.

Keeping my promises, here are the numbers: My share of royalties of the arc42 by example book has been 112,15€ from June to August. Gernot, Stefan and I split revenues, so that is only my share. Gernot himself already donates through Leanpubs causes. I don’t have numbers from my publisher for Spring Boot Book. I therefore decided to round this number to 250€:

Me and my family have been incredibly lucky the last years and I’ more than happy that one can give. We live in Germany and despite some idiots on various (social) media, it’s really fortunate to live here. Health care is working, social system as well. We don’t have war but clean water, food and everything. We should not forget that this is by far not self-evident for many, many people on this planet-

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On becoming a Java Champion

July 2018 marks a personal highlight of mine. Just a bit after Rabea brought the news to our very own EuregJUG, the Java Champions account send this tweet out:

My name along this Java illuminaries. When I started this blog here more than twelve years ago, that was something I never even dreamed of. In 2006 the Java Champions program already existed but me, just been from university and vocal training for about 4 years or so, had no clue at all.

While getting my feet wet, I took inspiration from many of the people in the program, from their code, blog posts and talks. Not knowing that I would be working with them later in my life, even being direct colleagues with one of the founders like Eberhard Wolff. Even better: I’m lucky enough to call some of them my friends.

Java Champion was a long shot. I’m hopefully not the worst software engineer and architect out there, but I’m very far from knowing all the things. Quite the contrary. Did I know what a Java bridge method is until Gunnar brought this up on twitter? Do I know much about theoretical computer science? Hell no. I’m very sure that many people who I find very inspiring could forget more than I ever knew and still know more than I do.

So I must have done something else right and I’m happy with that. I just want to write some points down that might help others on their way:

On growth

Somewhen back in autumn 2014 I got Prokura in my company. I’m still not aware of an English word for that, but it means something along the lines that I can make and execute business decisions. My Prokura was only restricted in the way that I could not have closed the company.

Looking back, it was my bosses saying “we trust you to run this thing and also, this is our way of saying here, you’re explicitly technical lead, too.” Sadly, it didn’t come with a manual. At this point I was already more than 12 years at ENERKO INFORMATIK. I had grown in this time, but mostly on many technical levels. Things I learned include SQL, PL/SQL, XML, XSLT, Java (obviously), Spring (more obvious), we did Groovy at some point, not speaking of all the Swing based stuff I wrote and AFAIK ENERKO still runs an Oracle Database Dictionary based ORM I invented.

But could I lead a team? That was hard for me for several reasons. The company didn’t have had much fluctuation (and still hasn’t), so one did basically “grew up” with one another and it’s a weird situation if one person suddenly changes, either internally or externally.

Back then, I somewhen added the title line of a Nick Cave song to my personal site: “You’ve got to just, Keep on pushing, Keep on pushing, Push the sky away.” That has been my motto for quite some time.

I needed to grow beyond technical, “hard skills”. I tried and surprisingly, the feedback I got after leaving ENERKO INFORMATIK in 2017 was better than the impression I had of myself. I managed to hire two new engineers and they are still with the company which makes me quite happy.

In the end, I still felt I didn’t manage to achieve to anything. It’s weird how self perception and perception from others diverge. I left the company and I am working now at Neo4j, where I work in a small team with Gerrit Meier on Neo4j OGM and Spring Data and I couldn’t be happier with it.

Things come with a price

Most of the things “Spring” I learned on working with and on Daily Fratze, a personal photo site I’m running since 2005. I really love that stuff and I learned so much by developing it. But: It ruined my sleep in the days between 2010 and 2013 (2013 was the year my 2nd kid was born), it made parts of my family time with the my 1st kid very hard. I wanted to “finish” stuff and basically didn’t do anything else in my spare time.

Partially the same happened during late 2014 and early 2015, coincident with the events described unter “Growth” when I developed and a lot more of Spring Boot related stuff inside my company. I was at home but I wasn’t there. I came back from the office, ate, and went into my office.

At some point, I needed to step back and also visited a doctor to help me to cope with sleep issues and dark moods.

We have a good family life most of the time, though. My wife always kept my back and I tried to be awake early every morning to be there for my kids. It’s by far not self evident that someone tolerates a partner that uses every minute awake when the kids are in their beds for coding, reading technical stuff and so on.

I gave a lot of talks since the end of 2015 with some success. The talks I enjoyed the most have been on Spring Boot and database related stuff. All the things I can explain in the middle of the night. However: I was and I still am super nervous at least a week before a talk. That feeling doesn’t seem to go away. Doing those talks is much more work for me than writing things (for example a book as explained in this post).

On mentoring

I was astonished that during the last 3 years or so many people came to me and thanked me for inspiration. That’s one of the reasons I’m trying to write this down here. Something like being a Java Champion or also success in general is most often not something that happens in a void, without help and support.

Everything I did and keep on doing: I would not be able to do this without having support in my life, a growing sense of what is good for my health (both mind and body) and without having had good mentors in my life.

There was my boss Rainer Barluschke at ENERKO INFORMATIK who taught me that there’s so much more than technical problems to solve. That it’s worth going a detour if the end result fits. Who even introduced me to some topics that seems to be more of the esoteric kind back than, like spiritual growth.

My ex-colleague Silke Böhler, who challenged me in JavaLand 2015 with some good food for thoughts and later on with a line “work is fun, but has to be taken seriously anyway…”. Apart from that: It has been the little things that last and helped along the way.

I already mentioned the support of my family, but also a kid can be a mentor. It’s hard to describe, but having someone near me that most of the time is in a good mood compared to myself, helps on focussing and accentuating the good things.

Summing things up…

Don’t give up trying to reach your goals because other peoples success seems to be so easily achieved. In the end, people of a group engage in the same game, but start with different preconditions. Look for opportunities where you are

  • Allowed to learn
  • Be able to fulfill a meaningful task, with all given due diligence and seriousness
  • Be part of a team, IT is not a single players sport

And also

  • Find a good mentor
  • Become a mentor: Pass on what you learned
  • Keep interest in other things outside your job… Not everything is related to IT

Right now, I feel at peace with myself for the first time in about 5 years. Going over the midlife crises? Who knows… I’m thankful that I actually could push my sky away by magnitudes and the last year will be a year I will always remember.

For the near future I’m super thrilled to work on cool stuff with Neo4j and Spring Data, with having the latest release of Neo4j OGM 3.1.1 just out of the door and see what happens next.

I made some innuendo to some people (Ralf 😉) that I do have ideas for a next book and as a spoiler: If put this together, I’ll try to bring this post here, something along that one and some other ideas into a form that might be worth reading for more people.

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